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Review

miRNAs as Biomarkers in Disease: Latest Findings Regarding Their Role in Diagnosis and Prognosis

by
Carmen Elena Condrat
1,†,
Dana Claudia Thompson
1,†,
Madalina Gabriela Barbu
1,†,
Oana Larisa Bugnar
1,
Andreea Boboc
1,
Dragos Cretoiu
1,2,
Nicolae Suciu
1,3,4,
Sanda Maria Cretoiu
2,* and
Silviu Cristian Voinea
5
1
Alessandrescu-Rusescu National Institute for Mother and Child Health, Fetal Medicine Excellence Research Center, 020395 Bucharest, Romania
2
Department of Cell and Molecular Biology and Histology, Carol Davila University of Medicine and Pharmacy, 8 Eroii Sanitari Blvd., 050474 Bucharest, Romania
3
Division of Obstetrics, Gynecology and Neonatology, Carol Davila University of Medicine and Pharmacy, 8 Eroii Sanitari Blvd., 050474 Bucharest, Romania
4
Department of Obstetrics and Gynecology, Polizu Clinical Hospital, Alessandrescu-Rusescu National Institute for Mother and Child Health, 020395 Bucharest, Romania
5
Department of Surgical Oncology, Prof. Dr. Alexandru Trestioreanu Oncology Institute, Carol Davila University of Medicine and Pharmacy, 252 Fundeni Rd., 022328 Bucharest, Romania
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Cells 2020, 9(2), 276; https://doi.org/10.3390/cells9020276
Submission received: 22 November 2019 / Revised: 14 January 2020 / Accepted: 21 January 2020 / Published: 23 January 2020
(This article belongs to the Special Issue microRNA as Biomarker)

Abstract

:
MicroRNAs (miRNAs) represent a class of small, non-coding RNAs with the main roles of regulating mRNA through its degradation and adjusting protein levels. In recent years, extraordinary progress has been made in terms of identifying the origin and exact functions of miRNA, focusing on their potential use in both the research and the clinical field. This review aims at improving the current understanding of these molecules and their applicability in the medical field. A thorough analysis of the literature consulting resources available in online databases such as NCBI, PubMed, Medline, ScienceDirect, and UpToDate was performed. There is promising evidence that in spite of the lack of standardized protocols regarding the use of miRNAs in current clinical practice, they constitute a reliable tool for future use. These molecules meet most of the required criteria for being an ideal biomarker, such as accessibility, high specificity, and sensitivity. Despite present limitations, miRNAs as biomarkers for various conditions remain an impressive research field. As current techniques evolve, we anticipate that miRNAs will become a routine approach in the development of personalized patient profiles, thus permitting more specific therapeutic interventions.

1. Introduction

microRNAs (miRNAs) represent a class of small, non-coding RNAs comprising of 17–25 nucleotides [1], whose main role is to regulate mRNA by leading to its degradation and also to adjust the protein levels [1,2,3,4]. Their discovery was first published in 1993 and they were described as “mediators of temporal pattern formation” in Caenorhabditis elegans [5,6,7,8,9]. Previous studies have shown that miRNA encoding sequences form up to 1% of the human genome [10].
Biogenesis of miRNA begins in the nucleus, where the transcription of its precursor, primary miRNA or pri-miRNA takes place under the influence of RNA polymerases II and III [11,12]. The resulting molecule is a hairpin-like structure, which contains a loop at one end [11]. This primordial mi-RNA precursor that is usually made up of hundreds of nucleotides is then processed consecutively by two RNase III enzymes [13,14,15]. The first enzyme to act upon the pri-miRNA, which still resides in the nucleus, is called Drosha or DCGR8, and turns it into a new hairpin-like structure of approximately 70 nucleotides, the Precursor-miRNA or pre-miRNA. The latter is then transported to the cytoplasm, with the help of Exportin-5, where it is cleaved again by the Ago2/Dicer complex leading to the short, mature miRNA double strands [16].
Further on, one of the strands, usually known as the guide strand, will be integrated into the RNA-induced silencing complex (RISC), while the other one, known as the passenger strand, is going to be degraded, even though in some occasions it has been found to be also functional [17]. In most cases, the strand that contains the less stable 5′ end or a uracil at the beginning is more likely to be selected as the guide strand [18,19,20]. In those situations, where the passenger strand is not degraded and both get incorporated into the miRISC complex, the mature miRNA in the guide strand will be the dominant one [21,22].
The main role of miRNA in the human body is gene regulation [23] by mediating the degradation of mRNA and also by regulating transcription and translation through canonical and non-canonical mechanisms [4]. The canonical mechanism means that the miRISC complex containing the miRNA guide strand is exerting its action by binding to the target mRNA through its 3′-untranslated region (3′-UTR) [3]. This process happens in accordance with the seed sequence of the miRNA, the first 2-7 nucleotides from the 5′ end, and it is followed by mRNA deadenylation, translation suppression and finally, degradation [24,25,26]. However, in human cells, about 60% of the interactions between the miRISC complex and mRNA are non-canonical [27], which means that their chains are not always entirely complementary [28]. This leads to the idea that a single miRNA could potentially target numerous mRNAs, while at the same time, one mRNA could contain multiple binding sites for miRNAs, turning this into a possibility that vast number of biological processes could be regulated by this interaction [3].
Another important role played by miRNA is intercellular signaling. Even though most of the miRNAs are found inside the cell, there is a big proportion that migrates outside it and can be found in bodily fluids [29,30,31,32,33]. These are the so-called circulating miRNAs and they are discharged in blood, urine, saliva, seminal fluid, breast milk [30,34] and other fluids through tissue damage, apoptosis, and necrosis [4], or through active passage, in microvesicles, exosomes, or through bonding to a protein [35,36]. The question has also been raised regarding the existence of exogenous miRNA in the blood of healthy subjects [37,38], its origin being assigned to bacteria, food and fungi primarily from the gut [3]. The possible pathological effects of these exogenous miRNA are also taken into consideration.
Previous studies have shown that about 10% of the circulating miRNAs are secreted in exosomes, while the other 90% form complexes with proteins like argonaute 2 (Ago2), nucleophosmin 1 (NPM 1) and high density lipoprotein (HDL) [36,39,40]. This kind of packaging is essential in order to prevent the digestion of miRNA, by the RNases known to be found in the bodily fluids [41].
Having a diameter of 50–100 nm, exosomes are a form endosome-derived microvesicles fusing with the membrane, which contains, amongst others, miRNA. They are secreted by numerous cells, both in vitro and in vivo and can be encountered in most of the fluids in the body [30,34]. Several previous studies have demonstrated in vitro the assimilation of exosomes by neighboring cells, thus leading to the idea of a “message in a bottle” type intercellular communication [42,43,44]. Furthermore, another hypothesis supported by recent findings states that miRNAs could be selectively designated to certain exosomes because of the fact that the miRNA variety found in these vesicles tends to be different from that of their cell of origin [42,45,46,47].
As mentioned earlier, the transport with the help of a protein of circulating miRNAs is estimated at around 90% of the total. Some papers suggest that this type of transport could be ATP-dependent [36], while others indicate that in order for the complex to reach the extracellular space, it requires a transporter, similar to the well-known protein-specific mechanism [31]. Both these hypotheses need more research before being proven correct.
In recent years, extraordinary progress has been made in terms of finding the origin and functions of miRNA and their potential use in research and clinical practice, for both healthy and diseased patients. For example, some studies have shown that the levels of certain circulating miRNAs, involved in inflammation, angiogenesis, and cardiac muscle contractility, are directly influenced by the intensity and length of exercising, thus indicating that they might play a role in mediating the physiological cardiac adaptation to exercising [48,49,50]. Another study has shown the key role miRNAs are playing in the creation of induced pluripotent stem cells (iPS). In this case, miRNAs are repressed by reprogramming factors, turning average fibroblasts into iPS cells [51].
However, probably the most promising role of miRNAs is that of a potential biomarker. Numerous authors have investigated this opportunity in various medical fields. Evidence suggests that they could play an essential role as biomarkers in cancer through exosome-mediated intercellular communication [1,4,52,53,54], in neurology for the diagnosis and prognosis of Alzheimer’s disease [55], for patients with spinal cord injury [56], epilepsy [57] or neurodegenerative ailments [58]. It could also be used in other fields like cardiology, as a faster and more accurate means of diagnosis for acute cardiovascular disease or heart failure [59,60,61] and in the case of infectious diseases for the diagnosis of sepsis [62]. Although current literature on miRNAs does not seem to lack in writings emphasizing the numerous roles of these molecules both in general [63,64,65] and in specific areas such as oncology [66,67,68], cardiovascular diseases [69,70], sepsis [71], our aim is to offer an overview on the current state of the knowledge while occasionally mentioning areas of controversy.

2. The Potential of miRNAs as Biomarkers

Biomarker is a term that defines different types of objective indicators of health or disease. Throughout history, and according to human technological advancements, these indicators have turned increasingly more precise and reliable. Some of the first biomarkers were discovered in ancient times and were represented by the medical signs, such as pulse, the looks and even the taste of urine.
Nowadays, the medical community refers to biomarkers as being certain molecules, usually proteins, detected in the various fluids of the human body, through specific means in medical laboratories. The best known protein biomarkers measurable in the blood are troponin for the diagnosis of myocardial infarction, carcinoembryonic antigen (CAE) for different types of cancer, aminotransferases ALT and AST for liver diseases and the prostate-specific antigen (PSA) for the diagnosis and prognosis of prostate cancer [2]. Recently, however, detecting new and improved protein biomarkers has turned out to be a time consuming and expensive operation, due to the low amount of clinically significant proteins, the complexity of their structure and the struggle in finding accurate detection methods.
In order to attain the desiderates of personalized medicine, new and more accurate biomarkers need to be discovered. An ideal biomarker needs to fit certain criteria. First of all, it needs to be easily accessible, which means that it needs to be discovered and measured through minimally invasive procedures. Another important criterion is the specificity to the investigated pathology, followed by sensitivity (its presence should be detected preferably before the clinical symptoms have appeared and should vary according to the disease progression or response to treatment). Last, but not least, it should be translatable from research to clinic [72].
Researchers have discovered the existence of free nucleic acids in the blood for about 60 years [73,74,75,76,77], while DNA and RNA from tumors are frequently encountered in the plasma of cancer patients [78,79,80,81]. It was considered for a while that RNA molecules could not be used as biomarkers from blood samples, because of the elevated levels of nucleases found in plasma [82], but the idea was dismissed once it was discovered that miRNAs were stable in samples of fixed tissues [83].
miRNAs have first been established as biomarkers for cancer in 2008, when Lawrie et al. utilized them for the examination of diffuse large B-cell lymphoma in the serum of patients [84,85], and ever since, their potential use as biomarkers has been mentioned in literature for numerous diseases.
This novel class of molecules possesses an array of advantages that could turn them into ideal candidates for biomarkers in a variety of afflictions. As mentioned before, the ideal biomarker needs to be easily accessible, a condition that applies to miRNAs that can easily be extracted through liquid biopsies from blood, urine and other bodily fluids. It also has a high specificity for the tissue or cell type of provenance and it is sensitive in the way that it varies according to the disease progression, being used in several studies for the differentiation of the cancer stages [86] and even for the measurement of the therapy responsiveness [87]. Moreover, the technologies for the detection of nucleic acids already exist and the development of new assays requires less time and lower costs in comparison to producing new antibodies for protein biomarkers.
Another advantage of miRNAs lies in their potential for being used as multimarker models for accurate diagnosis, guided treatment and evaluating responsiveness to treatment. While running many protein markers may be both expensive and time-consuming, using multimarker panels composed of numerous miRNAs may provide a non-invasive method for diagnosis and prediction of disease progression. For instance, identifying the urinary miRNA signature of lupus nephritis has promoted the early detection of renal fibrosis [88]. This is especially important in cancer, a thoroughly heterogenous disease, where a multimarker approach would be preferable. To this extent, a nine-miRNA multimarker panel for breast carcinoma has already been shown to significantly improve the reliability of breast cancer diagnosis [89].
However, the research of miRNAs as biomarkers is still in its early stages, therefore at the moment, the findings generally lack reproducibility. There are several discordances reported between different teams that have analyzed the same tumors [90]. In order to resolve this issue, standardized protocols must be developed both for the initial stages of the process, like sample collection, transport, and storage, as well as data analyzing for the diversity of technological methods used.

3. miRNA Identification Techniques

In order to aid the diagnostic process and optimize treatment plans, research has delved into molecular testing, aiming to develop an efficient and cost-effective method for detecting miRNAs involved in some of the most common diseases worldwide, while also trying to identify new such molecules associated with these pathologies. Nevertheless, this quest has proven to be rather challenging, mainly due to the fact that the field of miRNAs itself is relatively new, and therefore traits such as detection limits, range of concentrations in body fluids, modulation depending on various parameters (age, gender, health/disease) are not yet clearly established [91].
The gold-standard for miRNA quantification is quantitative reverse transcriptase PCR (RT-qPCR) [91,92,93]. Stem-loop reverse transcription (RT)-based TaqMan microRNA assay is the main PCR technique used in research, with the advantage of having high sensitivity and specificity rates [94]. This is a two-step method, the first step requiring the binding of miRNA molecules by primers at the 3′ end in order to proceed to stem-loop reverse transcription [95]. For the second step of the technique, real-time PCR is used to quantify the miRNAs targeted [95]. Other available options are direct RT-based and poly (A) tailing-based SYBR miRNA assays [94]. The downside to these techniques is that sensing errors for the samples used can sometimes happen and also, during the amplification steps, there is high risk of contamination [91].
Northern blot hybridization represents another widely used alternative for quantitative assessments of miRNAs. This method involves the separation of the total amount of RNA on polyacrylamide gel that possesses the property of denaturation, followed by its transfer on a nylon membrane. After that, the RNA undergoes a process of UV cross-linking and, lastly, it is hybridized using a radioactive substance [96,97,98,99]. However, this technique tends to be strenuous, it necessitates large quantities of RNA and it has sometimes been reported to omit rare types of miRNA [99]. Under these circumstances, efforts have been made in order to improve the method, therefore leading to the possibility of using lower amounts of RNA and also shortening the execution time of the technique [100,101,102].
Two other methods are also in use for the same purpose of identifying the miRNAs. In situ hybridization or ISH is a technique that utilizes radioactive, fluorescent or dioxygenin probes to bind the desired RNA, therefore comparing the expression of miRNAs in various cells [103]. The disadvantages of ISH, however, are still significant and include laborious steps and long processes, with a predisposition towards errors [103]. The second method is next-generation sequencing or NGS. Despite the fact that this is a highly accurate technique that has the ability to detect single miRNAs with the precision of one nucleotide, its high costs lead to a limitation of this technique’s wider accessibility [99].

4. miRNAs in Cancer

Cancer is the most important cause of mortality worldwide. Even though detection in its early stages could lead to a better outcome for the survival of the patient, unfortunately, late detection still remains a major concern nowadays, leading to poor prognosis and a high mortality rate [104]. As a result, and in order to overcome this problem in the age of personalized medicine, new and more sensitive diagnosis methods, such as biomarkers, need to be developed.
Numerous reports have published throughout the years, hundreds of cases of deregulated miRNAs found in the plasma and serum of cancer patients, in comparison to healthy subjects [105] and a lot more studies have nominated circulating miRNAs as potential biomarkers for the diagnosis and prognosis of cancer [105,106,107,108]. The role that circulating miRNAs are playing in cancer is controlling the oncogenes (which is achieved through tumor suppressor miRNAs) and the tumor suppressors (through oncomiRs) [109]. Usually, the oncomiRs tend to be excessively represented (for example miR-17-92 or miR-21 clusters) [110,111], while the miRNAs with tumor suppression function (let-7 cluster), are expressed insufficiently [112]. These discoveries have been made experimentally, by inhibition or inducement of their function.
There are, however, miRNAs that can act both as suppressors or oncomiRs. For example, miR-155 was first considered an oncomiR for lymphoma and pancreatic cancer [113,114], but other evidence suggests that in the cases of ovarian and gastric cancers and melanoma, its expression is inhibited, therefore acting like a tumor suppressor [115,116,117].
There are various causes of the abnormal expression of circulating miRNAs in cancer. Around 50% of the miRNA coding genes are located in areas of the genome that are associated with cancer, which are translocated or amplified in malignancies [118]. Another reason is represented by the function variation of the enzymes involved in the biosynthesis of miRNA, like Drosha and Dicer 1 [119]. A decrease in the levels of these enzymes has been reported in the case of bladder [120] and ovarian cancers [121], while elevated levels are encountered in gastric [122] and cervical squamous cell neoplasms [123]. Lastly, the alteration of circulating miRNAs in cancer could also be caused by transcriptional errors of pri-miRNA [119].
Further on, this paper will describe several types of cancer and how their miRNA profiles could potentially lead to medical advancements in terms of an early diagnosis and a better profiling of these malignancies. A brief summary of some of the most important findings analyzed in this paper is provided in Table 1.

4.1. miRNAs in Breast Cancer

Breast cancer is the most frequent neoplasm affecting women and a major cause of mortality worldwide [146]. Depending on the existence and/or absence of various hormone receptors, such as estrogen receptor (ER), progesterone receptor (PR) and the receptor for the human epidermal growth factor (HER2), the disease can be classified in subtypes, the most aggressive one being triple-negative breast cancer (TNBC) [147]. Lately, efforts have been made in order to find miRNA biomarkers that could improve the diagnostic process, as well as the treatment. As it happens in all types of malignancies studied, miRNAs can have either tumorigenic or suppressing effects on the breast cancer cells [148]. Knowing which miRNAs are involved in breast malignancy and what role they play would not only help in diagnosing the disease and correctly stratifying the patients into risk groups, but it could also aid in the development of new therapeutic agents.
One of the most researched miRNAs in breast cancer is miR-10b, which in early studies was believed to be downregulated in malignant tissues compared to normal samples [124]. However, later research proved that it is in fact overexpressed, but only in metastatic disease [125]. It has been shown that its expression in non-metastatic disease induces cell invasion and metastasis, probably by increasing the expression of RhoC (Ras homolog gene family, member C), a known oncogene [125]. Furthermore, the quantity of miR-10b detected, positively correlated with the disease progression [125]. High levels of miR-10b were also found in ER-negative breast cancer, indicating a poor disease outcome [126].
A recent study offered the possibility of a new miRNA prognostic biomarker, namely miRNA-196a which was already known to be involved in numerous malignancies, among them being breast cancer [127,128]. Suggestive for its supposed oncogenic role, it has been shown that the polymorphism of a single particular nucleotide within its encoding gene (MIR196A2) leads to a decreased expression of miR-196a and a lower risk of breast cancer [129]. miR-196a was found to alter angiogenesis and apoptosis, two of the mechanisms involved in the formation of malignant tumors [130]. High levels of miR-196a were detected in drug-resistant ER-positive breast cancer and seemed to be a valid prognostic instrument for advanced and post-menopausal disease with estrogen receptors [127].
Another disease biomarker is miRNA-4417 which was lately found to be a solid prognostic feature for triple-negative breast cancer (TNBC) [52]. TNBC has been defined by the absence of hormone receptors, thus making it unresponsive to current ER and HER2 targeting therapies [131]. As a result, the development of new potential therapies would have a great impact on the survival rate and could lead to a reduction in toxicity levels related to conventional chemotherapy. Located on chromosome 1p36, miR-4417 was found to be downregulated in TNBC, simultaneously with the evolution of cells to a malignant stage, a fact associated with poor prognosis for the evolution of the disease [52]. Furthermore, when overexpressed in vitro, it was observed that miR-4417 inhibited the migration and the tumorigenic roles of TNBC cells, raising the possibility of it being one of the future therapy agents that could be used in this type of cancer [52].
The association between miRNAs and drug-resistance was extensively researched. A recent study compared a number of 411 miRNAs expressed in 36 breast cancer cell lines in relation to 34 drugs, among which were Docetaxel, Paclitaxel, Tivantinib and Veliparib [149]. They found that miR-187-5p and miR-106a-3p were indicative of drug resistance to both Docetaxel and Paclitaxel and that miR-556-5p associated with sensitivity only to the latter [149]. Various other miRNAs were linked to drug-sensitivity, such as let-7d-5p and miR-18a-5p to Tivantinib, let-7a-5p to Bortezonib, miR-135a-3p to JNJ-707 and miR-185-3p to Panabinostat [149]. Other miRNAs found in relation to drug-resistance were miR-637 to Tivantinib, miR-182-5p to Valiparib and miR-629-5p to Tipifarnib [149]. Therefore, miRNAs could be used in the future as markers for intrinsic drug resistance/sensitivity leading to a better understanding of the therapy needed for each particular case and as a result reducing overtreatment and therapy costs.

