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Cancer Biomarker Research and Personalized Medicine 2.0
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Cancer Biomarker Research and Personalized Medicine 2.0

A special issue of Journal of Personalized Medicine (ISSN 2075-4426). This special issue belongs to the section "Disease Biomarker".

Deadline for manuscript submissions: closed (1 March 2024) | Viewed by 15006

Special Issue Editors

Dr. James Meehan

E-Mail Website
Guest Editor
The Royal (Dick) School of Veterinary Studies, Roslin Institute, The University of Edinburgh, Edinburgh, Midlothian, Scotland, UK
Interests: personalized medicine; prostate cancer; breast cancer; cancer cell biology; cancer biomarkers
Special Issues, Collections and Topics in MDPI journals
Dr. Mark E. Gray

E-Mail Website
Guest Editor
The Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh, Midlothian, Scotland, UK
Interests: cancer biology; translational oncology; animal models; personalized medicine; cancer biomarkers
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Treating individual patients based on specific factors, such as biomarkers, is what differentiates personalized medicine from standard treatment regimens. Although personalized medicine can be applied to almost any branch of medicine, it is perhaps most easily applied to the field of oncology. Cancer is a heterogeneous disease, meaning that even though patients may be histologically diagnosed with the same cancer type, their tumors may have different molecular characteristics, genetic mutations, or tumor microenvironments that can influence prognosis or treatment response. Through biomarkers, patients could be separated into cohorts with respect to distinctions in disease predisposition, prognosis, and expected response rates to different treatments. Improved outcomes can therefore be made using biomarkers to identify patients that have a greater likelihood of achieving a benefit from certain treatments, while also distinguishing those that require more aggressive treatment strategies.

As such, there is currently a major drive in oncology to achieve personalized cancer medicine through the identification and use of disease-specific biomarkers. These biomarkers include genes, intracellular or secreted proteins, circulating tumor cells, exosomes, and DNA. This Special Issue aims to describe current developments in biomarker research ranging from in vitro and animal model studies, through to preclinical and clinical validation trails. In doing so, we will describe exciting new tissue- and blood/urine-based biomarker research, highlighting the advantages and potential limitations of incorporating biomarkers into clinical practices.

Dr. James Meehan
Dr. Mark E. Gray
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Personalized Medicine is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • tissue-based biomarker
  • blood-based biomarker
  • urine-based biomarker
  • cancer
  • precision medicine
  • personalized medicine

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Related Special Issue

Published Papers (6 papers)

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Editorial

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4 pages, 175 KiB  
Editorial
Special Issue “Cancer Biomarker Research and Personalized Medicine 2.0”
by James Meehan and Mark Gray
J. Pers. Med. 2024, 14(6), 549; https://doi.org/10.3390/jpm14060549 - 21 May 2024
Viewed by 723
Abstract
In 2022, there was an estimated incidence of 20 million cancer cases and 9 [...] Full article
(This article belongs to the Special Issue Cancer Biomarker Research and Personalized Medicine 2.0)

