Next Article in Journal
Prediction of Response to Anti-Angiogenic Treatment for Advanced Colorectal Cancer Patients: From Biological Factors to Functional Imaging
Next Article in Special Issue
snRNAs from Radical Prostatectomy Specimens Have the Potential to Serve as Prognostic Factors for Clinical Recurrence after Biochemical Recurrence in Patients with High-Risk Prostate Cancer
Previous Article in Journal
Breast Tumor Tissue Image Classification Using Single-Task Meta Learning with Auxiliary Network
Previous Article in Special Issue
The Detection and Negative Reversion of Circulating Tumor Cells as Prognostic Biomarkers for Metastatic Castration-Resistant Prostate Cancer with Bone Metastases Treated by Enzalutamide
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Cancers
Volume 16
Issue 7
10.3390/cancers16071363
cancers-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Systematic Review

Biomarkers for Pre-Treatment Risk Stratification of Prostate Cancer Patients: A Systematic Review

by
José Pedro Sequeira
1,2,3,
Sofia Salta
1,4,
Rui Freitas
1,5,
Rafael López-López
2,6,7,
Ángel Díaz-Lagares
2,7,8,
Rui Henrique
1,9,10 and
Carmen Jerónimo
1,9,10,*
1
Cancer Biology & Epigenetics Group, Research Center of IPO Porto (CI-IPOP)/CI-IPOP @RISE (Health Research Network), Portuguese Oncology Institute of Porto (IPO Porto)/Porto Comprehensive Cancer Center Raquel Seruca (Porto.CCC Raquel Seruca), R. Dr. António Bernardino de Almeida, 4200-072 Porto, Portugal
2
Epigenomics Unit, Cancer Epigenomics, Translational Medical Oncology Group (ONCOMET), Health Research Institute of Santiago de Compostela (IDIS), University Clinical Hospital of Santiago (CHUS/SERGAS), 15706 Santiago de Compostela, Spain
3
Doctoral Program in Biomedical Sciences, ICBAS-School of Medicine & Biomedical Sciences, University of Porto (ICBAS-UP), Rua Jorge Viterbo Ferreira 228, 4050-513 Porto, Portugal
4
Doctoral Program in Pathology and Molecular Genetics, ICBAS-School of Medicine & Biomedical Sciences, University of Porto (ICBAS-UP), Rua Jorge Viterbo Ferreira 228, 4050-513 Porto, Portugal
5
Department of Urology & Urology Clinic, Portuguese Oncology Institute of Porto (IPO Porto)/Porto Comprehensive Cancer Center Raquel Seruca (Porto.CCC), R. Dr. António Bernardino de Almeida, 4200-072 Porto, Portugal
6
Roche-Chus Joint Unit, Translational Medical Oncology Group (ONCOMET), Health Research Institute of Santiago (IDIS), 15706 Santiago de Compostela, Spain
7
Centro de Investigación Biomédica en Red Cáncer (CIBERONC), ISCIII, 28029 Madrid, Spain
8
Department of Clinical Analysis, University Hospital Complex of Santiago de Compostela (CHUS), 15706 Santiago de Compostela, Spain
9
Department of Pathology, Portuguese Oncology Institute of Porto (IPO Porto)/Porto Comprehensive Cancer Center Raquel Seruca (Porto.CCC), R. Dr. António Bernardino de Almeida, 4200-072 Porto, Portugal
10
Department of Pathology and Molecular Immunology, ICBAS-School of Medicine & Biomedical Sciences, University of Porto (ICBAS-UP), Rua Jorge Viterbo Ferreira 228, 4050-513 Porto, Portugal
*
Author to whom correspondence should be addressed.
Cancers 2024, 16(7), 1363; https://doi.org/10.3390/cancers16071363
Submission received: 28 February 2024 / Revised: 24 March 2024 / Accepted: 28 March 2024 / Published: 30 March 2024
(This article belongs to the Special Issue Biomarkers in Prostate Cancers)
Browse Figures
Versions Notes

Abstract

:

Simple Summary

PCa remains a leading health concern worldwide. Serum PSA-based PCa screening led to a well-documented decreased mortality but at the cost of the increased overdiagnosis/overtreatment of indolent disease. Although various tools have been developed to predict PCa patient outcome prior to treatment, mostly based on serum PSA, the Gleason score, and clinical T stage, all have a suboptimal performance and require tissue biopsies from the prostate. To obviate that need, overcome Gleason score subjectivity and the limited specificity of serum PSA, devising more effective tools is mandatory, while also taking the opportunity to adopt minimally invasive strategies based on liquid biopsies.