4.2. miRNAs in Ovarian Cancer

Ovarian cancer is one of the major malignancies in the gynecologic pathology, having high mortality rates worldwide, in part due to the fact that it has no specific clinical manifestation in the early stages, leading to late diagnosis and tumor metastasis [150,151,152]. In addition, it has increased recurrence and drug resistance rates, further affecting the 5-year survival, which was measured at 46.2% [153]. In consequence, finding new miRNA biomarkers that could be used for screening and early diagnosis of this disease might improve the outcome of the disease.
With this purpose, a case-control study on a small number of ovarian cancer patients was carried out, revealing 147 miRNAs with altered expression compared to case studies [154]. They detected an increased expression of miR-30c1 and low expression levels of miR-342-3p, miR-181a and miR-450b-5p in the blood samples of ovarian cancer patients compared to controls [154]. Furthermore, after comparing their results to studies on other types of malignancies, they found that the expression models of miRNAs observed in the study differed from those obtained in a variety of other diseases, reaching the conclusion that the expression patterns were specific to ovarian cancer and were not influenced by chemotherapy or inflammation [154].
Other upregulated miRNAs in ovarian cancer tissues are miR-199a, miR-200a, miR-200b, miR-200 [132] and miR-200c, the latter being highly expressed only in the serous epithelial type [133]. miR-200 family was found to inhibit the transcriptional inhibitors of E-cadherin, ZEB1, and ZEB2, thus promoting E-cadherin expression and the transformation into epithelial cells of mesenchymal cells [134]. In addition, by decreasing the expression of miR-200a, ovarian cancer cells underwent epithelial-mesenchymal transition (EMT), leading to cell invasion and metastasis [134,135]. miR-200 expression levels corroborated with disease recurrence and survival rates, predicting poor outcomes when low levels were detected [135]. Moreover, the miR-200 cluster was also found to be involved in the inhibition of angiogenesis by targeting IL-8 and CXCL-1 produced by the tumor cells [134]. Providing proof of this, researchers transferred miR-200 in tumor epithelium, resulting in a critical decrease in angiogenesis and tumor metastasis [155]. Also a modulator of E-cadherin, miR-506 was proved to inhibit cell invasion, migration and EMT through another one of its transcriptional suppressors, known as SNAI2 [136]. When expressed in ovarian cancer, miR-506 was associated with a favorable prognosis [136].
Studies on the presence of miRNAs in various malignancies have shown that by inhibiting the expression of proteins like HMGA2, RAS, c-Myc, and cdk6, the let-7 family had a suppressing effect on tumor growth and cell invasion [137,138]. One of its constituents, let-7b, was proved to inhibit tumor growth, both in vitro and in vivo, while low levels of let-7f were detected in those ovarian cancer cells that had metastatic abilities and high invasion capabilities, further endorsing the supposition that it might play a suppressing role in tumor invasion and metastasis [134,138]. Also inhibiting metastasis, miR-183 and miR-22 were highly expressed in low metastatic ovarian cancer [139]. Further research is needed in order to develop reliable diagnostic and prognostic tools that would ensure better chances of survival for ovarian cancer patients.

4.3. miRNAs in Cervical Cancer

Cervical cancer is the fourth most common malignancy diagnosed in women, with human papilloma virus (HPV) being present in 99% of all cases [66,145]. Although the mechanisms are not yet fully understood, there seems to be a link between the development of cervical cancer and the altered expression of miRNAs [145,156]. It is well known that beside circulating freely or bound to proteins in the blood, miRNAs can also be found in exosomes, small extracellular vesicles that originate from all types of cells, including malignant ones [145]. They carry the genetic material of the cell that released them, their components, like mRNAs being associated with malignancy in various studies [145]. Exosomes could be used as vehicles for tumor-suppressing miRNAs which would improve the survival rates and the prognostic of cervical cancer patients [157].
Studies on cervical cancer have observed the upregulation of a number of circulating miRNAs, like miRNA-20a, miRNA-21, miRNA-203, miRNA-205, miRNA-485-5 as well as the upregulation of some tissue-specific miRNAs, among them being miR-7, miR-10a, miR135b and miR-149 [145]. miR-21a is known to stimulate inflammation and proliferation and inhibit apoptosis through a variety of mechanisms like angiogenesis, cell invasion, and transduction of NF-kB pathway [66]. After lowering its expression in SiHa cells, an inhibition of tumor growth was noted [158].
Another miRNA that was detected in high quantities in cervical cancer is miR-944. Although many studies stated that it functions as an oncogene in various malignancies, among them being cervical cancer, by stimulating cell migration, invasion and proliferation [140,141], there are some that described tumor suppressor properties in cancers located in the colon, stomach, and breast [159,160,161]. The hypothesis that it might have oncogenic functions was recently studied using five types of cell lines and 116 cervical tissues [142]. miR-944 was significantly higher expressed in cervical cancer tissues compared to controls, as well as in HPV-positive samples compared to those that were HPV-negative [142]. Its expression was associated with advanced FIGO stage, bulky tumor size, lymph node metastasis, and lower survival rates [142]. Considering all of the above, miR-944 could potentially serve as a prognostic biomarker [142].
In contrast, other miRNAs like miR-138 [143], miR-148b [162], miR-195 [163], miR-214 [164] were downregulated in cervical cancer, suggesting their role as tumor suppressors. As stated before, miR-138 is lowly expressed in this malignancy and acts like a tumor suppressor by stimulating apoptosis and preventing cell migration and tumor growth [143,144,145]. It is negatively associated with lymph node metastasis and advanced FIGO stage, having the potential of being used as a prognostic biomarker [143,144,145]. miR-34a and miR-206 could also be possible prognostic instruments as their low expression in cervical cancer was linked to late stage disease, lymph node dissemination, and low survival rates [165].
A major problem of cancer therapies is the development of chemotherapy resistance [166]. As already stated, miRNAs expression levels seem to influence the drug-sensitivity/resistance of the tumor cell [167]. Some of the miRNAs found to modulate the cells’ response to chemotherapy are miR-34a [168], miR-375 [169] and miR-664 [170]. For instance, a study on paclitaxel treated patients found that the expression levels of miR-375 increased in a dose-dependent pattern, leading to a decreased sensitivity to the drug both in vitro and in vivo [169,171]. In contrast, an increased expression of miR-664 in HeLa cells led to a higher sensitivity of cervical cancer cells to cisplatin and lowered cell migration [170].

4.4. miRNAs in Distinguishing Cancer Subtypes

Depending on the type of cells from which tumors originate and their underlying pathological mechanisms, cancers can be separated into distinct subtypes that possess different clinical and genetic features. Prompt and accurate diagnosis is virtually critical in order to initiate an adequate and effective treatment. Since miRNAs are known to be implicated in apoptosis regulation as well as initiation and progression of carcinogenesis [118,172], it stands to reason that these small RNAs be investigated with the aim of achieving a more precise diagnosis.
To illustrate this fact, we propose the typical example of lung cancer, where miRNA expression patterns differ not only between lung cancer patients and healthy people, but also between its two main categories, non-small-cell lung carcinoma (NSCLC) and small-cell lung carcinoma (SCLC), as well as their subtypes [173]. Up to date, in NSCLCs, an abundance of miRNAs has been identified, that can also vary depending on the stage of the disease [174,175]. A precise categorization into adenocarcinoma (ADC) and squamous cell carcinoma (SCC) is essential due to the differing treatment options [176]. For example, several studies highlighted a far higher expression of miR-205 in SCCs than in ADCs [177,178], with some authors referring to it as a promising biomarker for the detection and recurrence prediction for lung cancer patients [179]. On the same note, searching for non-invasive and highly sensitive biomarkers for the early diagnosis of NSCLC, Jin et al. managed to show, through next generation sequencing, that ADCs exhibit the tumor-derived exosomal miRNAs miR-30a-3p, miR-30e-3p, miR-181-5p and miR-361-5p, while SCCs predominantly express miR-10b-5p, miR-15b-5p and miR-320b [180]. Along the same lines, studies have shown that precursors of certain miRNAs, such as miRNA-944 and miRNA-3662, can differentiate early ADC from SCC, thus aiming to label pri-miRNAs as a novel class of lung cancer markers. In this regard, it was observed that pri-miRNA-3662 was highly expressed in ADC patients in stages I and II, while the expression of pri-miRNA-944 was higher in SCC patients in stages I and II [181]. On the other hand, in SCLC, Nishikawa et al. showed that miR-375 was significantly increased in SCLC cell lines as opposed to those of different histologic types when they examined the miRNA expression profiles on lung cell lines [182]. Moreover, a recent cohort study has identified 13 miRNAs able to accurately differentiate between SCLC and NSCLC patients, 3 of which, namely hsa-miR-331-5p, hsa-miR-451a and hsa-miR-363-3p, reaching sensitivity and specificity of 100% [183].
Breast cancer is another example of inter- and intratumor heterogeneity, with different cell populations observed not only among different individuals, but sometimes also within the same tumor [184,185]. The critical importance of biomarkers in guiding treatment for breast cancer has long been established [186,187] and earlier discussed. Because of the generally poorer prognosis of TNBC, research has been focused lately on the discovery of biomarkers of breast cancer subtypes. To this extent, Shin et al. noticed a decreased expression of miR-16, miR-21 and miR-199a-5p along with an increase in the levels of miR-92a-3p and miR-342-3p in both blood and tissue samples collected from TNBC patients [188]. Meanwhile, in HER2+ tumors, it has repeatedly been shown that miR-4728 is co-expressed with HER2, acting as an oncogene [189,190,191]. Moreover, Søkilde et al. have recently demonstrated the expression of two other small RNAs in HER2-enriched tumors, namely miR-2115 and miR-7158, which are otherwise very lowly expressed in healthy tissues [192]. Furthermore, they also observed that the miRNA cluster miR-99a/let-7c/miR-125b was predominantly expressed in Luminal A breast cancer (hormone receptor-positive/HER2-negative/levels of the protein Ki-67), its low expression predicting poorer outcomes [193], thus ensuring the distinction between it and Luminal B type tumors (hormone receptor-positive/HER2-positive/HER2-negative with high levels of Ki-67).
On the other hand, there are studies showing that some miRNAs that are highly expressed in cancer patients can be rather non-specific. For instance, Ferracin et al. assessed the levels of nine different miRNAs in the serum and plasma of lung, breast, colorectal and melanoma patients, and found that miR-21-5p was the single miRNA that was persistently raised in all cancer patients [194].

5. miRNAs in Cardiovascular Disease

According to the World Health Organization, cardiovascular diseases (CVDs) remain the number 1 cause of death worldwide. The need for novel diagnostic and prognostic biomarkers that can aid both the prevention and the treatment of CVD is therefore pressing. This review will mainly focus on acute coronary syndromes since findings continue to accumulate showing that heart attack and coronary artery disease, in general, are the most common causes of CVD deaths in both male and female patients [195].
Acute coronary syndromes refer to the damage suffered by the myocardial tissue as a result of decreased blood flow through the coronary arteries, its gravity depending on the grade of the occlusion. Circulating biomarkers that are traditionally used, while indispensable in current medical practice, still leave room for improvement, since, to date, no ideal cardiac biomarker can be singled out [196]. Cardiac troponins, considered one of the most advantageous assays for the diagnosis of acute myocardial necrosis, can be spilled into the bloodstream in any situation that involves damage to the cardiac muscle, making it an organ-specific biomarker [197]. For instance, an increase in oxygen demand caused by tachycardia can also lead to decreased perfusion, thus elevating the cardiac troponins [198]. A similar increase can be witnessed in infiltrative cardiomyopathies such as cardiac sarcoidosis [199]. A broad number of non-cardiac events can also lead to troponin elevation, including chronic renal failure [200], cerebrovascular accidents [201] but also strenuous endurance training [202].
With these disadvantages in mind, and due to the specificity of new molecular and genetic indicators, their use becomes more and more appealing. As seen in Table 2, quite a few studies have highlighted the upregulation of the same four miRNAs, namely miR-1, miR-133a, miR-208a/b, and miR-499, shortly after myocardial infarction [59,203,204,205], which are classically referred to as the myomiR family [206].
miR-1 is a muscle-specific miRNA that can be identified in both cardiac and skeletal muscles, its release in AMI suggesting necrotic death of cardiac myocytes as source [207]. Although on a relatively small sample, Ai et al. managed to highlight an obvious increase in miR-1 levels in acute myocardial infarction (AMI) patients, which returned to basal levels following treatment [61]. Other groups have also reported elevated plasma miR-1 levels in AMI patients [61,208], while Corsten et al. have pointed out that this elevation was rather more moderate compared to the ones detected in the case of miR-208b and miR-499 [59].
miR-208a is encoded within the α-cardiac muscle myosin heavy chain genes, while miR-208b and miR-499 are encoded within introns of the β-cardiac muscle myosin heavy chain genes and are, therefore, regarded as heart-specific miRNAs [209], with all three of them belonging to the miR-208 family [210]. An analysis performed by Wang et al. found that miR-208a had the highest accuracy in diagnosing AMI, with levels significantly increasing as early as 1 h post-occlusion in 90% of investigated AMI patients and in 100% AMI patients within 4 h [208]. On the other hand, when Liu et al. compared samples from AMI patients analyzing plasma levels of miR-1, -208 and -499, they discovered that the latter had the highest predictive value of the three, and a reliability above that of traditional cardiac biomarkers, TnT, and CKMB [205]. Their result was in accordance with that of Devaux et al., which included a much larger sample size [211].
As for miR-133, Zhou et al. have highlighted its significantly higher levels in AMI patients, showing that it has an approximately 4-fold increase that returns to normal levels after about 7 days [70]. This contradicts an earlier study that found no meaningful differences between AMI patients and normal individuals when it came to miR-133 levels [212]. Furthermore, Eitel at al. managed to demonstrate that miR-133a plasma levels could be an indicator of the clinical prognosis of ST-elevation myocardial infarction. They showed that the higher the levels, the greater the risk of serious myocardial damage, severe reperfusion injury and reduced myocardial salvage [213].
Table 2. miRNA regulation in acute coronary syndromes.
Table 2. miRNA regulation in acute coronary syndromes.
miRNAsSerum LevelsReferences
miR-1Ai et al. [61], Wang et al. [208], Corsten et al. [59], Liu et al. [205], Kuwabara et al. [214], Widera et al. [215]
miR-133aZhou et al. [70], Eitel at al. [213], Kuwabara et al. [214], Widera et al. [215], Xiao et al. [216]
miR-208a/bCorsten et al. [59], Liu et al. [205], Widera et al. [215], Liu et al. [210], Zhang et al. [217], Liu et al. [218]
miR-499Corsten et al. [59], Liu et al. [205], Adachi et al. [219], Youssef et al. [220], Zhao et al. [221]
Despite encouraging results, miRNAs are not currently used in the clinical diagnosis of acute myocardial infarction, for the most part because of the higher costs than those implied by the now gold-standard cardiac troponins, but also because of lack of sufficient clinical trials and standardized protocols.

6. miRNAs in Sepsis

Sepsis is a severe condition that is characterized by an abnormal host response to pathogenic microorganisms consisting of an excessive inflammatory response and subsequent multiple organ failure [222]. Although epidemiological data indicate that sepsis-associated mortality rates seem to decline, its incidence is still on the rise and it is therefore currently regarded as a major healthcare burden [223].
Accumulating evidence suggests that aberrant miRNA expression is associated with both sepsis [224,225] and non-infective systemic inflammatory response syndrome (SIRS) [226]. Previous studies have shown alterations in numerous circulating miRNAs during sepsis. Over time, biomarkers have been developed and used in the evaluation of sepsis, such as interleukins (IL)-1, -2, -4, -6, -8, -12, complement components C3a and C5a, C-reactive protein (CRP), procalcitonin (PCT), etc. [227,228], but it’s been proven that their individual deficiencies, sometimes to the point of inaccuracy, can be overcome when using combinations of these markers [229]. In order to further identify the pathogen(s), blood cultures are the next step, associating important limitations since they are time-consuming and can identify microbes that are only grown under standard conditions [230]. Due to these circumstances, the need for faster, cheaper and more specific alternatives persists, and miRNAs have shown themselves to be a promising method.
For instance, Yao L et al. observed that miR-25 levels dropped in sepsis patients and that lower levels were also associated with overall increased mortality. In doing that, they also highlighted the superiority of miR-25 over traditional biomarkers like C reactive protein and procalcitonin, according to the receiver operating characteristic (ROC) curve analysis performed within their study [231]. These findings were later experimentally reproduced in vivo and in vitro by Yao Y et al., showing that miR-25 was indeed downregulated in sepsis models consisting of cecal ligation and puncture in rodents and lipopolysaccharide-induced cardiomyocytes [232].
It is currently accepted that miR-223 is a crucial player in regulating exaggerated immune responses through its targets which include, among others, Stathmin, STAT3, Granzyme B, IGFR1 and Artemin [233]. In a study performed by Wang et al. investigating 50 sepsis diagnosed patients, miR-223 had lower plasma values compared to SIRS patients and healthy individuals [234].
miR-155 is another important regulator of the inflammatory response, with recent studies highlighting its key role during bacterial infection, where it acts as a negative regulator of inflammation [235]. Bandyopadhyay et al. showed that the inducing of miR-155 during Francisella tularensis infection prompted the translational repression of MyD88 and SHIP-1, thus inhibiting the secretion of endotoxin-stimulated TNFα and attenuating the innate inflammatory response [236].
miR-150 is a molecule that is expressed almost exclusively in immune cells [237]. Its dysregulation in sepsis has been recognized by different authors [238,239]. However, other reports show that the differences between miR-150 levels in patients with sepsis and patients with non-infective SIRS were minute, while also highlighting that it was rather relevant in predicting a poor outcome of critical conditions regardless of the presence of infection, thus recommending its use as a prognostic marker in critical patients [224,240].
The deregulation of many miRNAs depending on the causative pathogen has been documented over the years, and we illustrated some of the most significant and recent findings in Table 3.
Many authors have suggested the use of miRNAs as biomarkers for a more targeted diagnosis that bypasses the time-consuming microbiological confirmation of infection. Still, some issues remain, including the lack of a standardized protocol regarding specimen collection, handling, and analysis.