Research

Jump to: Editorial

15 pages, 1370 KiB  
Article
Variation Analysis in Premenopausal and Postmenopausal Breast Cancer Cases
by Ibrahim Halil Erdogdu, Seda Orenay-Boyacioglu, Olcay Boyacioglu, Duygu Gurel, Nurten Akdeniz and Ibrahim Meteoglu
J. Pers. Med. 2024, 14(4), 434; https://doi.org/10.3390/jpm14040434 - 20 Apr 2024
Cited by 1 | Viewed by 1503
Abstract
Menopausal status affects the prognoses and consequences of breast cancer. Therefore, this retrospective study aimed to reveal the molecular variation profile differences in breast cancer patients according to their menopausal status, with the hypothesis that the molecular variation profiles will be different at [...] Read more.
Menopausal status affects the prognoses and consequences of breast cancer. Therefore, this retrospective study aimed to reveal the molecular variation profile differences in breast cancer patients according to their menopausal status, with the hypothesis that the molecular variation profiles will be different at premenopausal and postmenopausal ages. Breast cancer patients (n = 254) who underwent molecular subtyping and QIAseq Human Breast Cancer NGS Panel screening between 2018 and 2022 were evaluated retrospectively. Their menopausal status was defined by age, and those aged 50 years and above were considered postmenopausal. Of the subjects, 58.66% (n = 149) were premenopausal and 41.34% (n = 105) were postmenopausal. The mean age at the time of diagnosis for all patients was 49.31 ± 11.19 years, with respective values of 42.11 ± 5.51 and 59.54 ± 9.01 years for the premenopausal and postmenopausal groups, respectively (p = 0.000). Among premenopausal patients, the percentages of patients in BCa subtypes (luminal A, luminal B-HER2(−), luminal B-HER2(+), HER2 positive, and triple-negative) were determined to be 34.90%, 8.05%, 26.17%, 10.74%, and 20.13%, respectively, while in the postmenopausal group, these values were 39.05%, 16.19%, 24.76%, 6.67%, and 13.33%, respectively (p > 0.05). Considering menopausal status, the distribution of hormone receptors in premenopausal patients was ER(+)/PgR(+) 63.76%, ER(−)/PgR(−) 23.49%, ER(+)/PgR(−) 10.74%, and ER(−)/PgR(+) 2.01%, respectively, while in postmenopausal women, this distribution was observed to be 74.29%, 23.81%, 1.90% and 0.00% in the same order (p = 0.008). The most frequently mutated gene was TP53 in 130 patients (51.18%), followed by PIK3CA in 85 patients (33.46%), BRCA2 and NF1 in 56 patients (22.05%), PTEN in 54 patients (21.26%), and ATR and CHEK2 in 53 patients (20.87%). TP53, PIK3CA, NF1, BRCA2, PTEN, and CHEK2 mutations were more frequently observed in premenopausal patients, while TP53, PIK3CA, BRCA2, BRCA1, and ATR mutations in postmenopausal patients. These findings contribute to a deeper understanding of the underlying causes of breast cancer with respect to menopausal status. This study is the first from Turkey that reflects the molecular subtyping and somatic mutation profiles of breast cancer patients according to menopausal status. Full article
(This article belongs to the Special Issue Cancer Biomarker Research and Personalized Medicine 2.0)
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16 pages, 2866 KiB  
Article
Development and Validation of Risk Prediction Models for Colorectal Cancer in Patients with Symptoms
by Wei Xu, Ines Mesa-Eguiagaray, Theresa Kirkpatrick, Jennifer Devlin, Stephanie Brogan, Patricia Turner, Chloe Macdonald, Michelle Thornton, Xiaomeng Zhang, Yazhou He, Xue Li, Maria Timofeeva, Susan Farrington, Farhat Din, Malcolm Dunlop and Evropi Theodoratou
J. Pers. Med. 2023, 13(7), 1065; https://doi.org/10.3390/jpm13071065 - 29 Jun 2023
Cited by 2 | Viewed by 1834
Abstract
We aimed to develop and validate prediction models incorporating demographics, clinical features, and a weighted genetic risk score (wGRS) for individual prediction of colorectal cancer (CRC) risk in patients with gastroenterological symptoms. Prediction models were developed with internal validation [CRC Cases: n = [...] Read more.