Abstract

Background: Prostate cancer (PCa) is one of the most frequently occurring malignancies. Although most cases are not life-threatening, approximately 20% endure an unfavorable outcome. PSA-based screening reduced mortality but at the cost of an increased overdiagnosis/overtreatment of low-risk (lrPCa) and favorable intermediate-risk (firPCa) PCa. PCa risk-groups are usually identified based on serum Prostate-Specific Antigen (PSA), the Gleason score, and clinical T stage, which have consistent although variable specificity or subjectivity. Thus, more effective and specific tools for risk assessment are needed, ideally making use of minimally invasive methods such as liquid biopsies. In this systematic review we assessed the clinical potential and analytical performance of liquid biopsy-based biomarkers for pre-treatment risk stratification of PCa patients. Methods: Studies that assessed PCa pre-treatment risk were retrieved from PubMed, Scopus, and MedLine. PCa risk biomarkers were analyzed, and the studies’ quality was assessed using the QUADAS-2 tool. Results: The final analysis comprised 24 full-text articles, in which case-control studies predominated, mostly reporting urine-based biomarkers (54.2%) and biomarker quantification by qPCR (41.7%). Categorization into risk groups was heterogeneous, predominantly making use of the Gleason score. Conclusion: This systematic review unveils the substantial clinical promise of using circulating biomarkers in assessing the risk for prostate cancer patients. However, the standardization of groups, categories, and biomarker validation are mandatory before this technique can be implemented. Circulating biomarkers might represent a viable alternative to currently available tools, obviating the need for tissue biopsies, and allowing for faster and more cost-effective testing, with superior analytical performance, specificity, and reproducibility.