7. miRNAs in Nervous System Disorders

Due to its intricacy, the nervous system (NS) represents one of the most important biological systems and is comprised of different cell types and advanced synaptic communications [250]. Nervous system diseases (NSDs), as a class of nervous disorders, are one of the main causes of disability and mortality [251]. In the field of neurology, even though the exact functions of miRNAs remain uncertain, studies conducted so far underline their potential to reveal the evolution of various diseases, making these molecules attractive candidates as potential biomarkers both for diagnosis and prognosis.
miRNAs are also responsible for NS functionality, being implicated in translation, RNA metabolism, gene development and regulation [252]. Many NSDs such as Alzheimer’s disease (AD), epilepsy, Parkinson’s disease (PD), glioblastoma (GBM), multiple sclerosis (MS), and myasthenia gravis (MG) are caused in part by aberrant expressions and/or dysfunctions of miRNAs [251,253,254,255,256,257].
It is known that AD represents one of the main causes for dementia, even if its exact pathogenesis is still unclear and there is no treatment available. In the cortex of AD patients, regardless of stage, miR-107 is poorly expressed [258]. Epilepsy, characterized by recurrent seizures that generate abnormal neuronal activity in the brain, is one of the most common NSDs globally [253,254]. In many cases of PD patients, a neurodegenerative disorder is characterized by motor and nonmotor symptoms, the overexpression of miRNAs miR-103a-3p, miR-30b-5p and miR-29a-3p can serve as potential biomarkers. Moreover, the expression levels of miR-I, miR-22 and miR-29 are used in order to distinguish healthy subjects from non-treated PD ones [259]. Also, miRNAs play important roles in the most lethal brain tumor (GBM) in cellular proliferation, apoptosis, invasion, angiogenesis and stemness [255]. In MS and MG, evidence indicates the implication of miR-132, miR-124, and miR-155 [257,260]. A brief summary of miRNAs implicated in neurologic disorders is provided in Table 4.
In Alzheimer’s disease, miRNA alterations can be detected both in the brain and cerebrospinal fluid (CSF) of the patients, and also in the biological fluids, such as plasma and serum, making them potential candidates as biomarkers [275]. The biomarkers currently in use are represented by tau (tubulin-associated unit) proteins and Aβ peptides [276]. However, they present certain drawbacks in the clinical practice, such as a lack of standardization of the technique in biological fluids and not enough evidence for cut-off values [277]. Also, in patients with mild cognitive impairment (MCI), their predictive values are still to be determined [278]. miRNAs on the other hand display higher sensitivity levels in comparison to protein biomarkers, mainly due to PCR amplification [279]. In addition, their analysis represents a relatively simple method, it is non-invasive and has a far lower cost than the currently used diagnostic techniques, such as MRI or molecular neuroimaging with PET.
Parkinson’s disease is a complex neurological ailment, which leads to peripheral deficits and cognitive impairment. At the moment, its diagnosis is established on motor functions rating and other clinical signs, which can only be assessed when the loss of the DAergic neuron is approximately 70% [280]. In this case, miRNAs have the potential to become reliable biomarkers for an early and precise diagnosis. Their alternative is represented by the detection of certain proteins, such as Lewy body formation or DJ-1 (that reveals the dysfunction of mitochondria) and α-SYN (for the aggregation of proteins) in brain tissue or CSF [281]. However, these are invasive procedures, or they are possible only after the patient has deceased. A large number of circulating miRNAs has been discovered in the past years for Parkinson’s disease, some of which could lead to an easier, non-invasive diagnosis for this pathology [282].
Similar impediments have been encountered in the case of multiple sclerosis. The currently used biomarkers include IgG and IgM antibodies, glycoproteins, markers of inflammation and chemokines [283]. However, the progression of this disease is very unpredictable and numerous phenotypes have been described, both of these issues leading to a lack of correlation with the present biomarkers. New and improved molecules are needed to solve this and provide a more specific diagnosis for the various phenotypes, determine the course of the disease and create a link with the level of disability [284,285]. In addition, less invasive methods could prove themselves more useful in clinical practice, regarding that the current source of biomarkers is CSF. miRNAs have the potential to meet all the discussed criteria.
Myasthenia gravis is an autoimmune condition characterized by the attack of autoantibodies upon the antigens found in the neuromuscular junction (AChR or MuSK). These antibodies represent a source of biomarkers for this disease, but even though they have the potential to diagnose it, the correlation between their titer and the progression of the disease or the patient’s response to therapy is still unreliable [286]. Further on, electromyography is another tool used for aiding the diagnosis in the clinical practice, but its practicality is limited by the low availability of expertise. The objective signs of this pathology rapidly fluctuate and can also be influenced by medication. The use of miRNAs as biomarkers for this condition could lead to an easier and improved method for diagnosis, a suitable solution for tracking the course of the disease and also a way to measure the response to treatment.
In 2007, Sayed et al. illustrated the importance of miRNAs in the NS by the finding that 70% of known miRNAs are expressed in the brain [287]. It is known that miR-9 and miR-134, both brain-specific, were extensively studied in neurogenesis and their aberrant expression was associated with abnormal brain development and many neurodevelopmental diseases [288,289,290]. It is important to evaluate miRNAs expression in NSDs in order to get relevant molecular information and help clinical management of the disease processes [1].

8. Limitations of Circulating miRNAs as Biomarkers

Nowadays, circulating miRNAs can be obtained from venous plasma or serum, and it was demonstrated that miRNAs profiles in venous and arterial plasma were very similar, with no significant differences in theirs profiles between venous and arterial blood. In cancer detection, the use of venous miRNAs is still challenged in some cases. For example, in a study on plasma samples from male rats performed in 2017, the authors identified ten arterial and fourteen venous highly expressed miRNAs, and the miRNA profiles in arterial plasma showed higher correlation with that in tissue [291]. In humans, sample studies showed similar observations, leading to the conclusion that the blood sampling method should be carefully chosen for specific miRNA biomarkers [292,293].
miRNAs can be detected using various specific and sensitive approaches, including Northern blot analysis [294,295], in situ hybridization [296], real-time PCR [95,297], miRNA microarray [298,299] and next-generation sequencing (NGS) [300], and some of them are already used as diagnostic or prognostic markers, this demonstrating their utility in both clinical and personalized medicine. Some previous research regarding circulating miRNAs did not result in highly specific and validated disease markers due to the lack of taking into consideration important factors such as prior treatments, age, sex. Also, data reproducibility may pose an important problem, due to a lack of consistency [301]. In order to determine the accurate level of expression of miRNA in a specific tissue, body fluid or cell type and to ensure that both the positive and negative results are reliable, it is important to minimize experimental or technical variations by controlling the pre-processing of miRNA detection and normalization experiments, data processing and optimization [302], due to the minor differences in miRNAs expression levels between healthy people and patients [173]. Binderup et al. have emphasized the importance of the chosen normalization strategy in RT-qPCR data analyses [303]. The most frequent normalization technique involves the use of an exogenous spike-in of a synthetic miRNA such as cel-miR-39 or an endogenous one like miR-16 as reference genes during the initial phase of RNA extraction [304]. However, it is generally strongly implied that the use of a single reference gene is insufficient for accurate miRNA results, with researchers preferring strategies that apply a combination of control miRNAs, both exogenous and endogenous [305].
One issue for using miRNAs as diagnostic markers is the relatively poor diagnostic specificity and reproducibility of some identified miRNAs. The methods used for miRNA detection need to be standardized prior to use for diagnostic purposes in order to generate and discover useful research hypotheses and new biomarkers for in vitro diagnostics [1]. The main challenges in using miRNAs in in vitro diagnostics are discovering specific miRNAs that can be used as biomarkers in a broad range of patients and developing accurate, simple and inexpensive methods that involve standardized pre- and post-analytical procedures.
miRNA expression is dysregulated in patients’ brains compared to healthy ones, and there is genetic evidence showing that these mutations that occur and disrupt miRNAs transcription are found in NDs. Manipulation of miRNAs could change the phenotypes in some NDs in experiments performed using animal models, so it is reasonable to expect to use miRNAs as therapeutic agents due to the small size of miRNA molecules [306]. Although using miRNAs as biomarkers and in therapy is very promising, there are still a lot of challenges, including the possibility of off-target effects which may cause severe unanticipated responses. Moreover, one miRNA can function differently according to the stage of the disease, making it difficult to be used as a precise target [307].
To avoid limitations, it is important to take into consideration aspects such as the sample size (if it is not powerful enough, it would not be able to detect variants with minor effects), prospective studies, which are important in confirming the observational study results, and the validation process, which has to be performed only after warranty of the epidemiological and functional studies [308].

9. Why Are miRNAs Not Used in Current Practice?

For clinical application, the most important evaluation criteria for circulating miRNAs as diagnostic and prognostic biomarkers are high sensitivity and specificity, to avoid false positive or negative diagnosis. In clinic, an appropriate biomarker for a specific cancer type should be both significantly differentially expressed and in correlation with the outcome of patients. The low specificity of single miRNA molecules compared with the circulating miRNA molecules was highlighted. Many cell-free miRNAs showed altered expression patterns in different types of cancers instead of a certain type, and also similar expression between benign and malignant tumors; miR-21-5p, miR-155-5p and miR-210-3p for example, are all involved in many cancer types, but for miR-21-5p, significantly increased levels in plasma of CRC patients were observed, but it could not distinguish between malignant cancer and benign polyps [309]. In patients with NSCLC [175,310,311,312,313], liver cancer [314] or gastric cancer [315,316], it was reportedly upregulated, but the same molecule was downregulated in patients suffering from breast cancer [188,313]. This means that the contribution of miRNAs in various types of cancers differs.
Therefore, it is essential to have a larger sample size to be able to decide between the healthy or diseased status. Due to many criteria used in clinical applications like age, gender, ethnicity, lifestyle, pre-treatment, history of diseases and so on it is mandatory to avoid the obstacle of limited sample size. Regarding detection results, they can be affected by measurement principle, method used, instrument, technicians [173].

10. Perspectives and Conclusions

Since their discovery, a large number of miRNAs has been described. Although progress has been made towards figuring out the role of miRNAs in pathological cases, many conditions remain insufficiently characterized since it is still a relatively young field. However, great potential lies in identifying these disease-specific molecules and integrating their role in the strategy of diagnosis, prognosis, and treatment.
Numerous miRNAs are frequently altered in various conditions, starting from the world’s main cause of morbidity and mortality, namely cardiovascular disease, to neurological disorders, sepsis and last but not least, to cancer. In order to rapidly diagnose the diseases associated with an increased death rate, new and improved methods are necessary. miRNAs have the required properties to constitute such a method.
Although many miRNAs can be found intracellularly, they have also been observed on the outside of the cell, later being coined circulating miRNAs, and demonstrated high stability and specificity. The high stability of circulating miRNA is mainly due to the encapsulation in lipid vesicles or the formation of complexes with different kinds of proteins that protect them against denaturation. Therefore, a wide variety of miRNAs are detected in exosomes, microvesicles, or high-density lipoprotein particles from a diversity of biological fluids, such as blood, urine, saliva, peritoneal fluid, amniotic fluid, bronchial lavage, CSF, and tears [2,317,318].
Several studies have shown that miRNAs are released in the bloodstream from different organs such as brain, heart, endothelial cells, ovary, uterus, and mammary glands, in both healthy subjects and patients. In cancer patients, studies demonstrated that circulating miRNAs seem to originate from tumor tissues and may evaluate tumor progression. Moreover, after resection of the tumor, oncogenic miRNAs levels tend to decrease [319].
Since it was discovered that miRNA could be detected in both extra- and intracellular environments, their potential use as biomarkers became one of the main focuses of current research. Although many studies have been conducted, there is still a lack of consistency between many miRNA signatures. These differences may be due to the source of miRNA (blood, plasma, CSF,) and the size of the cohorts of patients with various comorbidities that may influence these molecules. For instance, some studies of Alzheimer’s disease analyze miRNAs originating from distinct blood compartments [320,321] while others focus on miRNAs from CSF [262,322,323]. It has been shown that hsa-miR-146a was upregulated in one of the latter studies [262], while in the second one it was downregulated [322] and the third showed no modification [323]. Not to mention that the sampling method, as well as the preservation and the processing of the samples, can also lead to discrepancies regarding the results.
Nowadays, various methods have been developed depending on the purpose of the project: hybridization-based approaches (microarray, nCounter Nanostring technology), reverse transcription quantitative PCR arrays (RT-qPCR), NGS. An initial inexpensive screening can be carried out through microarray based on RNA-DNA hybrid capture, while NGS can be used for detecting new miRNAs and different isoforms [324,325,326]. Due to the many qualities of RT-qPCR, which include high sensitivity, specificity, accessibility, and reproducibility, RT-qPCR is currently the most used technique, remaining the gold standard method for the verification of microarray and NGS results.
There is an imperious need for faster ways to detect different pathologies, especially seeing that many types of cancer are discovered in late stages. miRNAs as biomarkers can fulfil this desideratum despite present limitations, thus being an impressive research field. As current techniques evolve, we anticipate that miRNAs will become a routine approach in the development of personalized patient profiles, therefore allowing targeted therapeutic interventions.

Author Contributions

Conceptualization, C.E.C., D.C.T., M.G.B.; methodology, D.C., S.M.C.; investigation, O.L.B., A.B.; resources, N.S.; writing—original draft preparation, C.E.C., D.C.T., M.G.B., O.L.B., A.B.; writing—review and editing, D.C.; visualization, S.M.C.; supervision, S.M.C.; project administration, C.E.C.; funding acquisition, N.S., S.C.V. All authors have read and agreed to the published version of the manuscript.