We aimed to develop and validate prediction models incorporating demographics, clinical features, and a weighted genetic risk score (wGRS) for individual prediction of colorectal cancer (CRC) risk in patients with gastroenterological symptoms. Prediction models were developed with internal validation [CRC Cases: n = 1686/Controls: n = 963]. Candidate predictors included age, sex, BMI, wGRS, family history, and symptoms (changes in bowel habits, rectal bleeding, weight loss, anaemia, abdominal pain). The baseline model included all the non-genetic predictors. Models A (baseline model + wGRS) and B (baseline model) were developed based on LASSO regression to select predictors. Models C (baseline model + wGRS) and D (baseline model) were built using all variables. Models’ calibration and discrimination were evaluated through the Hosmer-Lemeshow test (calibration curves were plotted) and C-statistics (corrected based on 1000 bootstrapping). The models’ prediction performance was: model A (corrected C-statistic = 0.765); model B (corrected C-statistic = 0.753); model C (corrected C-statistic = 0.764); and model D (corrected C-statistic = 0.752). Models A and C, that integrated wGRS with demographic and clinical predictors, had a statistically significant improved prediction performance. Our findings suggest that future application of genetic predictors holds significant promise, which could enhance CRC risk prediction. Therefore, further investigation through model external validation and clinical impact is merited. Full article
(This article belongs to the Special Issue Cancer Biomarker Research and Personalized Medicine 2.0)
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11 pages, 725 KiB  
Article
Evaluation of Neutrophil-to-Lymphocyte Ratio (NLR), Platelet-to-Lymphocyte Ratio (PLR) and Systemic Immune–Inflammation Index (SII) as Potential Biomarkers in Patients with Sporadic Medullary Thyroid Cancer (MTC)
by Roberta Modica, Roberto Minotta, Alessia Liccardi, Giuseppe Cannavale, Elio Benevento and Annamaria Colao
J. Pers. Med. 2023, 13(6), 953; https://doi.org/10.3390/jpm13060953 - 5 Jun 2023
Cited by 17 | Viewed by 2536
Abstract
Medullary thyroid cancer (MTC) is a rare neuroendocrine neoplasm, and calcitonin is its main biomarker. An elevated neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR) and systemic immune–inflammation index (SII) have been considered as negative prognostic factors in several neoplasms. The aim of this study [...] Read more.
Medullary thyroid cancer (MTC) is a rare neuroendocrine neoplasm, and calcitonin is its main biomarker. An elevated neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR) and systemic immune–inflammation index (SII) have been considered as negative prognostic factors in several neoplasms. The aim of this study is to evaluate the potential role of NLR, PLR and SII as biomarkers in MTC. Clinical data and tumor histological characteristics of patients with sporadic MTC, referred to the NET Unit of Federico II University of Naples (ENETS CoE) from 2012 to 2022, were retrospectively evaluated by analyzing preoperative and postoperative calcitonin, NLR, PLR and SII. We included 35 MTC patients undergoing total thyroidectomy. The mean preoperative NLR was 2.70 (±1.41, 0.93–7.98), the PLR was 121.05 (±41.9, 40.98–227.23) and SII was 597.92 (±345.58, 186.59–1628). We identified a statistically significant difference between pre- and post-thyroidectomy NLR (p = 0.02), SII (p = 0.02) and calcitonin (p = 0.0) values. No association with prognosis or tumor characteristics emerged. Elevated preoperative NLR and SII suggest a possible disease-associated inflammatory response, and their reduction after surgery may be related to debulking effects. Further studies are needed to define the role of NLR, PLR and SII as prognostic markers in MTC. Full article
(This article belongs to the Special Issue Cancer Biomarker Research and Personalized Medicine 2.0)
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18 pages, 2347 KiB  
Article
Machine Learning-Assisted FTIR Analysis of Circulating Extracellular Vesicles for Cancer Liquid Biopsy
by Riccardo Di Santo, Maria Vaccaro, Sabrina Romanò, Flavio Di Giacinto, Massimiliano Papi, Gian Ludovico Rapaccini, Marco De Spirito, Luca Miele, Umberto Basile and Gabriele Ciasca
J. Pers. Med. 2022, 12(6), 949; https://doi.org/10.3390/jpm12060949 - 10 Jun 2022
Cited by 23 | Viewed by 3934
Abstract
Extracellular vesicles (EVs) are abundantly released into the systemic circulation, where they show remarkable stability and harbor molecular constituents that provide biochemical information about their cells of origin. Due to this characteristic, EVs are attracting increasing attention as a source of circulating biomarkers [...] Read more.