1. Introduction

Prostate cancer (PCa) is the second most diagnosed cancer and the fifth leading cause of cancer death in men worldwide, accounting for 1,414,259 new cases and 375,304 deaths in 2020 [1]. In 2019, 2562 PCa cases were newly diagnosed and 1901 patients died from PCa in Northern Portugal [2]. Despite a relatively steady mortality over the past two decades, a trend for rising incidence has been observed, with more than 1.8 million new cases expected in 2030 [1]. Serum PSA-based screening detects ~90% of localized PCa [3]. However, PSA is not cancer-specific, leading to false-positives and consequent unnecessary biopsies, as well as an overdiagnosis of non-clinically significant PCa (ncsPCa) [4,5,6]. Additionally, PSA’s ability to monitor residual disease and predict biochemical recurrence (BCR) is rather ambiguous [7]. To improve PSA performance, tools for PCa diagnosis and the identification of clinically significant PCa, such as the prostate health index (PHI, based on −2proPSA, the percentage of free PSA, and the total PSA), 4k score (total PSA, free PSA, intact PSA, and human kallikrein 2), and PCA3 (a prostate-specific mRNA biomarker) have been reported [8]. Importantly, these three tools disclosed an area under the curve (AUC) that was superior to 0.70 [8,9].
PCa patients’ risk stratification is key to ensuring that adequate therapeutic decisions are made, considering the heterogenous outcomes [10]. Hence, over the years, several clinical risk-stratification tools have been proposed, among which the D’Amico Risk Classification is generally considered the gold standard. This system divides PCa patients into three different risk groups—low-risk (lrPCa), intermediate-risk (irPCa), and high-risk (hrPCa) PCa—according to serum PSA levels, Gleason score (GS) in the prostate biopsy, and clinical stage. Several upgrades to the D’Amico classification have been proposed, adding variables to the tool, such as more detailed clinicopathological information or basing classifications on risk scores or nomograms [11]. Comparing the most consensually used clinical tools [11,12,13,14,15,16,17,18,19], the Cambridge Prognostic Group’s (CPG) resulted in the highest concordance index (0.78) between the predicted and the verified PCa outcome among all of the non-nomograms tools [11,14]. According to the CPG tool, and contrary to the D’Amico system, PCa patients may be subdivided into five groups—lrPCa, favorable intermediate-risk PCa (firPCa), unfavorable intermediate-risk PCa (uirPCa), hrPCa, and very high-risk PCa (vhrPCa)—again based on GS, serum PSA, and clinical stage [14]. Nonetheless, the low specificity of serum PSA and the discordance between clinical and pathological T stage remain as weaknesses of this system. Importantly, stratification into distinct risk groups has become more important than ever, because lrPCa are usually determined to almost always be localized tumors, with a low likelihood of progression. Thus, these patients should be offered active surveillance instead of radiotherapy/prostatectomy, as no significant differences in mortality are found when comparing both strategies [20]. Moreover, identification of hrPCa’s patients allows for timely monitorization of the disease and the implementation of more accurate therapeutic decisions [21]. Nonetheless, pre-treatment risk stratification of PCa patients is complex, and several tools have been developed to overcome the inherent challenges, mainly strategies that include genomic markers. Tissue-based technologies have been reported, such as the Decipher score® [22], the Prolaris® cell cycle progression test [23], and the Oncotype DX® Prostate Score [24]. Moreover, Grönberg and colleagues have developed a new stratification system for hrPCa—the STHLM3 [4,6,25,26]. This is based on an algorithm computing age, family history, previous biopsy results, blood biomarkers, genetic markers, digital rectal exam (DRE), and prostate volume [4,6,25,26]. Although several updates to this model have been reported, increasing its accuracy [Area Under the Curve (AUC) = 75%], its specificity remains lower than desirable (50.0%), although it has achieved a sensitivity of 84.0% [27]. Owing to its low cost-effectiveness, its use has been mostly restricted to cases with serum PSA ≥ 3 ng/mL. This, however, might lead to an increase in undetected malignancies, since only patients aged 45–75 years and with PSA > 3 ng/mL will be indicated to receive a biopsy after repeat testing validation [6]. Notwithstanding the high potential of reported tools, their high cost constitutes a barrier to its implementation in routine clinical practice [6,28].
Tissue biopsies remain the gold standard for PCa diagnosis [29]. Despite tissue collection from the prostate having improved due to magnetic resonance imaging-guided biopsies [30], some downsides related to tissue biopsy remain, including challenges in collecting significant amounts of material, bias due to tumor heterogeneity, and procedural issues (with adverse effects for the patient) [29,31,32]. Thus, a need to better select the patients that will benefit from a magnetic resonance imaging-guided biopsy [30,32] or from the introduction of circulating biomarkers remains. In this context, liquid biopsies emerged as alternative strategies, attracting special attention from the scientific community due to their significant potential to unveil novel biomarkers and reduce the risk of complications associated with histological biopsies [33,34,35,36]. The search for new biomarkers in liquid biopsies that could mimic the clinical/pathological variables used in D’Amico-based tools might improve the concordance between the predicted and the real PCa outcome. Moreover, unnecessary biopsies could be obviated using circulating biomarkers [36,37].
To address some of these challenges, we performed a systematic review of published data on the pre-treatment risk assessment of PCa patients using liquid biopsy strategies. This enabled us to better understand the potential of circulating biomarkers for pre-treatment risk assessment and identify gaps which must be filled before effective translation into the clinical setting.

2. Materials and Methods

2.1. Study Outcomes, Search Strategy and Selection Process

Studies that reported circulating biomarkers for pre-treatment risk stratification of PCa patients were included in this systematic review. Pubmed, MedLine, and Scopus databases were searched for publications from inception up to 24 February 2023. Grey literature or other databases were not accessed. The search strategies are provided as Supplementary Materials (Supplementary File S1). Two independent authors (JPS and SS) screened all titles and abstracts; full texts of the remaining publications were obtained, and eligibility of the publications was assessed. When discrepancies were found, a third author (RF) screened the paper. After the final selection, each study was identified with a sequential ID code to facilitate the identification by the authors.

2.2. Inclusion and Exclusion Criteria

Since the aim of this systematic review was to synthetize the studies which analyzed circulating biomarkers, only works that reported biomarkers evaluated in liquid biopsies were included. Studies disclosing biomarkers for risk assessment after treatment were excluded from the analysis, as well as those which did not identify the variables used for risk categorization. Reports that focused their approach on a specific subset of samples [e.g., only analyzed metastatic castration-resistant PCa (mCRPC)] were also excluded. In this systematic review, only Portuguese or English original studies were included.