Funding

This work and the APC were supported by grants of the Romanian Ministry of Research and Innovation, CCCDI-UEFISCDI, projects number PN-III-P1-1.2-PCCDI-2017-0833/68/2018 and PN-III-P1-1.2-PCCDI-2017-0820/67/2018 within PNCDI III.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Wang, J.; Chen, J.; Sen, S. MicroRNA as Biomarkers and Diagnostics. J. Cell. Physiol. 2016, 231, 25–30. [Google Scholar] [CrossRef]
  2. Etheridge, A.; Lee, I.; Hood, L.; Galas, D.; Wang, K. Extracellular microRNA: A new source of biomarkers. Mutat. Res. 2011, 717, 85–90. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  3. Chevillet, J.R.; Lee, I.; Briggs, H.A.; He, Y.; Wang, K. Issues and prospects of microRNA-based biomarkers in blood and other body fluids. Molecules 2014, 19, 6080–6105. [Google Scholar] [CrossRef] [PubMed]
  4. Kai, K.; Dittmar, R.L.; Sen, S. Secretory microRNAs as biomarkers of cancer. Semin. Cell Dev. Biol. 2018, 78, 22–36. [Google Scholar] [CrossRef]
  5. Wightman, B.; Ha, I.; Ruvkun, G. Posttranscriptional regulation of the heterochronic gene lin-14 by lin-4 mediates temporal pattern formation in C. elegans. Cell 1993, 75, 855–862. [Google Scholar] [CrossRef]
  6. Lee, R.C.; Feinbaum, R.L.; Ambros, V. The C. elegans heterochronic gene lin-4 encodes small RNAs with antisense complementarity to lin-14. Cell 1993, 75, 843–854. [Google Scholar] [CrossRef]
  7. Lee, R.C.; Ambros, V. An extensive class of small RNAs in Caenorhabditis elegans. Science 2001, 294, 862–864. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  8. Lau, N.C.; Lim, L.P.; Weinstein, E.G.; Bartel, D.P. An abundant class of tiny RNAs with probable regulatory roles in Caenorhabditis elegans. Science 2001, 294, 858–862. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  9. Hydbring, P.; Badalian-Very, G. Clinical applications of microRNAs. F1000Research 2013, 2, 136. [Google Scholar] [CrossRef]
  10. Friedman, R.C.; Farh, K.K.; Burge, C.B.; Bartel, D.P. Most mammalian mRNAs are conserved targets of microRNAs. Genome Res. 2009, 19, 92–105. [Google Scholar] [CrossRef] [Green Version]
  11. Lee, Y.; Kim, M.; Han, J.; Yeom, K.H.; Lee, S.; Baek, S.H.; Kim, V.N. MicroRNA genes are transcribed by RNA polymerase II. EMBO J. 2004, 23, 4051–4060. [Google Scholar] [CrossRef] [PubMed]
  12. Krol, J.; Loedige, I.; Filipowicz, W. The widespread regulation of microRNA biogenesis, function and decay. Nat. Rev. Genet. 2010, 11, 597–610. [Google Scholar] [CrossRef] [PubMed]
  13. Lee, Y.; Ahn, C.; Han, J.; Choi, H.; Kim, J.; Yim, J.; Lee, J.; Provost, P.; Radmark, O.; Kim, S.; et al. The nuclear RNase III Drosha initiates microRNA processing. Nature 2003, 425, 415–419. [Google Scholar] [CrossRef] [PubMed]
  14. Gregory, R.I.; Yan, K.P.; Amuthan, G.; Chendrimada, T.; Doratotaj, B.; Cooch, N.; Shiekhattar, R. The Microprocessor complex mediates the genesis of microRNAs. Nature 2004, 432, 235–240. [Google Scholar] [CrossRef] [PubMed]
  15. Lee, Y.; Jeon, K.; Lee, J.T.; Kim, S.; Kim, V.N. MicroRNA maturation: Stepwise processing and subcellular localization. EMBO J. 2002, 21, 4663–4670. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  16. Yi, R.; Qin, Y.; Macara, I.G.; Cullen, B.R. Exportin-5 mediates the nuclear export of pre-microRNAs and short hairpin RNAs. Genes Dev. 2003, 17, 3011–3016. [Google Scholar] [CrossRef] [Green Version]
  17. Kwak, P.B.; Tomari, Y. The N domain of Argonaute drives duplex unwinding during RISC assembly. Nat. Struct. Mol. Biol. 2012, 19, 145–151. [Google Scholar] [CrossRef]
  18. Borchert, G.M.; Lanier, W.; Davidson, B.L. RNA polymerase III transcribes human microRNAs. Nat. Struct. Mol. Biol. 2006, 13, 1097–1101. [Google Scholar] [CrossRef]
  19. Noland, C.L.; Doudna, J.A. Multiple sensors ensure guide strand selection in human RNAi pathways. RNA 2013, 19, 639–648. [Google Scholar] [CrossRef] [Green Version]
  20. Khvorova, A.; Reynolds, A.; Jayasena, S.D. Functional siRNAs and miRNAs exhibit strand bias. Cell 2003, 115, 209–216. [Google Scholar] [CrossRef] [Green Version]
  21. Meijer, H.A.; Smith, E.M.; Bushell, M. Regulation of miRNA strand selection: Follow the leader? Biochem. Soc. Trans. 2014, 42, 1135–1140. [Google Scholar] [CrossRef] [PubMed]
  22. Ha, M.; Kim, V.N. Regulation of microRNA biogenesis. Nat. Rev. Mol. Cell Biol. 2014, 15, 509–524. [Google Scholar] [CrossRef] [PubMed]
  23. O’Brien, J.; Hayder, H.; Zayed, Y.; Peng, C. Overview of MicroRNA Biogenesis, Mechanisms of Actions, and Circulation. Front. Endocrinol. 2018, 9, 402. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  24. Bartel, D.P. MicroRNAs: Target recognition and regulatory functions. Cell 2009, 136, 215–233. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  25. Huntzinger, E.; Izaurralde, E. Gene silencing by microRNAs: Contributions of translational repression and mRNA decay. Nat. Rev. Genet. 2011, 12, 99–110. [Google Scholar] [CrossRef] [PubMed]
  26. Eichhorn, S.W.; Guo, H.; McGeary, S.E.; Rodriguez-Mias, R.A.; Shin, C.; Baek, D.; Hsu, S.H.; Ghoshal, K.; Villen, J.; Bartel, D.P. mRNA destabilization is the dominant effect of mammalian microRNAs by the time substantial repression ensues. Mol. Cell 2014, 56, 104–115. [Google Scholar] [CrossRef] [Green Version]
  27. Helwak, A.; Kudla, G.; Dudnakova, T.; Tollervey, D. Mapping the human miRNA interactome by CLASH reveals frequent noncanonical binding. Cell 2013, 153, 654–665. [Google Scholar] [CrossRef] [Green Version]
  28. Jonas, S.; Izaurralde, E. Towards a molecular understanding of microRNA-mediated gene silencing. Nat. Rev. Genet. 2015, 16, 421–433. [Google Scholar] [CrossRef]
  29. Zen, K.; Zhang, C.Y. Circulating microRNAs: A novel class of biomarkers to diagnose and monitor human cancers. Med. Res. Rev. 2012, 32, 326–348. [Google Scholar] [CrossRef]
  30. Weber, J.A.; Baxter, D.H.; Zhang, S.; Huang, D.Y.; Huang, K.H.; Lee, M.J.; Galas, D.J.; Wang, K. The microRNA spectrum in 12 body fluids. Clin. Chem. 2010, 56, 1733–1741. [Google Scholar] [CrossRef]
  31. Wang, K.; Zhang, S.; Weber, J.; Baxter, D.; Galas, D.J. Export of microRNAs and microRNA-protective protein by mammalian cells. Nucleic Acids Res. 2010, 38, 7248–7259. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  32. Zubakov, D.; Boersma, A.W.; Choi, Y.; van Kuijk, P.F.; Wiemer, E.A.; Kayser, M. MicroRNA markers for forensic body fluid identification obtained from microarray screening and quantitative RT-PCR confirmation. Int. J. Legal. Med. 2010, 124, 217–226. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  33. Hanson, E.K.; Lubenow, H.; Ballantyne, J. Identification of forensically relevant body fluids using a panel of differentially expressed microRNAs. Anal. Biochem. 2009, 387, 303–314. [Google Scholar] [CrossRef] [PubMed]
  34. Raposo, G.; Stoorvogel, W. Extracellular vesicles: Exosomes, microvesicles, and friends. J. Cell Biol. 2013, 200, 373–383. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  35. O’Driscoll, L. Expanding on exosomes and ectosomes in cancer. N. Engl. J. Med. 2015, 372, 2359–2362. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  36. Vickers, K.C.; Palmisano, B.T.; Shoucri, B.M.; Shamburek, R.D.; Remaley, A.T. MicroRNAs are transported in plasma and delivered to recipient cells by high-density lipoproteins. Nat. Cell. Biol. 2011, 13, 423–433. [Google Scholar] [CrossRef] [Green Version]
  37. Wang, K.; Li, H.; Yuan, Y.; Etheridge, A.; Zhou, Y.; Huang, D.; Wilmes, P.; Galas, D. The complex exogenous RNA spectra in human plasma: An interface with human gut biota? PLoS ONE 2012, 7, e51009. [Google Scholar] [CrossRef]
  38. Semenov, D.V.; Baryakin, D.N.; Brenner, E.V.; Kurilshikov, A.M.; Vasiliev, G.V.; Bryzgalov, L.A.; Chikova, E.D.; Filippova, J.A.; Kuligina, E.V.; Richter, V.A. Unbiased approach to profile the variety of small non-coding RNA of human blood plasma with massively parallel sequencing technology. Expert Opin. Biol. Ther. 2012, 12 (Suppl. S1), S43–S51. [Google Scholar] [CrossRef]
  39. Arroyo, J.D.; Chevillet, J.R.; Kroh, E.M.; Ruf, I.K.; Pritchard, C.C.; Gibson, D.F.; Mitchell, P.S.; Bennett, C.F.; Pogosova-Agadjanyan, E.L.; Stirewalt, D.L.; et al. Argonaute2 complexes carry a population of circulating microRNAs independent of vesicles in human plasma. Proc. Natl. Acad. Sci. USA 2011, 108, 5003–5008. [Google Scholar] [CrossRef] [Green Version]
  40. Cortez, M.A.; Bueso-Ramos, C.; Ferdin, J.; Lopez-Berestein, G.; Sood, A.K.; Calin, G.A. MicroRNAs in body fluids--the mix of hormones and biomarkers. Nat. Rev. Clin. Oncol. 2011, 8, 467–477. [Google Scholar] [CrossRef] [Green Version]
  41. Weickmann, J.L.; Glitz, D.G. Human ribonucleases. Quantitation of pancreatic-like enzymes in serum, urine, and organ preparations. J. Biol. Chem. 1982, 257, 8705–8710. [Google Scholar]
  42. Valadi, H.; Ekström, K.; Bossios, A.; Sjöstrand, M.; Lee, J.J.; Lötvall, J.O. Exosome-mediated transfer of mRNAs and microRNAs is a novel mechanism of genetic exchange between cells. Nat. Cell Biol. 2007, 9, 654–659. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  43. Morello, M.; Minciacchi, V.R.; de Candia, P.; Yang, J.; Posadas, E.; Kim, H.; Griffiths, D.; Bhowmick, N.; Chung, L.W.; Gandellini, P.; et al. Large oncosomes mediate intercellular transfer of functional microRNA. Cell Cycle 2013, 12, 3526–3536. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  44. Kharaziha, P.; Ceder, S.; Li, Q.; Panaretakis, T. Tumor cell-derived exosomes: A message in a bottle. Biochim. Biophys. Acta 2012, 1826, 103–111. [Google Scholar] [CrossRef] [PubMed]
  45. Mittelbrunn, M.; Gutierrez-Vazquez, C.; Villarroya-Beltri, C.; Gonzalez, S.; Sanchez-Cabo, F.; Gonzalez, M.A.; Bernad, A.; Sanchez-Madrid, F. Unidirectional transfer of microRNA-loaded exosomes from T cells to antigen-presenting cells. Nat. Commun. 2011, 2, 282. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  46. Villarroya-Beltri, C.; Gutierrez-Vazquez, C.; Sanchez-Cabo, F.; Perez-Hernandez, D.; Vazquez, J.; Martin-Cofreces, N.; Martinez-Herrera, D.J.; Pascual-Montano, A.; Mittelbrunn, M.; Sanchez-Madrid, F. Sumoylated hnRNPA2B1 controls the sorting of miRNAs into exosomes through binding to specific motifs. Nat. Commun. 2013, 4, 2980. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  47. Batagov, A.O.; Kuznetsov, V.A.; Kurochkin, I.V. Identification of nucleotide patterns enriched in secreted RNAs as putative cis-acting elements targeting them to exosome nano-vesicles. BMC Genom. 2011, 12 (Suppl. S3), S18. [Google Scholar] [CrossRef] [Green Version]
  48. Baggish, A.L.; Hale, A.; Weiner, R.B.; Lewis, G.D.; Systrom, D.; Wang, F.; Wang, T.J.; Chan, S.Y. Dynamic regulation of circulating microRNA during acute exhaustive exercise and sustained aerobic exercise training. J. Physiol. 2011, 589, 3983–3994. [Google Scholar] [CrossRef]
  49. Clauss, S.; Wakili, R.; Hildebrand, B.; Kaab, S.; Hoster, E.; Klier, I.; Martens, E.; Hanley, A.; Hanssen, H.; Halle, M.; et al. MicroRNAs as Biomarkers for Acute Atrial Remodeling in Marathon Runners (The miRathon Study—A Sub-Study of the Munich Marathon Study). PLoS ONE 2016, 11, e0148599. [Google Scholar] [CrossRef]
  50. Cui, S.; Sun, B.; Yin, X.; Guo, X.; Chao, D.; Zhang, C.; Zhang, C.Y.; Chen, X.; Ma, J. Time-course responses of circulating microRNAs to three resistance training protocols in healthy young men. Sci. Rep. 2017, 7, 2203. [Google Scholar] [CrossRef] [Green Version]
  51. Ranganathan, K.; Sivasankar, V. MicroRNAs—Biology and clinical applications. J. Oral. Maxillofac. Pathol. 2014, 18, 229–234. [Google Scholar] [CrossRef] [PubMed]
  52. Wong, C.K.; Gromisch, C.; Ozturk, S.; Papageorgis, P.; Abdolmaleky, H.M.; Reinhard, B.M.; Thiagalingam, A.; Thiagalingam, S. MicroRNA-4417 is a tumor suppressor and prognostic biomarker for triple-negative breast cancer. Cancer Biol. Ther. 2019, 20, 1113–1120. [Google Scholar] [CrossRef] [PubMed]
  53. Zhang, H.D.; Jiang, L.H.; Sun, D.W.; Hou, J.C.; Ji, Z.L. CircRNA: A novel type of biomarker for cancer. Breast Cancer 2018, 25, 1–7. [Google Scholar] [CrossRef] [PubMed]
  54. Gao, C.; Zhou, C.; Zhuang, J.; Liu, L.; Liu, C.; Li, H.; Liu, G.; Wei, J.; Sun, C. MicroRNA expression in cervical cancer: Novel diagnostic and prognostic biomarkers. J. Cell. Biochem. 2018, 119, 7080–7090. [Google Scholar] [CrossRef]
  55. Wiedrick, J.T.; Phillips, J.I.; Lusardi, T.A.; McFarland, T.J.; Lind, B.; Sandau, U.S.; Harrington, C.A.; Lapidus, J.A.; Galasko, D.R.; Quinn, J.F.; et al. Validation of MicroRNA Biomarkers for Alzheimer’s Disease in Human Cerebrospinal Fluid. J. Alzheimers Dis. 2019, 67, 875–891. [Google Scholar] [CrossRef]
  56. Tigchelaar, S.; Gupta, R.; Shannon, C.P.; Streijger, F.; Sinha, S.; Flibotte, S.; Rizzuto, M.A.; Street, J.; Paquette, S.; Ailon, T.; et al. MicroRNA Biomarkers in Cerebrospinal Fluid and Serum Reflect Injury Severity in Human Acute Traumatic Spinal Cord Injury. J. Neurotrauma 2019, 36, 2358–2371. [Google Scholar] [CrossRef]
  57. Raoof, R.; Bauer, S.; El Naggar, H.; Connolly, N.M.C.; Brennan, G.P.; Brindley, E.; Hill, T.; McArdle, H.; Spain, E.; Forster, R.J.; et al. Dual-center, dual-platform microRNA profiling identifies potential plasma biomarkers of adult temporal lobe epilepsy. EBioMedicine 2018, 38, 127–141. [Google Scholar] [CrossRef] [Green Version]
  58. Sheinerman, K.S.; Umansky, S.R. Circulating cell-free microRNA as biomarkers for screening, diagnosis and monitoring of neurodegenerative diseases and other neurologic pathologies. Front. Cell. Neurosci. 2013, 7, 150. [Google Scholar] [CrossRef] [Green Version]
  59. Corsten, M.F.; Dennert, R.; Jochems, S.; Kuznetsova, T.; Devaux, Y.; Hofstra, L.; Wagner, D.R.; Staessen, J.A.; Heymans, S.; Schroen, B. Circulating MicroRNA-208b and MicroRNA-499 reflect myocardial damage in cardiovascular disease. Circ. Cardiovasc. Genet. 2010, 3, 499–506. [Google Scholar] [CrossRef]
  60. Tijsen, A.J.; Creemers, E.E.; Moerland, P.D.; de Windt, L.J.; van der Wal, A.C.; Kok, W.E.; Pinto, Y.M. MiR423-5p as a circulating biomarker for heart failure. Circ. Res. 2010, 106, 1035–1039. [Google Scholar] [CrossRef]
  61. Ai, J.; Zhang, R.; Li, Y.; Pu, J.; Lu, Y.; Jiao, J.; Li, K.; Yu, B.; Li, Z.; Wang, R.; et al. Circulating microRNA-1 as a potential novel biomarker for acute myocardial infarction. Biochem. Biophys. Res. Commun. 2010, 391, 73–77. [Google Scholar] [CrossRef] [PubMed]
  62. Sun, Z.; Zhang, Q.; Cui, X.; Yang, J.; Zhang, B.; Song, G. Differential expression of miRNA and its role in sepsis. Pediatrics 2018, 142, 563. [Google Scholar] [CrossRef]
  63. Mori, M.A.; Ludwig, R.G.; Garcia-Martin, R.; Brandão, B.B.; Kahn, C.R. Extracellular miRNAs: From Biomarkers to Mediators of Physiology and Disease. Cell Metab. 2019, 30, 656–673. [Google Scholar] [CrossRef] [PubMed]
  64. Tutar, L.; Ozgur, A.; Tutar, Y. Involvement of miRNAs and Pseudogenes in Cancer. Methods Mol. Biol. 2018, 1699, 45–66. [Google Scholar] [CrossRef] [PubMed]
  65. Terrinoni, A.; Calabrese, C.; Basso, D.; Aita, A.; Caporali, S.; Plebani, M. The circulating miRNAs as diagnostic and prognostic markers. Clin. Chem. Lab. Med. 2019, 57, 932–953. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  66. Nahand, J.S.; Taghizadeh-boroujeni, S.; Karimzadeh, M.; Borran, S.; Pourhanifeh, M.H.; Moghoofei, M.; Bokharaei-Salim, F.; Karampoor, S.; Jafari, A.; Asemi, Z.; et al. microRNAs: New prognostic, diagnostic, and therapeutic biomarkers in cervical cancer. J. Cell. Physiol. 2019, 234, 17064–17099. [Google Scholar] [CrossRef]
  67. Mahdian-Shakib, A.; Dorostkar, R.; Tat, M.; Hashemzadeh, M.S.; Saidi, N. Differential role of microRNAs in prognosis, diagnosis, and therapy of ovarian cancer. Biomed. Pharmacother. 2016, 84, 592–600. [Google Scholar] [CrossRef]
  68. Asiaf, A.; Ahmad, S.T.; Arjumand, W.; Zargar, M.A. MicroRNAs in Breast Cancer: Diagnostic and Therapeutic Potential. Methods Mol. Biol. 2018, 1699, 23–43. [Google Scholar] [CrossRef]
  69. Parizadeh, S.M.; Jafarzadeh-Esfehani, R.; Ghandehari, M.; Parizadeh, S.M.R.; Hassanian, S.M.; Rezayi, M.; Ghayour-Mobarhan, M.; Ferns, G.A.; Avan, A. Circulating Exosomes as Potential Biomarkers in Cardiovascular Disease. Curr. Pharm. Des. 2018, 24, 4436–4444. [Google Scholar] [CrossRef]
  70. Zhou, S.-S.; Jin, J.-P.; Wang, J.-Q.; Zhang, Z.-G.; Freedman, J.H.; Zheng, Y.; Cai, L. miRNAS in cardiovascular diseases: Potential biomarkers, therapeutic targets and challenges. Acta Pharmacol. Sin. 2018, 39, 1073–1084. [Google Scholar] [CrossRef] [Green Version]
  71. Kingsley, S.M.K.; Bhat, B.V. Role of microRNAs in sepsis. Inflamm. Res. 2017, 66, 553–569. [Google Scholar] [CrossRef] [PubMed]
  72. Taylor, C.R. Introduction to Predictive Biomarkers: Definitions and Characteristics. In Predictive Biomarkers in Oncology: Applications in Precision Medicine; Badve, S., Kumar, G.L., Eds.; Springer International Publishing: Cham, Switzerland, 2019; pp. 3–18. [Google Scholar] [CrossRef]
  73. Mandel, P.; Metais, P. Les acides nucleiques du plasma sanguin chez l’homme. C R Seances Soc. Biol. Fil. 1948, 142, 241–243. [Google Scholar] [PubMed]
  74. Bendich, A.; Wilczok, T.; Borenfreund, E. Circulating DNA as a possible factor in oncogenesis. Science 1965, 148, 374–376. [Google Scholar] [CrossRef] [PubMed]
  75. Tan, E.M.; Schur, P.H.; Carr, R.I.; Kunkel, H.G. Deoxybonucleic acid (DNA) and antibodies to DNA in the serum of patients with systemic lupus erythematosus. J. Clin. Investig. 1966, 45, 1732–1740. [Google Scholar] [CrossRef] [PubMed]
  76. Kamm, R.C.; Smith, A.G. Nucleic acid concentrations in normal human plasma. Clin. Chem. 1972, 18, 519–522. [Google Scholar] [CrossRef] [PubMed]
  77. Stroun, M.; Anker, P.; Maurice, P.; Gahan, P.B. Circulating Nucleic Acids in Higher Organisms11This work was supported by the Ligue Suisse contre le Cancer, the O. J. Isvet Fund, and a grant from Hoffmann-La Roche. In International Review of Cytology; Bourne, G.H., Danielli, J.F., Jeon, K.W., Eds.; Academic Press: Cambridge, MA, USA, 1977; Volume 51, pp. 1–48. [Google Scholar]
  78. El-Hefnawy, T.; Raja, S.; Kelly, L.; Bigbee, W.L.; Kirkwood, J.M.; Luketich, J.D.; Godfrey, T.E. Characterization of amplifiable, circulating RNA in plasma and its potential as a tool for cancer diagnostics. Clin. Chem. 2004, 50, 564–573. [Google Scholar] [CrossRef]
  79. Chen, X.Q.; Bonnefoi, H.; Pelte, M.F.; Lyautey, J.; Lederrey, C.; Movarekhi, S.; Schaeffer, P.; Mulcahy, H.E.; Meyer, P.; Stroun, M.; et al. Telomerase RNA as a detection marker in the serum of breast cancer patients. Clin. Cancer Res. 2000, 6, 3823–3826. [Google Scholar]
  80. Hasselmann, D.O.; Rappl, G.; Rossler, M.; Ugurel, S.; Tilgen, W.; Reinhold, U. Detection of tumor-associated circulating mRNA in serum, plasma and blood cells from patients with disseminated malignant melanoma. Oncol. Rep. 2001, 8, 115–118. [Google Scholar] [CrossRef]
  81. Anker, P.; Lefort, F.; Vasioukhin, V.; Lyautey, J.; Lederrey, C.; Chen, X.Q.; Stroun, M.; Mulcahy, H.E.; Farthing, M.J. K-ras mutations are found in DNA extracted from the plasma of patients with colorectal cancer. Gastroenterology 1997, 112, 1114–1120. [Google Scholar] [CrossRef]
  82. Kamm, R.C.; Smith, A.G. Ribonuclease activity in human plasma. Clin. Biochem. 1972, 5, 198–200. [Google Scholar] [CrossRef]
  83. Xi, Y.; Nakajima, G.; Gavin, E.; Morris, C.G.; Kudo, K.; Hayashi, K.; Ju, J. Systematic analysis of microRNA expression of RNA extracted from fresh frozen and formalin-fixed paraffin-embedded samples. RNA 2007, 13, 1668–1674. [Google Scholar] [CrossRef] [Green Version]
  84. Lawrie, C.H.; Gal, S.; Dunlop, H.M.; Pushkaran, B.; Liggins, A.P.; Pulford, K.; Banham, A.H.; Pezzella, F.; Boultwood, J.; Wainscoat, J.S.; et al. Detection of elevated levels of tumour-associated microRNAs in serum of patients with diffuse large B-cell lymphoma. Br. J. Haematol. 2008, 141, 672–675. [Google Scholar] [CrossRef]
  85. Mitchell, P.S.; Parkin, R.K.; Kroh, E.M.; Fritz, B.R.; Wyman, S.K.; Pogosova-Agadjanyan, E.L.; Peterson, A.; Noteboom, J.; O’Briant, K.C.; Allen, A.; et al. Circulating microRNAs as stable blood-based markers for cancer detection. Proc. Natl. Acad. Sci. USA 2008, 105, 10513–10518. [Google Scholar] [CrossRef] [Green Version]
  86. Lan, H.; Lu, H.; Wang, X.; Jin, H. MicroRNAs as potential biomarkers in cancer: Opportunities and challenges. Biomed. Res. Int. 2015, 2015, 125094. [Google Scholar] [CrossRef] [Green Version]
  87. Acunzo, M.; Romano, G.; Wernicke, D.; Croce, C.M. MicroRNA and cancer—A brief overview. Adv. Biol. Regul. 2015, 57, 1–9. [Google Scholar] [CrossRef]
  88. Solé, C.; Moline, T.; Vidal, M.; Ordi-Ros, J.; Cortés-Hernández, J. An Exosomal Urinary miRNA Signature for Early Diagnosis of Renal Fibrosis in Lupus Nephritis. Cells 2019, 8, 773. [Google Scholar] [CrossRef] [Green Version]
  89. Xiong, D.D.; Lv, J.; Wei, K.L.; Feng, Z.B.; Chen, J.T.; Liu, K.C.; Chen, G.; Luo, D.Z. A nine-miRNA signature as a potential diagnostic marker for breast carcinoma: An integrated study of 1,110 cases. Oncol. Rep. 2017, 37, 3297–3304. [Google Scholar] [CrossRef] [Green Version]