Extracellular vesicles (EVs) are abundantly released into the systemic circulation, where they show remarkable stability and harbor molecular constituents that provide biochemical information about their cells of origin. Due to this characteristic, EVs are attracting increasing attention as a source of circulating biomarkers for cancer liquid biopsy and personalized medicine. Despite this potential, none of the discovered biomarkers has entered the clinical practice so far, and novel approaches for the label-free characterization of EVs are highly demanded. In this regard, Fourier Transform Infrared Spectroscopy (FTIR) has great potential as it provides a quick, reproducible, and informative biomolecular fingerprint of EVs. In this pilot study, we investigated, for the first time in the literature, the capability of FTIR spectroscopy to distinguish between EVs extracted from sera of cancer patients and controls based on their mid-IR spectral response. For this purpose, EV-enriched suspensions were obtained from the serum of patients diagnosed with Hepatocellular Carcinoma (HCC) of nonviral origin and noncancer subjects. Our data point out the presence of statistically significant differences in the integrated intensities of major mid-IR absorption bands, including the carbohydrate and nucleic acids band, the protein amide I and II bands, and the lipid CH stretching band. Additionally, we used Principal Component Analysis combined with Linear Discriminant Analysis (PCA-LDA) for the automated classification of spectral data according to the shape of specific mid-IR spectral signatures. The diagnostic performances of the proposed spectral biomarkers, alone and combined, were evaluated using multivariate logistic regression followed by a Receiving Operator Curve analysis, obtaining large Areas Under the Curve (AUC = 0.91, 95% CI 0.81–1.0). Very interestingly, our analyses suggest that the discussed spectral biomarkers can outperform the classification ability of two widely used circulating HCC markers measured on the same groups of subjects, namely alpha-fetoprotein (AFP), and protein induced by the absence of vitamin K or antagonist-II (PIVKA-II). Full article
(This article belongs to the Special Issue Cancer Biomarker Research and Personalized Medicine 2.0)
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13 pages, 931 KiB  
Article
Associations of Complete Blood Count Parameters with Disease-Free Survival in Right- and Left-Sided Colorectal Cancer Patients
by Alhasan Alsalman, Mohammad A. Al-Mterin, Ala Abu-Dayeh, Ferial Alloush, Khaled Murshed and Eyad Elkord
J. Pers. Med. 2022, 12(5), 816; https://doi.org/10.3390/jpm12050816 - 18 May 2022
Cited by 9 | Viewed by 3084
Abstract
Colorectal cancer (CRC) is a leading cause of cancer-related deaths worldwide. Some complete blood count (CBC) parameters are found to be associated with CRC prognosis. In this study, ninety-seven pretreated CRC patients were included, and the patients were divided into two groups: left-sided [...] Read more.
Colorectal cancer (CRC) is a leading cause of cancer-related deaths worldwide. Some complete blood count (CBC) parameters are found to be associated with CRC prognosis. In this study, ninety-seven pretreated CRC patients were included, and the patients were divided into two groups: left-sided and right-sided, depending on the anatomical location of the tumor. Based on clinicopathologic features including tumor budding, disease stages, and tumor anatomical location, levels of CBC parameters were compared, and disease-free survivals (DFS) were determined. There were differences between patients with different tumor budding scores for only three parameters, including red cell distribution width (RDW), numbers of platelets, and mean platelet volume (MPV). Furthermore, numbers of WBCs, monocytes, and MPV in CRC patients with early disease stages were higher than those with advanced stages. However, levels of eosinophil in CRC patients with advanced stages were higher than those with early stages. Depending on the tumor anatomical location, we observed that numbers of red blood cells (RBCs), hemoglobin (Hgb), and hematocrit (Hct) in CRC patients with left-sided tumors were higher than those with right-sided tumors. We found that low levels of MPV were associated with shorter DFS. However, high levels of eosinophils were associated with shorter DFS in all CRC patients. When patients were divided based on the tumor anatomical location, higher levels of MPV, MCHC, and Hgb were associated with better DFS in the left-sided but not right-sided CRC patients. However, left-sided, but not right-sided, CRC patients with high levels of eosinophil and RDW had shorter DFS. Furthermore, right-sided, but not left-sided, CRC patients with high levels of platelets tended to have a shorter DFS. Our data show that MPV and eosinophils could serve as potential prognostic biomarkers in pre-treatment CRC patients, regardless of the tumor anatomical location. Additionally, lower levels of MPV, MCHC, and Hgb, and high levels of eosinophils and RDW could be negative predictive biomarkers in left-sided CRC patients. Full article
(This article belongs to the Special Issue Cancer Biomarker Research and Personalized Medicine 2.0)
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