2.3. Data Collection

The data collection was performed in a standardized form by two independent authors (JPS and SS). Variables extracted included: biomarker, type of biomarker and liquid biopsy used, risk stratification variables, method for quantification, number of samples used, and analytical variables [AUC, sensitivity, specificity, Positive Predictive Value (PPV), Negative Predictive Value (NPV), and accuracy], when available.

2.4. Quality Assessment

Two authors (JPS and SS) assessed the quality of studies using the QUADAS-2 tool [38]. Bias was assessed based on: participants selection, index test description, reference test, and flow and timing as reported by Salta et al. [39]. Table 1 shows a quality assessment for all the included studies.
This systematic review was written according to the Preferred Reporting Items of Systematic Reviews and Meta-analysis of Diagnostic Test Accuracy Studies (PRISMA-DTA) guidelines [40,41] and was registered on the PROSPERO database at the Centre of Reviews and Dissemination, University of York, UK (registration number CRD42023455874) (https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=455874, accessed on 1 September 2023).
Table 1. Studies’ quality assessment according to QUADAS-2. QUADAS items: P1 = participant selection is fully described; P2 = a consecutive or random sample of patients was selected; P3 = a case-control design was avoided; P4 = study avoided inappropriate exclusions; T1 = the index test was well described; T2 = the index test results were interpreted without knowledge of the results of the reference standard; T3 = the threshold used was pre-specified; R1 = the reference test was well described; R2 = the reference standard (groups risk) was likely to classify the target correctly; R3 = the reference standard results were interpreted without knowledge of the results of the index test; F1 = appropriate interval between index and reference test; F2 = all the patients received a reference standard (i.e., all the patients had a risk group); F3 = all the patients received the same reference standard (i.e., all the patients had a risk group according to the same stratification tool); F4 = all the patients were included in the analysis. Y = fulfilled; N = not fulfilled; U = unclear. Regarding the risk of bias, patient selection showed the highest concerns regarding applicability and risk of bias, while the other variables presented low risk of bias. The analysis of the studies’ applicability unveiled a possible concern regarding index test and reference standard.
Table 1. Studies’ quality assessment according to QUADAS-2. QUADAS items: P1 = participant selection is fully described; P2 = a consecutive or random sample of patients was selected; P3 = a case-control design was avoided; P4 = study avoided inappropriate exclusions; T1 = the index test was well described; T2 = the index test results were interpreted without knowledge of the results of the reference standard; T3 = the threshold used was pre-specified; R1 = the reference test was well described; R2 = the reference standard (groups risk) was likely to classify the target correctly; R3 = the reference standard results were interpreted without knowledge of the results of the index test; F1 = appropriate interval between index and reference test; F2 = all the patients received a reference standard (i.e., all the patients had a risk group); F3 = all the patients received the same reference standard (i.e., all the patients had a risk group according to the same stratification tool); F4 = all the patients were included in the analysis. Y = fulfilled; N = not fulfilled; U = unclear. Regarding the risk of bias, patient selection showed the highest concerns regarding applicability and risk of bias, while the other variables presented low risk of bias. The analysis of the studies’ applicability unveiled a possible concern regarding index test and reference standard.
Author, Year, JournalType of BiomarkerRisk of BiasConcerns of Applicability
Patient SelectionIndex TestReference StandardFlow and TimingPatient SelectionIndex TestReference Standard
P1P2P3P4PT1T2T3TR1R2R3RF1F2F3F4F
Souza et al., 2020, Carcin [42]mRNAYNNNHighYYNLowYNYLowUYYNPossibleHighLowPossible
Connel et al., 2019, BJU Int [43]mRNAYYUYLowUUUHighYYYLowYNYNPossibleLowHighLow
Van Neste et al., 2016, Eur. Urol. [44]mRNAYYYYLowYYYLowYNYLowYYNYLowLowLowPossible
Alvarez-Cubero et al., 2023, Int. J. Mol. Sci. [45]mRNAYNNUHighYYULowYNYLowUNYNPossibleHighLowPossible
Connel et al., 2021, Cancers [46]mRNAYYYYLowYNYLowYNYLowYYYYLowLowPossiblePossible
Johnson et al., 2020, BMC Medicine [47]mRNAYYYYLowYYYLowYYULowYNUNHighLowLowLow
Rahnama’i et al., 2020, Cancer Reports [48]mRNAYYYYLowYNUPossibleYNYLowYYYYLowLowPossiblePossible
Ruiz-Plazas et al., 2021, Cancers [49]miRNAYYYNPossibleYNYLowYNYLowUYYYLowPossiblePossiblePossible
Martínez-González et al., 2021, Biomedicines [50]miRNAYUNUHighYNUPossibleNNYHighUYYUPossibleHighPossibleHigh
Ramirez-Garrastacho et al., 2021, Brit. J. Can. [51]miRNAYUUUHighYNUPossibleYNYLowUYYYLowHighPossiblePossible
Kim et al., 2021, Sci. Rep. [52]miRNAYNNNHighYNUPossibleYYYLowYYYYLowHighPossibleLow
Koo et al., 2018, Small [53]mRNA, miRNA, lncRNANUYUHighYNUPossibleYNYLowUYYYLowHighPossiblePossible
Miyoshi et al., 2022, BMC Cancer [54]ProteinYYYYLowYYYLowYNYLowUYYYLowLowLowPossible
Bhakdi et al., 2019, Cancers [55]ProteinYYYYLowYYULowYNYLowYNYNPossibleLowLowPossible
Delkov et al., 2022, Turk J Med Sci [56]ProteinYNNYPossibleYUUPossibleYYNLowUYYYLowPossiblePossibleLow
Mahmud et al., 2021, Anal. Chem. [57]ProteinNNNUHighYNUPossibleYNYLowUYYYLowHighPossiblePossible
Ankerst at al., 2015, BMC Urology [58]ProteinYNNYPossibleYYULowYNYLowYNYYLowPossibleLowPossible
Outzen et al., 2016, Brit. J. Nut. [59]ProteinYNNUHighYYYLowYNYLowYYYULowHighLowPossible
Goetze et al., 2022, Clin. Prot. [60]ProteinYYYYLowYUUPossibleYNYLowYYYYLowLowPossiblePossible
Chiu et al., 2021, Prost. Can. Prost. Dis. [61]ProteinNYYUHighYYNLowYNYLowYNYNPossibleHighPossiblePossible
Biggs et al., 2016, Oncotarget [62]ProteinNUNUHighYUUPossibleYNUPossibleYUUUHighHighPossiblePossible
Chen et al., 2022, Front. Immunol. [63]ProteinYUNYPossibleYYNLowYNYLowYYYYLowPossiblePossiblePossible
Brikun et al., 2019, Exp Hematol Oncol [64]DNA methylationYNNUHighYUUPossibleYYYLowUUUNHighHighPossibleLow
Connell et al., 2020, The Prostate [65]DNA methylationYYYYLowYNYLowYNYLowYYYYLowLowPossiblePossible