  90. Zhang, J.; Zhao, H.; Gao, Y.; Zhang, W. Secretory miRNAs as novel cancer biomarkers. Biochim. Biophys. Acta 2012, 1826, 32–43. [Google Scholar] [CrossRef]
  91. Gillespie, P.; Ladame, S.; O’Hare, D. Molecular methods in electrochemical microRNA detection. Analyst 2018, 144, 114–129. [Google Scholar] [CrossRef]
  92. Chen, Y.X.; Huang, K.J.; Niu, K.X. Recent advances in signal amplification strategy based on oligonucleotide and nanomaterials for microRNA detection-a review. Biosens. Bioelectron. 2018, 99, 612–624. [Google Scholar] [CrossRef]
  93. Yuan, Y.H.; Chi, B.Z.; Wen, S.H.; Liang, R.P.; Li, Z.M.; Qiu, J.D. Ratiometric electrochemical assay for sensitive detecting microRNA based on dual-amplification mechanism of duplex-specific nuclease and hybridization chain reaction. Biosens. Bioelectron. 2018, 102, 211–216. [Google Scholar] [CrossRef]
  94. Chen, C.; Tan, R.; Wong, L.; Fekete, R.; Halsey, J. Quantitation of microRNAs by real-time RT-qPCR. Methods Mol. Biol. 2011, 687, 113–134. [Google Scholar] [CrossRef]
  95. Chen, C.; Ridzon, D.A.; Broomer, A.J.; Zhou, Z.; Lee, D.H.; Nguyen, J.T.; Barbisin, M.; Xu, N.L.; Mahuvakar, V.R.; Andersen, M.R.; et al. Real-time quantification of microRNAs by stem-loop RT-PCR. Nucleic Acids Res. 2005, 33, e179. [Google Scholar] [CrossRef]
  96. Pacak, A.; Barciszewska-Pacak, M.; Swida-Barteczka, A.; Kruszka, K.; Sega, P.; Milanowska, K.; Jakobsen, I.; Jarmolowski, A.; Szweykowska-Kulinska, Z. Heat Stress Affects Pi-related Genes Expression and Inorganic Phosphate Deposition/Accumulation in Barley. Front. Plant Sci. 2016, 7, 926. [Google Scholar] [CrossRef] [Green Version]
  97. Barciszewska-Pacak, M.; Milanowska, K.; Knop, K.; Bielewicz, D.; Nuc, P.; Plewka, P.; Pacak, A.M.; Vazquez, F.; Karlowski, W.; Jarmolowski, A.; et al. Arabidopsis microRNA expression regulation in a wide range of abiotic stress responses. Front. Plant Sci. 2015, 6, 410. [Google Scholar] [CrossRef] [PubMed]
  98. Kruszka, K.; Pacak, A.; Swida-Barteczka, A.; Nuc, P.; Alaba, S.; Wroblewska, Z.; Karlowski, W.; Jarmolowski, A.; Szweykowska-Kulinska, Z. Transcriptionally and post-transcriptionally regulated microRNAs in heat stress response in barley. J. Exp. Bot. 2014, 65, 6123–6135. [Google Scholar] [CrossRef] [Green Version]
  99. Smoczynska, A.; Sega, P.; Stepien, A.; Knop, K.; Jarmolowski, A.; Pacak, A.; Szweykowska-Kulinska, Z. miRNA Detection by Stem-Loop RT-qPCR in Studying microRNA Biogenesis and microRNA Responsiveness to Abiotic Stresses. Methods Mol. Biol. 2019, 1932, 131–150. [Google Scholar] [CrossRef] [PubMed]
  100. Pall, G.S.; Hamilton, A.J. Improved northern blot method for enhanced detection of small RNA. Nat. Protoc. 2008, 3, 1077–1084. [Google Scholar] [CrossRef]
  101. Wang, X.; Tong, Y.; Wang, S. Rapid and accurate detection of plant miRNAs by liquid northern hybridization. Int. J. Mol. Sci. 2010, 11, 3138–3148. [Google Scholar] [CrossRef] [Green Version]
  102. Varallyay, E.; Burgyan, J.; Havelda, Z. Detection of microRNAs by Northern blot analyses using LNA probes. Methods 2007, 43, 140–145. [Google Scholar] [CrossRef]
  103. Javelle, M.; Timmermans, M.C. In situ localization of small RNAs in plants by using LNA probes. Nat. Protoc. 2012, 7, 533–541. [Google Scholar] [CrossRef] [PubMed]
  104. Bray, F.; Ferlay, J.; Soerjomataram, I.; Siegel, R.L.; Torre, L.A.; Jemal, A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 2018, 68, 394–424. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  105. He, Y.; Lin, J.; Kong, D.; Huang, M.; Xu, C.; Kim, T.K.; Etheridge, A.; Luo, Y.; Ding, Y.; Wang, K. Current State of Circulating MicroRNAs as Cancer Biomarkers. Clin. Chem. 2015, 61, 1138–1155. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  106. Chen, M.; Calin, G.A.; Meng, Q.H. Chapter Five—Circulating microRNAs as Promising Tumor Biomarkers. In Advances in Clinical Chemistry; Makowski, G.S., Ed.; Elsevier: Amsterdam, The Netherlands, 2014; Volume 67, pp. 189–214. [Google Scholar]
  107. Ghai, V.; Wang, K. Recent progress toward the use of circulating microRNAs as clinical biomarkers. Arch. Toxicol. 2016, 90, 2959–2978. [Google Scholar] [CrossRef]
  108. Schwarzenbach, H.; Nishida, N.; Calin, G.A.; Pantel, K. Clinical relevance of circulating cell-free microRNAs in cancer. Nat. Rev. Clin. Oncol. 2014, 11, 145–156. [Google Scholar] [CrossRef]
  109. Armand-Labit, V.; Pradines, A. Circulating cell-free microRNAs as clinical cancer biomarkers. Biomol. Concepts 2017, 8, 61–81. [Google Scholar] [CrossRef] [PubMed]
  110. He, L.; Thomson, J.M.; Hemann, M.T.; Hernando-Monge, E.; Mu, D.; Goodson, S.; Powers, S.; Cordon-Cardo, C.; Lowe, S.W.; Hannon, G.J.; et al. A microRNA polycistron as a potential human oncogene. Nature 2005, 435, 828–833. [Google Scholar] [CrossRef]
  111. Baffa, R.; Fassan, M.; Volinia, S.; O’Hara, B.; Liu, C.G.; Palazzo, J.P.; Gardiman, M.; Rugge, M.; Gomella, L.G.; Croce, C.M.; et al. MicroRNA expression profiling of human metastatic cancers identifies cancer gene targets. J. Pathol. 2009, 219, 214–221. [Google Scholar] [CrossRef]
  112. Di Leva, G.; Croce, C.M. Roles of small RNAs in tumor formation. Trends Mol. Med. 2010, 16, 257–267. [Google Scholar] [CrossRef] [Green Version]
  113. Ryu, J.K.; Hong, S.M.; Karikari, C.A.; Hruban, R.H.; Goggins, M.G.; Maitra, A. Aberrant MicroRNA-155 expression is an early event in the multistep progression of pancreatic adenocarcinoma. Pancreatology 2010, 10, 66–73. [Google Scholar] [CrossRef] [Green Version]
  114. Kluiver, J.; Poppema, S.; de Jong, D.; Blokzijl, T.; Harms, G.; Jacobs, S.; Kroesen, B.J.; van den Berg, A. BIC and miR-155 are highly expressed in Hodgkin, primary mediastinal and diffuse large B cell lymphomas. J. Pathol. 2005, 207, 243–249. [Google Scholar] [CrossRef]
  115. Qin, W.; Ren, Q.; Liu, T.; Huang, Y.; Wang, J. MicroRNA-155 is a novel suppressor of ovarian cancer-initiating cells that targets CLDN1. FEBS Lett. 2013, 587, 1434–1439. [Google Scholar] [CrossRef] [Green Version]
  116. Li, C.L.; Nie, H.; Wang, M.; Su, L.P.; Li, J.F.; Yu, Y.Y.; Yan, M.; Qu, Q.L.; Zhu, Z.G.; Liu, B.Y. microRNA-155 is downregulated in gastric cancer cells and involved in cell metastasis. Oncol. Rep. 2012, 27, 1960–1966. [Google Scholar] [CrossRef] [Green Version]
  117. Levati, L.; Pagani, E.; Romani, S.; Castiglia, D.; Piccinni, E.; Covaciu, C.; Caporaso, P.; Bondanza, S.; Antonetti, F.R.; Bonmassar, E.; et al. MicroRNA-155 targets the SKI gene in human melanoma cell lines. Pigment Cell Melanoma Res. 2011, 24, 538–550. [Google Scholar] [CrossRef]
  118. Calin, G.A.; Sevignani, C.; Dumitru, C.D.; Hyslop, T.; Noch, E.; Yendamuri, S.; Shimizu, M.; Rattan, S.; Bullrich, F.; Negrini, M.; et al. Human microRNA genes are frequently located at fragile sites and genomic regions involved in cancers. Proc. Natl. Acad. Sci. USA 2004, 101, 2999–3004. [Google Scholar] [CrossRef] [Green Version]
  119. Lin, S.; Gregory, R.I. MicroRNA biogenesis pathways in cancer. Nat. Rev. Cancer 2015, 15, 321–333. [Google Scholar] [CrossRef]
  120. Catto, J.W.; Miah, S.; Owen, H.C.; Bryant, H.; Myers, K.; Dudziec, E.; Larre, S.; Milo, M.; Rehman, I.; Rosario, D.J.; et al. Distinct microRNA alterations characterize high- and low-grade bladder cancer. Cancer Res. 2009, 69, 8472–8481. [Google Scholar] [CrossRef] [Green Version]
  121. Merritt, W.M.; Lin, Y.G.; Han, L.Y.; Kamat, A.A.; Spannuth, W.A.; Schmandt, R.; Urbauer, D.; Pennacchio, L.A.; Cheng, J.F.; Nick, A.M.; et al. Dicer, Drosha, and outcomes in patients with ovarian cancer. N. Engl. J. Med. 2008, 359, 2641–2650. [Google Scholar] [CrossRef] [Green Version]
  122. Tchernitsa, O.; Kasajima, A.; Schafer, R.; Kuban, R.J.; Ungethum, U.; Gyorffy, B.; Neumann, U.; Simon, E.; Weichert, W.; Ebert, M.P.; et al. Systematic evaluation of the miRNA-ome and its downstream effects on mRNA expression identifies gastric cancer progression. J. Pathol. 2010, 222, 310–319. [Google Scholar] [CrossRef]
  123. Muralidhar, B.; Winder, D.; Murray, M.; Palmer, R.; Barbosa-Morais, N.; Saini, H.; Roberts, I.; Pett, M.; Coleman, N. Functional evidence that Drosha overexpression in cervical squamous cell carcinoma affects cell phenotype and microRNA profiles. J. Pathol. 2011, 224, 496–507. [Google Scholar] [CrossRef]
  124. Iorio, M.V.; Ferracin, M.; Liu, C.G.; Veronese, A.; Spizzo, R.; Sabbioni, S.; Magri, E.; Pedriali, M.; Fabbri, M.; Campiglio, M.; et al. MicroRNA gene expression deregulation in human breast cancer. Cancer Res. 2005, 65, 7065–7070. [Google Scholar] [CrossRef] [Green Version]
  125. Ma, L.; Teruya-Feldstein, J.; Weinberg, R.A. Tumour invasion and metastasis initiated by microRNA-10b in breast cancer. Nature 2007, 449, 682–688. [Google Scholar] [CrossRef]
  126. Heneghan, H.M.; Miller, N.; Lowery, A.J.; Sweeney, K.J.; Newell, J.; Kerin, M.J. Circulating microRNAs as novel minimally invasive biomarkers for breast cancer. Ann. Surg. 2010, 251, 499–505. [Google Scholar] [CrossRef]
  127. Milevskiy, M.J.G.; Gujral, U.; Del Lama Marques, C.; Stone, A.; Northwood, K.; Burke, L.J.; Gee, J.M.W.; Nephew, K.; Clark, S.; Brown, M.A. MicroRNA-196a is regulated by ER and is a prognostic biomarker in ER+ breast cancer. Br. J. Cancer 2019, 120, 621–632. [Google Scholar] [CrossRef] [Green Version]
  128. Hui, A.B.; Shi, W.; Boutros, P.C.; Miller, N.; Pintilie, M.; Fyles, T.; McCready, D.; Wong, D.; Gerster, K.; Waldron, L.; et al. Robust global micro-RNA profiling with formalin-fixed paraffin-embedded breast cancer tissues. Lab. Investig. 2009, 89, 597–606. [Google Scholar] [CrossRef]
  129. Hoffman, A.E.; Zheng, T.; Yi, C.; Leaderer, D.; Weidhaas, J.; Slack, F.; Zhang, Y.; Paranjape, T.; Zhu, Y. microRNA miR-196a-2 and breast cancer: A genetic and epigenetic association study and functional analysis. Cancer Res. 2009, 69, 5970–5977. [Google Scholar] [CrossRef] [Green Version]
  130. Hanahan, D.; Weinberg, R.A. Hallmarks of cancer: The next generation. Cell 2011, 144, 646–674. [Google Scholar] [CrossRef] [Green Version]
  131. Foulkes, W.D.; Smith, I.E.; Reis-Filho, J.S. Triple-negative breast cancer. N. Engl. J. Med. 2010, 363, 1938–1948. [Google Scholar] [CrossRef] [Green Version]
  132. Iorio, M.V.; Visone, R.; Di Leva, G.; Donati, V.; Petrocca, F.; Casalini, P.; Taccioli, C.; Volinia, S.; Liu, C.G.; Alder, H.; et al. MicroRNA signatures in human ovarian cancer. Cancer Res. 2007, 67, 8699–8707. [Google Scholar] [CrossRef] [Green Version]
  133. Kan, C.W.; Hahn, M.A.; Gard, G.B.; Maidens, J.; Huh, J.Y.; Marsh, D.J.; Howell, V.M. Elevated levels of circulating microRNA-200 family members correlate with serous epithelial ovarian cancer. BMC Cancer 2012, 12, 627. [Google Scholar] [CrossRef] [Green Version]
  134. Chen, S.N.; Chang, R.; Lin, L.T.; Chern, C.U.; Tsai, H.W.; Wen, Z.H.; Li, Y.H.; Li, C.J.; Tsui, K.H. MicroRNA in Ovarian Cancer: Biology, Pathogenesis, and Therapeutic Opportunities. Int. J. Environ. Res. Public Health 2019, 16, 1510. [Google Scholar] [CrossRef] [Green Version]
  135. Hu, X.; Macdonald, D.M.; Huettner, P.C.; Feng, Z.; El Naqa, I.M.; Schwarz, J.K.; Mutch, D.G.; Grigsby, P.W.; Powell, S.N.; Wang, X. A miR-200 microRNA cluster as prognostic marker in advanced ovarian cancer. Gynecol. Oncol. 2009, 114, 457–464. [Google Scholar] [CrossRef]
  136. Yang, D.; Sun, Y.; Hu, L.; Zheng, H.; Ji, P.; Pecot, C.V.; Zhao, Y.; Reynolds, S.; Cheng, H.; Rupaimoole, R.; et al. Integrated analyses identify a master microRNA regulatory network for the mesenchymal subtype in serous ovarian cancer. Cancer Cell 2013, 23, 186–199. [Google Scholar] [CrossRef] [Green Version]
  137. Lu, L.; Schwartz, P.; Scarampi, L.; Rutherford, T.; Canuto, E.M.; Yu, H.; Katsaros, D. MicroRNA let-7a: A potential marker for selection of paclitaxel in ovarian cancer management. Gynecol. Oncol. 2011, 122, 366–371. [Google Scholar] [CrossRef]
  138. Bussing, I.; Slack, F.J.; Grosshans, H. let-7 microRNAs in development, stem cells and cancer. Trends Mol. Med. 2008, 14, 400–409. [Google Scholar] [CrossRef]
  139. Li, J.; Liang, S.H.; Lu, X. Potential role of ezrin and its related microRNA in ovarian cancer invasion and metastasis. Zhonghua Fu Chan Ke Za Zhi 2010, 45, 787–792. [Google Scholar]
  140. He, Z.; Xu, H.; Meng, Y.; Kuang, Y. miR-944 acts as a prognostic marker and promotes the tumor progression in endometrial cancer. Biomed. Pharmacother. 2017, 88, 902–910. [Google Scholar] [CrossRef]
  141. He, H.; Tian, W.; Chen, H.; Jiang, K. MiR-944 functions as a novel oncogene and regulates the chemoresistance in breast cancer. Tumour. Biol. 2016, 37, 1599–1607. [Google Scholar] [CrossRef]
  142. Park, S.; Kim, J.; Eom, K.; Oh, S.; Kim, S.; Kim, G.; Ahn, S.; Park, K.H.; Chung, D.; Lee, H. microRNA-944 overexpression is a biomarker for poor prognosis of advanced cervical cancer. BMC Cancer 2019, 19, 419. [Google Scholar] [CrossRef] [Green Version]
  143. Zhou, N.; Fei, D.; Zong, S.; Zhang, M.; Yue, Y. MicroRNA-138 inhibits proliferation, migration and invasion through targeting hTERT in cervical cancer. Oncol. Lett. 2016, 12, 3633–3639. [Google Scholar] [CrossRef]
  144. Li, H.; Sheng, Y.; Zhang, Y.; Gao, N.; Deng, X.; Sheng, X. MicroRNA-138 is a potential biomarker and tumor suppressor in human cervical carcinoma by reversely correlated with TCF3 gene. Gynecol. Oncol. 2017, 145, 569–576. [Google Scholar] [CrossRef] [PubMed]
  145. Hasanzadeh, M.; Movahedi, M.; Rejali, M.; Maleki, F.; Moetamani-Ahmadi, M.; Seifi, S.; Hosseini, Z.; Khazaei, M.; Amerizadeh, F.; Ferns, G.A.; et al. The potential prognostic and therapeutic application of tissue and circulating microRNAs in cervical cancer. J. Cell. Physiol. 2019, 234, 1289–1294. [Google Scholar] [CrossRef] [PubMed]
  146. Tao, Z.; Shi, A.; Lu, C.; Song, T.; Zhang, Z.; Zhao, J. Breast Cancer: Epidemiology and Etiology. Cell Biochem. Biophys. 2015, 72, 333–338. [Google Scholar] [CrossRef] [PubMed]
  147. Dai, X.; Li, T.; Bai, Z.; Yang, Y.; Liu, X.; Zhan, J.; Shi, B. Breast cancer intrinsic subtype classification, clinical use and future trends. Am. J. Cancer Res. 2015, 5, 2929–2943. [Google Scholar]
  148. O’Bryan, S.; Dong, S.; Mathis, J.M.; Alahari, S.K. The roles of oncogenic miRNAs and their therapeutic importance in breast cancer. Eur. J. Cancer 2017, 72, 1–11. [Google Scholar] [CrossRef]
  149. Uhr, K.; Prager-van der Smissen, W.J.C.; Heine, A.A.J.; Ozturk, B.; van Jaarsveld, M.T.M.; Boersma, A.W.M.; Jager, A.; Wiemer, E.A.C.; Smid, M.; Foekens, J.A.; et al. MicroRNAs as possible indicators of drug sensitivity in breast cancer cell lines. PLoS ONE 2019, 14, e0216400. [Google Scholar] [CrossRef] [Green Version]
  150. Jayson, G.C.; Kohn, E.C.; Kitchener, H.C.; Ledermann, J.A. Ovarian cancer. Lancet 2014, 384, 1376–1388. [Google Scholar] [CrossRef]
  151. Matulonis, U.A.; Sood, A.K.; Fallowfield, L.; Howitt, B.E.; Sehouli, J.; Karlan, B.Y. Ovarian cancer. Nat. Rev. Dis. Primers 2016, 2, 16061. [Google Scholar] [CrossRef]
  152. Reid, B.M.; Permuth, J.B.; Sellers, T.A. Epidemiology of ovarian cancer: A review. Cancer Biol. Med. 2017, 14, 9–32. [Google Scholar] [CrossRef] [Green Version]
  153. Chien, J.; Poole, E.M. Ovarian Cancer Prevention, Screening, and Early Detection: Report From the 11th Biennial Ovarian Cancer Research Symposium. Int. J. Gynecol. Cancer 2017, 27, S20–S22. [Google Scholar] [CrossRef]
  154. Hausler, S.F.; Keller, A.; Chandran, P.A.; Ziegler, K.; Zipp, K.; Heuer, S.; Krockenberger, M.; Engel, J.B.; Honig, A.; Scheffler, M.; et al. Whole blood-derived miRNA profiles as potential new tools for ovarian cancer screening. Br. J. Cancer 2010, 103, 693–700. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  155. Pecot, C.V.; Rupaimoole, R.; Yang, D.; Akbani, R.; Ivan, C.; Lu, C.; Wu, S.; Han, H.D.; Shah, M.Y.; Rodriguez-Aguayo, C.; et al. Tumour angiogenesis regulation by the miR-200 family. Nat. Commun. 2013, 4, 2427. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  156. Cortez, M.A.; Welsh, J.W.; Calin, G.A. Circulating microRNAs as noninvasive biomarkers in breast cancer. Recent Results Cancer Res. 2012, 195, 151–161. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  157. Ohno, S.; Takanashi, M.; Sudo, K.; Ueda, S.; Ishikawa, A.; Matsuyama, N.; Fujita, K.; Mizutani, T.; Ohgi, T.; Ochiya, T.; et al. Systemically injected exosomes targeted to EGFR deliver antitumor microRNA to breast cancer cells. Mol. Ther. 2013, 21, 185–191. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  158. Wang, X.M.; Xu, J.; Cheng, Z.Q.; Peng, Q.Z.; Hu, J.T.; Gao, L.K.; Zhang, S.F.; Jin, H.T. Study on effects of microRNA-21 antisense oligonucleotide in vivo and in vitro on bionomics of human cervical squamous carcinoma cell lines SiHa. Zhonghua Bing Li Xue Za Zhi 2012, 41, 254–259. [Google Scholar] [CrossRef]
  159. Flores-Perez, A.; Marchat, L.A.; Rodriguez-Cuevas, S.; Bautista, V.P.; Fuentes-Mera, L.; Romero-Zamora, D.; Maciel-Dominguez, A.; de la Cruz, O.H.; Fonseca-Sanchez, M.; Ruiz-Garcia, E.; et al. Suppression of cell migration is promoted by miR-944 through targeting of SIAH1 and PTP4A1 in breast cancer cells. BMC Cancer 2016, 16, 379. [Google Scholar] [CrossRef] [Green Version]
  160. Pan, T.; Chen, W.; Yuan, X.; Shen, J.; Qin, C.; Wang, L. miR-944 inhibits metastasis of gastric cancer by preventing the epithelial-mesenchymal transition via MACC1/Met/AKT signaling. FEBS Open Bio 2017, 7, 905–914. [Google Scholar] [CrossRef]
  161. Wen, L.; Li, Y.; Jiang, Z.; Zhang, Y.; Yang, B.; Han, F. miR-944 inhibits cell migration and invasion by targeting MACC1 in colorectal cancer. Oncol. Rep. 2017, 37, 3415–3422. [Google Scholar] [CrossRef]
  162. Mou, Z.; Xu, X.; Dong, M.; Xu, J. MicroRNA-148b Acts as a Tumor Suppressor in Cervical Cancer by Inducing G1/S-Phase Cell Cycle Arrest and Apoptosis in a Caspase-3-Dependent Manner. Med. Sci. Monit. 2016, 22, 2809–2815. [Google Scholar] [CrossRef] [Green Version]
  163. Song, R.; Cong, L.; Ni, G.; Chen, M.; Sun, H.; Sun, Y.; Chen, M. MicroRNA-195 inhibits the behavior of cervical cancer tumors by directly targeting HDGF. Oncol. Lett. 2017, 14, 767–775. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  164. Wang, J.M.; Ju, B.H.; Pan, C.J.; Gu, Y.; Li, M.Q.; Sun, L.; Xu, Y.Y.; Yin, L.R. MiR-214 inhibits cell migration, invasion and promotes the drug sensitivity in human cervical cancer by targeting FOXM1. Am. J. Transl. Res. 2017, 9, 3541–3557. [Google Scholar] [PubMed]
  165. Chen, A.H.; Qin, Y.E.; Tang, W.F.; Tao, J.; Song, H.M.; Zuo, M. MiR-34a and miR-206 act as novel prognostic and therapy biomarkers in cervical cancer. Cancer Cell Int. 2017, 17, 63. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  166. Magee, P.; Shi, L.; Garofalo, M. Role of microRNAs in chemoresistance. Ann. Transl. Med. 2015, 3, 332. [Google Scholar] [CrossRef] [PubMed]
  167. Ma, J.; Dong, C.; Ji, C. MicroRNA and drug resistance. Cancer Gene Ther. 2010, 17, 523–531. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  168. Yu, L.; Xiong, J.; Guo, L.; Miao, L.; Liu, S.; Guo, F. The effects of lanthanum chloride on proliferation and apoptosis of cervical cancer cells: Involvement of let-7a and miR-34a microRNAs. Biometals 2015, 28, 879–890. [Google Scholar] [CrossRef] [PubMed]
  169. Shen, Y.; Wang, P.; Li, Y.; Ye, F.; Wang, F.; Wan, X.; Cheng, X.; Lu, W.; Xie, X. miR-375 is upregulated in acquired paclitaxel resistance in cervical cancer. Br. J. Cancer 2013, 109, 92–99. [Google Scholar] [CrossRef] [Green Version]
  170. Yang, Y.; Liu, H.; Wang, X.; Chen, L. Up-regulation of microRNA-664 inhibits cell growth and increases cisplatin sensitivity in cervical cancer. Int. J. Clin. Exp. Med. 2015, 8, 18123–18129. [Google Scholar]
  171. Shen, Y.; Zhou, J.; Li, Y.; Ye, F.; Wan, X.; Lu, W.; Xie, X.; Cheng, X. miR-375 mediated acquired chemo-resistance in cervical cancer by facilitating EMT. PLoS ONE 2014, 9, e109299. [Google Scholar] [CrossRef]
  172. Bhaskaran, M.; Mohan, M. MicroRNAs: History, biogenesis, and their evolving role in animal development and disease. Vet. Pathol. 2014, 51, 759–774. [Google Scholar] [CrossRef] [Green Version]
  173. Wang, H.; Peng, R.; Wang, J.; Qin, Z.; Xue, L. Circulating microRNAs as potential cancer biomarkers: The advantage and disadvantage. Clin. Epigenetics 2018, 10, 59. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  174. Zhang, H.; Mao, F.; Shen, T.; Luo, Q.; Ding, Z.; Qian, L.; Huang, J. Plasma miR-145, miR-20a, miR-21 and miR-223 as novel biomarkers for screening early-stage non-small cell lung cancer. Oncol. Lett. 2017, 13, 669–676. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  175. Arab, A.; Karimipoor, M.; Irani, S.; Kiani, A.; Zeinali, S.; Tafsiri, E.; Sheikhy, K. Potential circulating miRNA signature for early detection of NSCLC. Cancer Genet. 2017, 216–217, 150–158. [Google Scholar] [CrossRef] [PubMed]
  176. Lemjabbar-Alaoui, H.; Hassan, O.U.; Yang, Y.-W.; Buchanan, P. Lung cancer: Biology and treatment options. Biochim. Biophys. Acta 2015, 1856, 189–210. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  177. Hamamoto, J.; Soejima, K.; Yoda, S.; Naoki, K.; Nakayama, S.; Satomi, R.; Terai, H.; Ikemura, S.; Sato, T.; Yasuda, H.; et al. Identification of microRNAs differentially expressed between lung squamous cell carcinoma and lung adenocarcinoma. Mol. Med. Rep. 2013, 8, 456–462. [Google Scholar] [CrossRef] [PubMed]
  178. Solomides, C.C.; Evans, B.J.; Navenot, J.M.; Vadigepalli, R.; Peiper, S.C.; Wang, Z.X. MicroRNA profiling in lung cancer reveals new molecular markers for diagnosis. Acta Cytol. 2012, 56, 645–654. [Google Scholar] [CrossRef] [PubMed]
  179. Li, J.-H.; Sun, S.-S.; Li, N.; Lv, P.; Xie, S.-Y.; Wang, P.-Y. MiR-205 as a promising biomarker in the diagnosis and prognosis of lung cancer. Oncotarget 2017, 8, 91938–91949. [Google Scholar] [CrossRef] [Green Version]
  180. Jin, X.; Chen, Y.; Chen, H.; Fei, S.; Chen, D.; Cai, X.; Liu, L.; Lin, B.; Su, H.; Zhao, L.; et al. Evaluation of Tumor-Derived Exosomal miRNA as Potential Diagnostic Biomarkers for Early-Stage Non-Small Cell Lung Cancer Using Next-Generation Sequencing. Clin. Cancer Res. 2017, 23, 5311–5319. [Google Scholar] [CrossRef] [Green Version]
  181. Powrozek, T.; Kuznar-Kaminska, B.; Dziedzic, M.; Mlak, R.; Batura-Gabryel, H.; Sagan, D.; Krawczyk, P.; Milanowski, J.; Malecka-Massalska, T. The diagnostic role of plasma circulating precursors of miRNA-944 and miRNA-3662 for non-small cell lung cancer detection. Pathol. Res. Pract. 2017, 213, 1384–1387. [Google Scholar] [CrossRef]
  182. Nishikawa, E.; Osada, H.; Okazaki, Y.; Arima, C.; Tomida, S.; Tatematsu, Y.; Taguchi, A.; Shimada, Y.; Yanagisawa, K.; Yatabe, Y.; et al. miR-375 is Activated by ASH1 and Inhibits YAP1 in a Lineage-Dependent Manner in Lung Cancer. Cancer Res. 2011, 71, 6165–6173. [Google Scholar] [CrossRef] [Green Version]
  183. Poroyko, V.; Mirzapoiazova, T.; Nam, A.; Mambetsariev, I.; Mambetsariev, B.; Wu, X.; Husain, A.; Vokes, E.E.; Wheeler, D.L.; Salgia, R. Exosomal miRNAs species in the blood of small cell and non-small cell lung cancer patients. Oncotarget 2018, 9, 19793–19806. [Google Scholar] [CrossRef] [Green Version]
  184. Turashvili, G.; Brogi, E. Tumor Heterogeneity in Breast Cancer. Front. Med. 2017, 4, 227. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  185. Ellsworth, R.E.; Blackburn, H.L.; Shriver, C.D.; Soon-Shiong, P.; Ellsworth, D.L. Molecular heterogeneity in breast cancer: State of the science and implications for patient care. Semin. Cell Dev. Biol. 2017, 64, 65–72. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  186. Harris, L.N.; Ismaila, N.; McShane, L.M.; Andre, F.; Collyar, D.E.; Gonzalez-Angulo, A.M.; Hammond, E.H.; Kuderer, N.M.; Liu, M.C.; Mennel, R.G.; et al. Use of Biomarkers to Guide Decisions on Adjuvant Systemic Therapy for Women With Early-Stage Invasive Breast Cancer: American Society of Clinical Oncology Clinical Practice Guideline. J. Clin. Oncol. 2016, 34, 1134–1150. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  187. Effects of chemotherapy and hormonal therapy for early breast cancer on recurrence and 15-year survival: An overview of the randomised trials. Lancet 2005, 365, 1687–1717. [CrossRef]
  188. Shin, V.Y.; Siu, J.M.; Cheuk, I.; Ng, E.K.O.; Kwong, A. Circulating cell-free miRNAs as biomarker for triple-negative breast cancer. Br. J. Cancer 2015, 112, 1751–1759. [Google Scholar] [CrossRef]
  189. Newie, I.; Sokilde, R.; Persson, H.; Grabau, D.; Rego, N.; Kvist, A.; von Stedingk, K.; Axelson, H.; Borg, A.; Vallon-Christersson, J.; et al. The HER2-encoded miR-4728-3p regulates ESR1 through a non-canonical internal seed interaction. PLoS ONE 2014, 9, e97200. [Google Scholar] [CrossRef]
  190. Newie, I.; Sokilde, R.; Persson, H.; Jacomasso, T.; Gorbatenko, A.; Borg, A.; de Hoon, M.; Pedersen, S.F.; Rovira, C. HER2-encoded mir-4728 forms a receptor-independent circuit with miR-21-5p through the non-canonical poly(A) polymerase PAPD5. Sci. Rep. 2016, 6, 35664. [Google Scholar] [CrossRef] [Green Version]
  191. Floros, K.V.; Lochmann, T.L. Coamplification of miR-4728 protects HER2-amplified breast cancers from targeted therapy. Proc. Natl. Acad. Sci. USA 2018, 115, E2594–E2603. [Google Scholar] [CrossRef] [Green Version]
  192. Søkilde, R.; Persson, H.; Ehinger, A.; Pirona, A.C.; Fernö, M.; Hegardt, C.; Larsson, C.; Loman, N.; Malmberg, M.; Rydén, L.; et al. Refinement of breast cancer molecular classification by miRNA expression profiles. BMC Genom. 2019, 20, 503. [Google Scholar] [CrossRef]
  193. Bailey, S.T.; Westerling, T.; Brown, M. Loss of estrogen-regulated microRNA expression increases HER2 signaling and is prognostic of poor outcome in luminal breast cancer. Cancer Res. 2015, 75, 436–445. [Google Scholar] [CrossRef] [Green Version]
  194. Ferracin, M.; Lupini, L.; Salamon, I.; Saccenti, E.; Zanzi, M.V.; Rocchi, A.; Da Ros, L.; Zagatti, B.; Musa, G.; Bassi, C.; et al. Absolute quantification of cell-free microRNAs in cancer patients. Oncotarget 2015, 6, 14545–14555. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  195. Thomas, H.; Diamond, J.; Vieco, A.; Chaudhuri, S.; Shinnar, E.; Cromer, S.; Perel, P.; Mensah, G.A.; Narula, J.; Johnson, C.O.; et al. Global Atlas of Cardiovascular Disease 2000-2016: The Path to Prevention and Control. Global Heart 2018, 13, 143–163. [Google Scholar] [CrossRef] [PubMed]
  196. Aydin, S.; Ugur, K.; Aydin, S.; Sahin, İ.; Yardim, M. Biomarkers in acute myocardial infarction: Current perspectives. Vasc. Health Risk Manag. 2019, 15, 1–10. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  197. Park, K.C.; Gaze, D.C.; Collinson, P.O.; Marber, M.S. Cardiac troponins: From myocardial infarction to chronic disease. Cardiovasc. Res. 2017, 113, 1708–1718. [Google Scholar] [CrossRef] [PubMed]
  198. Patane, S.; Marte, F.; Di Bella, G. Abnormal troponin I levels after supraventricular tachycardia. Int. J. Cardiol. 2009, 132, e57–e59. [Google Scholar] [CrossRef]
  199. Ipek, E.; Demirelli, S.; Ermis, E.; Inci, S. Sarcoidosis and the heart: A review of the literature. Intractable Rare Dis. Res. 2015, 4, 170–180. [Google Scholar] [CrossRef] [Green Version]
  200. Petrovic, D.; Stojimirovic, B.B. Cardiac troponins: Outcome predictors in hemodialysis patients. J. Artif. Organs 2009, 12, 258–263. [Google Scholar] [CrossRef]
  201. Christensen, H.; Johannesen, H.H.; Christensen, A.F.; Bendtzen, K.; Boysen, G. Serum cardiac troponin I in acute stroke is related to serum cortisol and TNF-alpha. Cerebrovasc. Dis. 2004, 18, 194–199. [Google Scholar] [CrossRef]
  202. Shave, R.; George, K.P.; Atkinson, G.; Hart, E.; Middleton, N.; Whyte, G.; Gaze, D.; Collinson, P.O. Exercise-induced cardiac troponin T release: A meta-analysis. Med. Sci. Sports Exerc. 2007, 39, 2099–2106. [Google Scholar] [CrossRef]
  203. Cheng, C.; Wang, Q.; You, W.; Chen, M.; Xia, J. MiRNAs as biomarkers of myocardial infarction: A meta-analysis. PLoS ONE 2014, 9, e88566. [Google Scholar] [CrossRef] [Green Version]
  204. Wang, Q.; Ma, J.; Jiang, Z.; Wu, F.; Ping, J.; Ming, L. Identification of microRNAs as diagnostic biomarkers for acute myocardial infarction in Asian populations: A systematic review and meta-analysis. Medicine 2017, 96, e7173. [Google Scholar] [CrossRef] [PubMed]
  205. Liu, X.; Fan, Z.; Zhao, T.; Cao, W.; Zhang, L.; Li, H.; Xie, Q.; Tian, Y.; Wang, B. Plasma miR-1, miR-208, miR-499 as potential predictive biomarkers for acute myocardial infarction: An independent study of Han population. Exp. Gerontol. 2015, 72, 230–238. [Google Scholar] [CrossRef] [PubMed]
  206. Vechetti, I.J.; Wen, Y.; Chaillou, T.; Murach, K.A.; Alimov, A.P.; Figueiredo, V.C.; Dal-Pai-Silva, M.; McCarthy, J.J. Life-long reduction in myomiR expression does not adversely affect skeletal muscle morphology. Sci. Rep. 2019, 9, 5483. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  207. Chen, J.-F.; Mandel, E.M.; Thomson, J.M.; Wu, Q.; Callis, T.E.; Hammond, S.M.; Conlon, F.L.; Wang, D.-Z. The role of microRNA-1 and microRNA-133 in skeletal muscle proliferation and differentiation. Nat. Genet. 2006, 38, 228–233. [Google Scholar] [CrossRef]
  208. Wang, G.K.; Zhu, J.Q.; Zhang, J.T.; Li, Q.; Li, Y.; He, J.; Qin, Y.W.; Jing, Q. Circulating microRNA: A novel potential biomarker for early diagnosis of acute myocardial infarction in humans. Eur. Heart J. 2010, 31, 659–666. [Google Scholar] [CrossRef]
  209. Callis, T.E.; Pandya, K.; Seok, H.Y.; Tang, R.-H.; Tatsuguchi, M.; Huang, Z.-P.; Chen, J.-F.; Deng, Z.; Gunn, B.; Shumate, J.; et al. MicroRNA-208a is a regulator of cardiac hypertrophy and conduction in mice. J. Clin. Investig. 2009, 119, 2772–2786. [Google Scholar] [CrossRef] [Green Version]
  210. Liu, H.; Yang, N.; Fei, Z.; Qiu, J.; Ma, D.; Liu, X.; Cai, G.; Li, S. Analysis of plasma miR-208a and miR-370 expression levels for early diagnosis of coronary artery disease. Biomed. Rep. 2016, 5, 332–336. [Google Scholar] [CrossRef] [Green Version]
  211. Devaux, Y.; Vausort, M.; Goretti, E.; Nazarov, P.V.; Azuaje, F.; Gilson, G.; Corsten, M.F.; Schroen, B.; Lair, M.L.; Heymans, S.; et al. Use of circulating microRNAs to diagnose acute myocardial infarction. Clin. Chem. 2012, 58, 559–567. [Google Scholar] [CrossRef] [Green Version]
  212. Kondkar, A.A.; Abu-Amero, K.K. Utility of circulating microRNAs as clinical biomarkers for cardiovascular diseases. Biomed. Res. Int. 2015, 2015, 821823. [Google Scholar] [CrossRef] [Green Version]
  213. Eitel, I.; Adams, V.; Dieterich, P.; Fuernau, G.; de Waha, S.; Desch, S.; Schuler, G.; Thiele, H. Relation of circulating MicroRNA-133a concentrations with myocardial damage and clinical prognosis in ST-elevation myocardial infarction. Am. Heart J. 2012, 164, 706–714. [Google Scholar] [CrossRef]
  214. Kuwabara, Y.; Ono, K.; Horie, T.; Nishi, H.; Nagao, K.; Kinoshita, M.; Watanabe, S.; Baba, O.; Kojima, Y.; Shizuta, S.; et al. Increased microRNA-1 and microRNA-133a levels in serum of patients with cardiovascular disease indicate myocardial damage. Circ. Cardiovasc. Genet. 2011, 4, 446–454. [Google Scholar] [CrossRef] [PubMed]
  215. Widera, C.; Gupta, S.K.; Lorenzen, J.M.; Bang, C.; Bauersachs, J.; Bethmann, K.; Kempf, T.; Wollert, K.C.; Thum, T. Diagnostic and prognostic impact of six circulating microRNAs in acute coronary syndrome. J. Mol. Cell. Cardiol. 2011, 51, 872–875. [Google Scholar] [CrossRef] [PubMed]
  216. Xiao, Y.; Zhao, J.; Tuazon, J.P.; Borlongan, C.V.; Yu, G. MicroRNA-133a and Myocardial Infarction. Cell Transplant. 2019, 28, 831–838. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  217. Zhang, Y.; Li, H.H.; Yang, R.; Yang, B.J.; Gao, Z.Y. Association between circulating microRNA-208a and severity of coronary heart disease. Scand. J. Clin. Lab. Investig. 2017, 77, 379–384. [Google Scholar] [CrossRef]
  218. Liu, X.; Yuan, L.; Chen, F.; Zhang, L.; Chen, X.; Yang, C.; Han, Z. Circulating miR-208b: A Potentially Sensitive and Reliable Biomarker for the Diagnosis and Prognosis of Acute Myocardial Infarction. Clin. Lab. 2017, 63, 101–109. [Google Scholar] [CrossRef]
  219. Adachi, T.; Nakanishi, M.; Otsuka, Y.; Nishimura, K.; Hirokawa, G.; Goto, Y.; Nonogi, H.; Iwai, N. Plasma microRNA 499 as a biomarker of acute myocardial infarction. Clin. Chem. 2010, 56, 1183–1185. [Google Scholar] [CrossRef] [Green Version]
  220. Youssef, G.; Shalaby, A.; Ibrahim, A.; Alghobary, H. Detection of Micro RNA-499 in Acute Myocardial Infarction, Significance of a New Marker. Res. J. Pharm. Biol. Chem. Sci. 2017, 8, 1480–1485. [Google Scholar]
  221. Zhao, C.H.; Cheng, G.C.; He, R.L.; Hong, Y.; Wan, Q.L.; Wang, Z.Z.; Pan, Z.Y. Analysis and clinical significance of microRNA-499 expression levels in serum of patients with acute myocardial infarction. Genet. Mol. Res. 2015, 14, 4027–4034. [Google Scholar] [CrossRef]
  222. Singer, M.; Deutschman, C.S.; Seymour, C.W.; Shankar-Hari, M.; Annane, D.; Bauer, M.; Bellomo, R.; Bernard, G.R.; Chiche, J.D.; Coopersmith, C.M.; et al. The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3). JAMA 2016, 315, 801–810. [Google Scholar] [CrossRef]
  223. Kim, H.I.; Park, S. Sepsis: Early Recognition and Optimized Treatment. Tuberc. Respir. Dis. (Seoul) 2019, 82, 6–14. [Google Scholar] [CrossRef]
  224. Benz, F.; Roy, S.; Trautwein, C.; Roderburg, C.; Luedde, T. Circulating MicroRNAs as Biomarkers for Sepsis. Int. J. Mol. Sci. 2016, 17, 78. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  225. Dumache, R.; Rogobete, A.F.; Bedreag, O.H.; Sarandan, M.; Cradigati, A.C.; Papurica, M.; Dumbuleu, C.M.; Nartita, R.; Sandesc, D. Use of miRNAs as biomarkers in sepsis. Anal. Cell. Pathol. 2015, 2015, 186716. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  226. Caserta, S.; Kern, F.; Cohen, J.; Drage, S.; Newbury, S.F.; Llewelyn, M.J. Circulating Plasma microRNAs can differentiate Human Sepsis and Systemic Inflammatory Response Syndrome (SIRS). Sci. Rep. 2016, 6, 28006. [Google Scholar] [CrossRef] [PubMed]
  227. Trancă, S.D.; Petrişor, C.L.; Hagău, N. Biomarkers in polytrauma induced systemic inflammatory response syndrome and sepsis—A narrative review. Rom. J. Anaesth. Intensive Care 2014, 21, 118–122. [Google Scholar] [PubMed]
  228. Margarit, S. Biomarkers of sepsis, a never-ending story. Jurnalul Roman de Anestezie Terapie Intensiva 2014, 21, 83–85. [Google Scholar]
  229. Dolin, H.H.; Papadimos, T.J.; Stepkowski, S.; Chen, X.; Pan, Z.K. A Novel Combination of Biomarkers to Herald the Onset of Sepsis Prior to the Manifestation of Symptoms. Shock 2018, 49, 364–370. [Google Scholar] [CrossRef]
  230. Trung, N.T.; Thau, N.S.; Bang, M.H.; Song, L.H. PCR-based Sepsis@Quick test is superior in comparison with blood culture for identification of sepsis-causative pathogens. Sci. Rep. 2019, 9, 13663. [Google Scholar] [CrossRef] [Green Version]
  231. Yao, L.; Liu, Z.; Zhu, J.; Li, B.; Chai, C.; Tian, Y. Clinical evaluation of circulating microRNA-25 level change in sepsis and its potential relationship with oxidative stress. Int. J. Clin. Exp. Pathol. 2015, 8, 7675–7684. [Google Scholar]
  232. Yao, Y.; Sun, F.; Lei, M. miR-25 inhibits sepsis-induced cardiomyocyte apoptosis by targetting PTEN. Biosci. Rep. 2018, 38, BSR20171511. [Google Scholar] [CrossRef] [Green Version]
  233. Haneklaus, M.; Gerlic, M.; O’Neill, L.A.; Masters, S.L. miR-223: Infection, inflammation and cancer. J. Intern. Med. 2013, 274, 215–226. [Google Scholar] [CrossRef]
  234. Wang, J.F.; Yu, M.L.; Yu, G.; Bian, J.J.; Deng, X.M.; Wan, X.J.; Zhu, K.M. Serum miR-146a and miR-223 as potential new biomarkers for sepsis. Biochem. Biophys. Res. Commun. 2010, 394, 184–188. [Google Scholar] [CrossRef] [PubMed]
  235. Zhou, X.; Li, X.; Wu, M. miRNAs reshape immunity and inflammatory responses in bacterial infection. Signal Transduct. Target. Ther. 2018, 3, 14. [Google Scholar] [CrossRef] [Green Version]
  236. Bandyopadhyay, S.; Long, M.E.; Allen, L.-A.H. Differential expression of microRNAs in Francisella tularensis-infected human macrophages: miR-155-dependent downregulation of MyD88 inhibits the inflammatory response. PLoS ONE 2014, 9, e109525. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  237. Davidson-Moncada, J.; Papavasiliou, F.N.; Tam, W. MicroRNAs of the immune system: Roles in inflammation and cancer. Ann. N. Y. Acad. Sci. 2010, 1183, 183–194. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  238. Vasilescu, C.; Rossi, S.; Shimizu, M.; Tudor, S.; Veronese, A.; Ferracin, M.; Nicoloso, M.S.; Barbarotto, E.; Popa, M.; Stanciulea, O.; et al. MicroRNA fingerprints identify miR-150 as a plasma prognostic marker in patients with sepsis. PLoS ONE 2009, 4, e7405. [Google Scholar] [CrossRef]
  239. Ma, Y.; Vilanova, D.; Atalar, K.; Delfour, O.; Edgeworth, J.; Ostermann, M.; Hernandez-Fuentes, M.; Razafimahatratra, S.; Michot, B.; Persing, D.H.; et al. Genome-wide sequencing of cellular microRNAs identifies a combinatorial expression signature diagnostic of sepsis. PLoS ONE 2013, 8, e75918. [Google Scholar] [CrossRef] [Green Version]
  240. Roderburg, C.; Luedde, M.; Vargas Cardenas, D.; Vucur, M.; Scholten, D.; Frey, N.; Koch, A.; Trautwein, C.; Tacke, F.; Luedde, T. Circulating microRNA-150 serum levels predict survival in patients with critical illness and sepsis. PLoS ONE 2013, 8, e54612. [Google Scholar] [CrossRef] [Green Version]
  241. Wu, S.-C.; Yang, J.C.-S.; Rau, C.-S.; Chen, Y.-C.; Lu, T.-H.; Lin, M.-W.; Tzeng, S.-L.; Wu, Y.-C.; Wu, C.-J.; Hsieh, C.-H. Profiling circulating microRNA expression in experimental sepsis using cecal ligation and puncture. PLoS ONE 2013, 8, e77936. [Google Scholar] [CrossRef] [Green Version]
  242. Poore, G.D.; Ko, E.R.; Valente, A.; Henao, R.; Sumner, K.; Hong, C.; Burke, T.W.; Nichols, M.; McClain, M.T.; Huang, E.S.; et al. A miRNA Host Response Signature Accurately Discriminates Acute Respiratory Infection Etiologies. Front. Microbiol. 2018, 9, 2957. [Google Scholar] [CrossRef]
  243. Mannala, G.K.; Izar, B.; Rupp, O.; Schultze, T.; Goesmann, A.; Chakraborty, T.; Hain, T. Listeria monocytogenes Induces a Virulence-Dependent microRNA Signature That Regulates the Immune Response in Galleria mellonella. Front. Microbiol. 2017, 8, 2463. [Google Scholar] [CrossRef] [Green Version]
  244. Furci, L.; Schena, E.; Miotto, P.; Cirillo, D.M. Alteration of human macrophages microRNA expression profile upon infection with Mycobacterium tuberculosis. Int. J. Mycobacteriol. 2013, 2, 128–134. [Google Scholar] [CrossRef] [Green Version]
  245. Zheng, K.; Chen, D.-S.; Wu, Y.-Q.; Xu, X.-J.; Zhang, H.; Chen, C.-F.; Chen, H.-C.; Liu, Z.-F. MicroRNA expression profile in RAW264.7 cells in response to Brucella melitensis infection. Int. J. Biol. Sci. 2012, 8, 1013–1022. [Google Scholar] [CrossRef] [Green Version]
  246. Mun, J.; Tam, C.; Chan, G.; Kim, J.H.; Evans, D.; Fleiszig, S. MicroRNA-762 is upregulated in human corneal epithelial cells in response to tear fluid and Pseudomonas aeruginosa antigens and negatively regulates the expression of host defense genes encoding RNase7 and ST2. PLoS ONE 2013, 8, e57850. [Google Scholar] [CrossRef] [PubMed]
  247. Yang, K.; Wu, M.; Li, M.; Li, D.; Peng, A.; Nie, X.; Sun, M.; Wang, J.; Wu, Y.; Deng, Q.; et al. miR-155 suppresses bacterial clearance in Pseudomonas aeruginosa-induced keratitis by targeting Rheb. J. Infect. Dis. 2014, 210, 89–98. [Google Scholar] [CrossRef] [PubMed]
  248. Chamnanchanunt, S.; Kuroki, C.; Desakorn, V.; Enomoto, M.; Thanachartwet, V.; Sahassananda, D.; Sattabongkot, J.; Jenwithisuk, R.; Fucharoen, S.; Svasti, S.; et al. Downregulation of plasma miR-451 and miR-16 in Plasmodium vivax infection. Exp. Parasitol. 2015, 155, 19–25. [Google Scholar] [CrossRef] [PubMed]
  249. Kaur, H.; Sehgal, R.; Kumar, A.; Sehgal, A.; Bansal, D.; Sultan, A.A. Screening and identification of potential novel biomarker for diagnosis of complicated Plasmodium vivax malaria. J. Transl. Med. 2018, 16, 272. [Google Scholar] [CrossRef] [Green Version]
  250. Lui, J.H.; Hansen, D.V.; Kriegstein, A.R. Development and evolution of the human neocortex. Cell 2011, 146, 18–36. [Google Scholar] [CrossRef] [Green Version]
  251. Wang, J.; Cao, Y.; Lu, X.; Wang, T.; Li, S.; Kong, X.; Bo, C.; Li, J.; Wang, X.; Ma, H.; et al. MicroRNAs and nervous system diseases: Network insights and computational challenges. Brief. Bioinform. 2019, 1–13. [Google Scholar] [CrossRef]
  252. Serafin, A.; Foco, L.; Zanigni, S.; Blankenburg, H.; Picard, A.; Zanon, A.; Giannini, G.; Pichler, I.; Facheris, M.F.; Cortelli, P.; et al. Overexpression of blood microRNAs 103a, 30b, and 29a in L-dopa-treated patients with PD. Neurology 2015, 84, 645–653. [Google Scholar] [CrossRef]
  253. Scheltens, P.; Blennow, K.; Breteler, M.M.; de Strooper, B.; Frisoni, G.B.; Salloway, S.; Van der Flier, W.M. Alzheimer’s disease. Lancet 2016, 388, 505–517. [Google Scholar] [CrossRef]
  254. Noebels, J. Pathway-driven discovery of epilepsy genes. Nat. Neurosci. 2015, 18, 344–350. [Google Scholar] [CrossRef] [Green Version]
  255. Costa, P.M.; Cardoso, A.L.; Mano, M.; de Lima, M.C. MicroRNAs in glioblastoma: Role in pathogenesis and opportunities for targeted therapies. CNS Neurol. Disord. Drug Targets 2015, 14, 222–238. [Google Scholar] [CrossRef] [PubMed]
  256. Sun, Y.; Luo, Z.-M.; Guo, X.-M.; Su, D.-F.; Liu, X. An updated role of microRNA-124 in central nervous system disorders: A review. Front. Cell. Neurosci. 2015, 9, 193. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  257. Wang, Y.Z.; Tian, F.F.; Yan, M.; Zhang, J.M.; Liu, Q.; Lu, J.Y.; Zhou, W.B.; Yang, H.; Li, J. Delivery of an miR155 inhibitor by anti-CD20 single-chain antibody into B cells reduces the acetylcholine receptor-specific autoantibodies and ameliorates experimental autoimmune myasthenia gravis. Clin. Exp. Immunol. 2014, 176, 207–221. [Google Scholar] [CrossRef] [PubMed]
  258. Wang, W.X.; Rajeev, B.W.; Stromberg, A.J.; Ren, N.; Tang, G.; Huang, Q.; Rigoutsos, I.; Nelson, P.T. The expression of microRNA miR-107 decreases early in Alzheimer’s disease and may accelerate disease progression through regulation of beta-site amyloid precursor protein-cleaving enzyme 1. J. Neurosci. 2008, 28, 1213–1223. [Google Scholar] [CrossRef]
  259. Margis, R.; Margis, R.; Rieder, C.R. Identification of blood microRNAs associated to Parkinsonis disease. J. Biotechnol. 2011, 152, 96–101. [Google Scholar] [CrossRef] [Green Version]
  260. Soreq, H.; Wolf, Y. NeurimmiRs: microRNAs in the neuroimmune interface. Trends Mol. Med. 2011, 17, 548–555. [Google Scholar] [CrossRef]
  261. Boissonneault, V.; Plante, I.; Rivest, S.; Provost, P. MicroRNA-298 and microRNA-328 regulate expression of mouse beta-amyloid precursor protein-converting enzyme 1. J. Biol. Chem. 2009, 284, 1971–1981. [Google Scholar] [CrossRef] [Green Version]
  262. Alexandrov, P.N.; Dua, P.; Hill, J.M.; Bhattacharjee, S.; Zhao, Y.; Lukiw, W.J. microRNA (miRNA) speciation in Alzheimer’s disease (AD) cerebrospinal fluid (CSF) and extracellular fluid (ECF). Int. J. Biochem. Mol. Biol. 2012, 3, 365–373. [Google Scholar]
  263. Chen, Y.; Gao, C.; Sun, Q.; Pan, H.; Huang, P.; Ding, J.; Chen, S. MicroRNA-4639 Is a Regulator of DJ-1 Expression and a Potential Early Diagnostic Marker for Parkinson’s Disease. Front. Aging Neurosci. 2017, 9, 232. [Google Scholar] [CrossRef]
  264. Botta-Orfila, T.; Morato, X.; Compta, Y.; Lozano, J.J.; Falgas, N.; Valldeoriola, F.; Pont-Sunyer, C.; Vilas, D.; Mengual, L.; Fernandez, M.; et al. Identification of blood serum micro-RNAs associated with idiopathic and LRRK2 Parkinson’s disease. J. Neurosci. Res. 2014, 92, 1071–1077. [Google Scholar] [CrossRef] [PubMed]
  265. Papagiannakopoulos, T.; Shapiro, A.; Kosik, K.S. MicroRNA-21 targets a network of key tumor-suppressive pathways in glioblastoma cells. Cancer Res. 2008, 68, 8164–8172. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  266. Zhang, C.Z.; Zhang, J.X.; Zhang, A.L.; Shi, Z.D.; Han, L.; Jia, Z.F.; Yang, W.D.; Wang, G.X.; Jiang, T.; You, Y.P.; et al. MiR-221 and miR-222 target PUMA to induce cell survival in glioblastoma. Mol. Cancer 2010, 9, 229. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  267. Shu, M.; Zheng, X.; Wu, S.; Lu, H.; Leng, T.; Zhu, W.; Zhou, Y.; Ou, Y.; Lin, X.; Lin, Y.; et al. Targeting oncogenic miR-335 inhibits growth and invasion of malignant astrocytoma cells. Mol. Cancer 2011, 10, 59. [Google Scholar] [CrossRef] [Green Version]
  268. Silber, J.; Lim, D.A.; Petritsch, C.; Persson, A.I.; Maunakea, A.K.; Yu, M.; Vandenberg, S.R.; Ginzinger, D.G.; James, C.D.; Costello, J.F.; et al. miR-124 and miR-137 inhibit proliferation of glioblastoma multiforme cells and induce differentiation of brain tumor stem cells. BMC Med. 2008, 6, 14. [Google Scholar] [CrossRef]
  269. Xia, H.; Yan, Y.; Hu, M.; Wang, Y.; Wang, Y.; Dai, Y.; Chen, J.; Di, G.; Chen, X.; Jiang, X. MiR-218 sensitizes glioma cells to apoptosis and inhibits tumorigenicity by regulating ECOP-mediated suppression of NF-kappaB activity. Neuro-Oncology 2013, 15, 413–422. [Google Scholar] [CrossRef] [Green Version]
  270. Nan, Y.; Han, L.; Zhang, A.; Wang, G.; Jia, Z.; Yang, Y.; Yue, X.; Pu, P.; Zhong, Y.; Kang, C. MiRNA-451 plays a role as tumor suppressor in human glioma cells. Brain Res. 2010, 1359, 14–21. [Google Scholar] [CrossRef]
  271. Ma, X.; Zhou, J.; Zhong, Y.; Jiang, L.; Mu, P.; Li, Y.; Singh, N.; Nagarkatti, M.; Nagarkatti, P. Expression, regulation and function of microRNAs in multiple sclerosis. Int. J. Med. Sci. 2014, 11, 810–818. [Google Scholar] [CrossRef] [Green Version]
  272. Punga, A.R.; Punga, T. Circulating microRNAs as potential biomarkers in myasthenia gravis patients. Ann. N. Y. Acad. Sci. 2018, 1412, 33–40. [Google Scholar] [CrossRef]
  273. Punga, T.; Bartoccioni, E.; Lewandowska, M.; Damato, V.; Evoli, A.; Punga, A.R. Disease specific enrichment of circulating let-7 family microRNA in MuSK+ myasthenia gravis. J. Neuroimmunol. 2016, 292, 21–26. [Google Scholar] [CrossRef] [Green Version]
  274. Nogales-Gadea, G.; Ramos-Fransi, A.; Suarez-Calvet, X.; Navas, M.; Rojas-Garcia, R.; Mosquera, J.L.; Diaz-Manera, J.; Querol, L.; Gallardo, E.; Illa, I. Analysis of serum miRNA profiles of myasthenia gravis patients. PLoS ONE 2014, 9, e91927. [Google Scholar] [CrossRef] [PubMed]
  275. Zendjabil, M. Circulating microRNAs as novel biomarkers of Alzheimer’s disease. Clin. Chim. Acta 2018, 484, 99–104. [Google Scholar] [CrossRef] [PubMed]
  276. Hampel, H.; O’Bryant, S.E.; Molinuevo, J.L.; Zetterberg, H.; Masters, C.L.; Lista, S.; Kiddle, S.J.; Batrla, R.; Blennow, K. Blood-based biomarkers for Alzheimer disease: Mapping the road to the clinic. Nat. Rev. Neurol. 2018, 14, 639–652. [Google Scholar] [CrossRef]
  277. Ritchie, C.; Smailagic, N.; Noel-Storr, A.H.; Ukoumunne, O.; Ladds, E.C.; Martin, S. CSF tau and the CSF tau/ABeta ratio for the diagnosis of Alzheimer’s disease dementia and other dementias in people with mild cognitive impairment (MCI). Cochrane Database Syst. Rev. 2017, 3, Cd010803. [Google Scholar] [CrossRef] [Green Version]
  278. Paolacci, L.; Giannandrea, D.; Mecocci, P.; Parnetti, L. Biomarkers for Early Diagnosis of Alzheimer’s Disease in the Oldest Old: Yes or No? J. Alzheimers Dis. 2017, 58, 323–335. [Google Scholar] [CrossRef] [PubMed]
  279. Kumar, S.; Vijayan, M.; Bhatti, J.S.; Reddy, P.H. MicroRNAs as Peripheral Biomarkers in Aging and Age-Related Diseases. Prog. Mol. Biol. Transl. Sci. 2017, 146, 47–94. [Google Scholar] [CrossRef] [PubMed]
  280. Hughes, A.J.; Daniel, S.E.; Ben-Shlomo, Y.; Lees, A.J. The accuracy of diagnosis of parkinsonian syndromes in a specialist movement disorder service. Brain 2002, 125, 861–870. [Google Scholar] [CrossRef] [Green Version]
  281. Van Dijk, K.D.; Teunissen, C.E.; Drukarch, B.; Jimenez, C.R.; Groenewegen, H.J.; Berendse, H.W.; van de Berg, W.D. Diagnostic cerebrospinal fluid biomarkers for Parkinson’s disease: A pathogenetically based approach. Neurobiol. Dis. 2010, 39, 229–241. [Google Scholar] [CrossRef]
  282. Mushtaq, G.; Greig, N.H.; Anwar, F.; Zamzami, M.A.; Choudhry, H.; Shaik, M.M.; Tamargo, I.A.; Kamal, M.A. miRNAs as Circulating Biomarkers for Alzheimer’s Disease and Parkinson’s Disease. Med. Chem. 2016, 12, 217–225. [Google Scholar] [CrossRef]
  283. Harris, V.K.; Tuddenham, J.F.; Sadiq, S.A. Biomarkers of multiple sclerosis: Current findings. Degener. Neurol. Neuromuscul. Dis. 2017, 7, 19–29. [Google Scholar] [CrossRef] [Green Version]
  284. Housley, W.J.; Pitt, D.; Hafler, D.A. Biomarkers in multiple sclerosis. Clin. Immunol. 2015, 161, 51–58. [Google Scholar] [CrossRef]
  285. Regev, K.; Healy, B.C.; Paul, A.; Diaz-Cruz, C.; Mazzola, M.A.; Raheja, R.; Glanz, B.I.; Kivisäkk, P.; Chitnis, T.; Jagodic, M.; et al. Identification of MS-specific serum miRNAs in an international multicenter study. Neurol.-Neuroimmunol. Neuroinflamm. 2018, 5, e491. [Google Scholar] [CrossRef] [Green Version]
  286. Meriggioli, M.; Sanders, D. Muscle autoantibodies in myasthenia gravis: Beyond diagnosis? Expert Rev. Clin. Immunol. 2012, 8, 427–438. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  287. Sayed, D.; Hong, C.; Chen, I.Y.; Lypowy, J.; Abdellatif, M. MicroRNAs play an essential role in the development of cardiac hypertrophy. Circ. Res. 2007, 100, 416–424. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  288. Schratt, G.M.; Tuebing, F.; Nigh, E.A.; Kane, C.G.; Sabatini, M.E.; Kiebler, M.; Greenberg, M.E. A brain-specific microRNA regulates dendritic spine development. Nature 2006, 439, 283–289. [Google Scholar] [CrossRef] [PubMed]
  289. Zhao, C.; Sun, G.; Li, S.; Shi, Y. A feedback regulatory loop involving microRNA-9 and nuclear receptor TLX in neural stem cell fate determination. Nat. Struct. Mol. Biol. 2009, 16, 365–371. [Google Scholar] [CrossRef] [Green Version]
  290. Mehler, M.F.; Mattick, J.S. Noncoding RNAs and RNA editing in brain development, functional diversification, and neurological disease. Physiol. Rev. 2007, 87, 799–823. [Google Scholar] [CrossRef] [Green Version]
  291. Xu, W.; Zhou, Y.; Xu, G.; Geng, B.; Cui, Q. Transcriptome analysis reveals non-identical microRNA profiles between arterial and venous plasma. Oncotarget 2017, 8, 28471–28480. [Google Scholar] [CrossRef]
  292. Zhou, X.; Wen, W.; Shan, X.; Zhu, W.; Xu, J.; Guo, R.; Cheng, W.; Wang, F.; Qi, L.W.; Chen, Y.; et al. A six-microRNA panel in plasma was identified as a potential biomarker for lung adenocarcinoma diagnosis. Oncotarget 2017, 8, 6513–6525. [Google Scholar] [CrossRef]
  293. Monzo, M.; Santasusagna, S.; Moreno, I.; Martinez, F.; Hernández, R.; Muñoz, C.; Castellano, J.J.; Moreno, J.; Navarro, A. Exosomal microRNAs isolated from plasma of mesenteric veins linked to liver metastases in resected patients with colon cancer. Oncotarget 2017, 8, 30859–30869. [Google Scholar] [CrossRef]
  294. Sempere, L.F.; Freemantle, S.; Pitha-Rowe, I.; Moss, E.; Dmitrovsky, E.; Ambros, V. Expression profiling of mammalian microRNAs uncovers a subset of brain-expressed microRNAs with possible roles in murine and human neuronal differentiation. Genome Biol. 2004, 5, R13. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  295. Valoczi, A.; Hornyik, C.; Varga, N.; Burgyan, J.; Kauppinen, S.; Havelda, Z. Sensitive and specific detection of microRNAs by northern blot analysis using LNA-modified oligonucleotide probes. Nucleic Acids Res. 2004, 32, e175. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  296. Kloosterman, W.P.; Wienholds, E.; de Bruijn, E.; Kauppinen, S.; Plasterk, R.H. In situ detection of miRNAs in animal embryos using LNA-modified oligonucleotide probes. Nat. Methods 2006, 3, 27–29. [Google Scholar] [CrossRef] [PubMed]
  297. Wang, J.; Chen, J.; Chang, P.; LeBlanc, A.; Li, D.; Abbruzzesse, J.L.; Frazier, M.L.; Killary, A.M.; Sen, S. MicroRNAs in plasma of pancreatic ductal adenocarcinoma patients as novel blood-based biomarkers of disease. Cancer Prev. Res. 2009, 2, 807–813. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  298. Thomson, J.M.; Parker, J.; Perou, C.M.; Hammond, S.M. A custom microarray platform for analysis of microRNA gene expression. Nat. Methods 2004, 1, 47–53. [Google Scholar] [CrossRef] [Green Version]
  299. Wang, J.; Raimondo, M.; Guha, S.; Chen, J.; Diao, L.; Dong, X.; Wallace, M.B.; Killary, A.M.; Frazier, M.L.; Woodward, T.A.; et al. Circulating microRNAs in Pancreatic Juice as Candidate Biomarkers of Pancreatic Cancer. J. Cancer 2014, 5, 696–705. [Google Scholar] [CrossRef] [Green Version]
  300. Wang, J.; Paris, P.L.; Chen, J.; Ngo, V.; Yao, H.; Frazier, M.L.; Killary, A.M.; Liu, C.-G.; Liang, H.; Mathy, C.; et al. Next generation sequencing of pancreatic cyst fluid microRNAs from low grade-benign and high grade-invasive lesions. Cancer Lett. 2015, 356, 404–409. [Google Scholar] [CrossRef]
  301. Sato, F.; Tsuchiya, S.; Terasawa, K.; Tsujimoto, G. Intra-platform repeatability and inter-platform comparability of microRNA microarray technology. PLoS ONE 2009, 4, e5540. [Google Scholar] [CrossRef] [Green Version]
  302. Witwer, K.W. Circulating microRNA biomarker studies: Pitfalls and potential solutions. Clin. Chem. 2015, 61, 56–63. [Google Scholar] [CrossRef]
  303. Binderup, H.G.; Madsen, J.S.; Heegaard, N.H.H.; Houlind, K.; Andersen, R.F.; Brasen, C.L. Quantification of microRNA levels in plasma—Impact of preanalytical and analytical conditions. PLoS ONE 2018, 13, e0201069. [Google Scholar] [CrossRef]
  304. Madadi, S.; Soleimani, M. Comparison of miR-16 and cel-miR-39 as reference controls for serum miRNA normalization in colorectal cancer. J. Cell. Biochem. 2019, 120, 4802–4803. [Google Scholar] [CrossRef] [PubMed]
  305. Schwarzenbach, H.; da Silva, A.M.; Calin, G.; Pantel, K. Data Normalization Strategies for MicroRNA Quantification. Clin. Chem. 2020, 61, 1333–1342. [Google Scholar] [CrossRef] [PubMed]
  306. Hayes, J.; Peruzzi, P.P.; Lawler, S. MicroRNAs in cancer: Biomarkers, functions and therapy. Trends Mol. Med. 2014, 20, 460–469. [Google Scholar] [CrossRef] [PubMed]
  307. Cao, D.-D.; Li, L.; Chan, W.-Y. MicroRNAs: Key Regulators in the Central Nervous System and Their Implication in Neurological Diseases. Int. J. Mol. Sci. 2016, 17, 842. [Google Scholar] [CrossRef] [PubMed]
  308. Sun, X.Y.; Zhang, J.; Niu, W.; Guo, W.; Song, H.T.; Li, H.Y.; Fan, H.M.; Zhao, L.; Zhong, A.F.; Dai, Y.H.; et al. A preliminary analysis of microRNA as potential clinical biomarker for schizophrenia. Am. J. Med Genet. Part B Neuropsychiatr. Genet. 2015, 168, 170–178. [Google Scholar] [CrossRef]
  309. Montagnana, M.; Benati, M.; Danese, E.; Minicozzi, A.M.; Paviati, E.; Gusella, M.; Pasini, F.; Bovo, C.; Guidi, G.C.; Lippi, G. Plasma Expression Levels of Circulating miR-21 are not Useful for Diagnosing and Monitoring Colorectal Cancer. Clin. Lab. 2016, 62, 967–970. [Google Scholar] [CrossRef]
  310. Geng, Q.; Fan, T.; Zhang, B.; Wang, W.; Xu, Y.; Hu, H. Five microRNAs in plasma as novel biomarkers for screening of early-stage non-small cell lung cancer. Respir. Res. 2014, 15, 149. [Google Scholar] [CrossRef] [Green Version]
  311. Liu, Q.; Yu, Z.; Yuan, S.; Xie, W.; Li, C.; Hu, Z.; Xiang, Y.; Wu, N.; Wu, L.; Bai, L.; et al. Circulating exosomal microRNAs as prognostic biomarkers for non-small-cell lung cancer. Oncotarget 2017, 8, 13048–13058. [Google Scholar] [CrossRef] [Green Version]
  312. Dejima, H.; Iinuma, H.; Kanaoka, R.; Matsutani, N.; Kawamura, M. Exosomal microRNA in plasma as a non-invasive biomarker for the recurrence of non-small cell lung cancer. Oncol. Lett. 2017, 13, 1256–1263. [Google Scholar] [CrossRef] [Green Version]
  313. Erbes, T.; Hirschfeld, M.; Rucker, G.; Jaeger, M.; Boas, J.; Iborra, S.; Mayer, S.; Gitsch, G.; Stickeler, E. Feasibility of urinary microRNA detection in breast cancer patients and its potential as an innovative non-invasive biomarker. BMC Cancer 2015, 15, 193. [Google Scholar] [CrossRef] [Green Version]
  314. Tsukamoto, M.; Iinuma, H.; Yagi, T.; Matsuda, K.; Hashiguchi, Y. Circulating Exosomal MicroRNA-21 as a Biomarker in Each Tumor Stage of Colorectal Cancer. Oncology 2017, 92, 360–370. [Google Scholar] [CrossRef]
  315. Sierzega, M.; Kaczor, M.; Kolodziejczyk, P.; Kulig, J.; Sanak, M.; Richter, P. Evaluation of serum microRNA biomarkers for gastric cancer based on blood and tissue pools profiling: The importance of miR-21 and miR-331. Br. J. Cancer 2017, 117, 266–273. [Google Scholar] [CrossRef] [Green Version]
  316. Zheng, Y.; Cui, L.; Sun, W.; Zhou, H.; Yuan, X.; Huo, M.; Chen, J.; Lou, Y.; Guo, J. MicroRNA-21 is a new marker of circulating tumor cells in gastric cancer patients. Cancer Biomark. 2011, 10, 71–77. [Google Scholar] [CrossRef] [PubMed]
  317. Williams, G.; Uchimoto, M.L.; Coult, N.; World, D.; Beasley, E. Body fluid mixtures: Resolution using forensic microRNA analysis. Forensic Sci. Int. Genet. Suppl. Ser. 2013, 4, e292. [Google Scholar] [CrossRef] [Green Version]
  318. Barger, J.F.; Nana-Sinkam, S.P. MicroRNA as tools and therapeutics in lung cancer. Respir. Med. 2015, 109, 803–812. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  319. Li, J.; Liu, Y.; Wang, C.; Deng, T.; Liang, H.; Wang, Y.; Huang, D.; Fan, Q.; Wang, X.; Ning, T.; et al. Serum miRNA expression profile as a prognostic biomarker of stage II/III colorectal adenocarcinoma. Sci. Rep. 2015, 5, 12921. [Google Scholar] [CrossRef] [PubMed]
  320. Wu, H.Z.; Ong, K.L.; Seeher, K.; Armstrong, N.J.; Thalamuthu, A.; Brodaty, H.; Sachdev, P.; Mather, K. Circulating microRNAs as Biomarkers of Alzheimer’s Disease: A Systematic Review. J. Alzheimers Dis. 2016, 49, 755–766. [Google Scholar] [CrossRef]
  321. Schipper, H.M.; Maes, O.C.; Chertkow, H.M.; Wang, E. MicroRNA expression in Alzheimer blood mononuclear cells. Gene Regul. Syst. Biol. 2007, 1, 263–274. [Google Scholar] [CrossRef]
  322. Muller, M.; Kuiperij, H.B.; Claassen, J.A.; Kusters, B.; Verbeek, M.M. MicroRNAs in Alzheimer’s disease: Differential expression in hippocampus and cell-free cerebrospinal fluid. Neurobiol. Aging 2014, 35, 152–158. [Google Scholar] [CrossRef]
  323. Cogswell, J.P.; Ward, J.; Taylor, I.A.; Waters, M.; Shi, Y.; Cannon, B.; Kelnar, K.; Kemppainen, J.; Brown, D.; Chen, C.; et al. Identification of miRNA changes in Alzheimer’s disease brain and CSF yields putative biomarkers and insights into disease pathways. J. Alzheimers Dis. 2008, 14, 27–41. [Google Scholar] [CrossRef]
  324. Jarry, J.; Schadendorf, D.; Greenwood, C.; Spatz, A.; van Kempen, L.C. The validity of circulating microRNAs in oncology: Five years of challenges and contradictions. Mol. Oncol. 2014, 8, 819–829. [Google Scholar] [CrossRef] [PubMed]
  325. Hrustincova, A.; Votavova, H.; Dostalova Merkerova, M. Circulating MicroRNAs: Methodological Aspects in Detection of These Biomarkers. Folia Biol. 2015, 61, 203–218. [Google Scholar]
  326. Pritchard, C.C.; Cheng, H.H.; Tewari, M. MicroRNA profiling: Approaches and considerations. Nat. Rev. Genet. 2012, 13, 358–369. [Google Scholar] [CrossRef] [PubMed]
Table 1. MicroRNA (miRNA) regulation in cancer.
Table 1. MicroRNA (miRNA) regulation in cancer.
miRNADiseaseRegulationMain CharacteristicsReference
miR-10bBreast Cancer UpregulatedOverexpressed only in metastatic disease
In non-metastatic disease—cell invasion and metastasis inductor
High levels correlated to disease progression and poor outcome
[124,125,126]
miR-196aBreast CancerUpregulatedOncogenic role
Alters apoptosis and angiogenesis
High levels associated with advanced disease
[127,128,129,130]
miR-4417Breast CancerDownregulatedPrognostic tool for TNBC
Levels correlated to the evolution of the disease
[52,131]
miR-200 cluster Ovarian CancerUpregulatedLow levels associated with cell invasion and metastasis
Low levels predict poor outcomes and disease recurrence
Angiogenesis inhibitors
[132,133,134,135]
miR-506Ovarian CancerDysregulatedCell invasion, migration and EMT inhibitor
High levels confer a good prognosis
[136]
let-7 clusterOvarian CancerDysregulatedSuppressors of tumor growth and cell invasion[134,137,138]
miR-183Ovarian CancerDysregulatedOverexpressed in low metastatic ovarian cancer[139]
miR-22Ovarian CancerDysregulatedOverexpressed in low metastatic ovarian cancer[139]
miR-21aCervical CancerUpregulatedInflammation and proliferation stimulator
Apoptosis inhibitor
[66]
miR-944Cervical CancerUpregulatedLevels associated with advanced FIGO stage, bulky tumor size, lymph node metastasis
Poor prognostic factor
Higher levels in HPV-positive cervical cancer
[140,141,142]
miR-138Cervical CancerDownregulatedTumor suppressor role
Apoptosis stimulator
Negatively associated with lymph node metastasis and advanced FIGO stage
[143,144,145]
Table 3. miRNA regulation in bacterial infections.
Table 3. miRNA regulation in bacterial infections.
PathogenmiRNAsRegulationReference
Staphylococcus aureusmiR-133a, miR-133b, miR-122, miR-205, miR-1899, miR-714, miR-291bWu and colleagues [241]
Streptococcus pneumoniaehsa-miR-199b-5p, hsa-miR-30a-5pPoore et al. [242]
hsa-miR-942-5p, hsa-miR-342-5p, hsa-miR-503-5p
Escherichia colimiR-16, miR-17, miR-20a, miR-26a, miR-26b, miR-106a, miR-106b, miR-451Wu and colleagues [241]
Listeria monocytogenesmiR-133, miR-998Mannala and colleagues [243]
miR-954, miR-3000
Mycobacterium tuberculosismiR-7-1, let-7f-2, miR-126, miR-130b, miR-18a, miR-19b-1, miR-505, miR-545, miR-550, miR-760, miR-875-5pFurci and colleagues [244]
miR-127-3p, miR-290b, miR-378, miR-337-5p, miR520c-3p, miR-573, miR-601, miR-645
Brucella melitensislet-7b, miR-93, miR-151-3p, miR-92a, miR-142-5p, miR-99a, miR-181b, miR-1981Zheng and colleagues [245]
Pseudomonas aeruginosamiR-302b, miR-301b, miR-762, miR-155Zhou et al. [235], Mun et al. [246], Yang et al. [247]
Plasmodium vivaxmiR-451, miR-16Chamnanchanunt and colleagues [248]
miR-7977Kaur and colleagues [249]
Table 4. miRNA regulation in neurologic disorders.
Table 4. miRNA regulation in neurologic disorders.
DiseasemiRNAsRegulationReference
Alzheimer’s DiseasemiR-107, miR-298, miR-328Wang et al. [258], Boissonneault et al. [261]
miR-9, miR-34a, miR-125b, miR-146a, miR-155Alexandrov et al. [262]
Parkinson’s DiseasemiR-103a-3p, miR-30b-5p, miR-29a-3p, miR-4639-5pMargis et al. [259], Chen et al. [263]
miR-29a-3p, miR-29c-3p, miR-19a-3p, and miR-19b-3pBotta-Orfila et al. [264]
GlioblastomamiR-21, miR-221, miR-222, miR-335Papagiannakopoulos et al. [265], Zhang et al. [266], Shu et al. [267]
miR-124, miR-137, miR-218, miR-451Silber et al. [268], Xia et al. [269], Nan et al. [270]
Multiple SclerosismiR-19a, miR-21, miR-22, miR-142-3p, miR-146a, miR-146b, miR-155, miR-210, miR-326Ma et al. [271]
miR-15a, miR-15b, miR-181c, miR-328Ma et al. [271]
Myasthenia GravismiR-21-5p, miR-150-5p, miR-151a-3p, let-7a-5p, let-7f-5p, miR-423-5pPunga et al. [272], Punga et al. [273]
miR-15b, miR-122, miR-140-3p, miR-185, miR-192, miR-20b, miR-885-5pNogales-Gadea et al. [274]

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Condrat, C.E.; Thompson, D.C.; Barbu, M.G.; Bugnar, O.L.; Boboc, A.; Cretoiu, D.; Suciu, N.; Cretoiu, S.M.; Voinea, S.C. miRNAs as Biomarkers in Disease: Latest Findings Regarding Their Role in Diagnosis and Prognosis. Cells 2020, 9, 276. https://doi.org/10.3390/cells9020276

AMA Style

Condrat CE, Thompson DC, Barbu MG, Bugnar OL, Boboc A, Cretoiu D, Suciu N, Cretoiu SM, Voinea SC. miRNAs as Biomarkers in Disease: Latest Findings Regarding Their Role in Diagnosis and Prognosis. Cells. 2020; 9(2):276. https://doi.org/10.3390/cells9020276

Chicago/Turabian Style

Condrat, Carmen Elena, Dana Claudia Thompson, Madalina Gabriela Barbu, Oana Larisa Bugnar, Andreea Boboc, Dragos Cretoiu, Nicolae Suciu, Sanda Maria Cretoiu, and Silviu Cristian Voinea. 2020. "miRNAs as Biomarkers in Disease: Latest Findings Regarding Their Role in Diagnosis and Prognosis" Cells 9, no. 2: 276. https://doi.org/10.3390/cells9020276

APA Style

Condrat, C. E., Thompson, D. C., Barbu, M. G., Bugnar, O. L., Boboc, A., Cretoiu, D., Suciu, N., Cretoiu, S. M., & Voinea, S. C. (2020). miRNAs as Biomarkers in Disease: Latest Findings Regarding Their Role in Diagnosis and Prognosis. Cells, 9(2), 276. https://doi.org/10.3390/cells9020276

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