3. Results

3.1. Literature Overview

The literature search retrieved 227 papers from Scopus, 23 from PubMed, and 15 from Medline (Figure 1).
Among these studies, thirteen based their approach on urine samples [43,44,46,47,48,51,52,53,56,57,61,64,65], five on plasma [42,45,50,59,62], four on serum [54,58,60,63], and only one used whole blood [55] and semen [49]. Although most (10/24) were based on a quantitative Polymerase Chain Reaction (qPCR) [42,44,47,48,49,50,51,52,53,64], six used mass-spectrometry technology [54,56,57,58,60,61], two analyzed the biomarkers using Nanostring [43,65], and one used Nanostring and ELISA together [46]. Digital PCR [45], chemical analysis [59], haemocytometer and fluorescence microscopy analyses [55], nanoscale flow cytometer [62], and Luminex cytokine immunoassays [63] were reported by one study each.
The proteins DHEA [54], tCECs [55], GABA [56], Thymidine glycol [57], Sarcosine [58], Fibronectin [60], Vitronectin [60], Spermine [61], circulating prostate microparticles [62], the element Selenium [59], and the panel PHI/rPSA/fPSA/TRAIL/IL-10 [63] were reported as biomarkers for PCa risk stratification. Seven studies identified potential mRNAs that could be used as biomarkers for risk assessment [42,43,44,45,46,47,48], whereas only two studies reported DNA methylation as a tool to assess PCa pre-treatment risk [64,65]. Moreover, microRNAs (miRNAs) and the levels of miR-221-3p [49], miR-222-3p [49], miR-23c [50], miR-26a-5p [52], miR-532-5p [52], miR-99b-3p [52], miR-186-5p [51], miR-30e [51], and miR-320a-3p [51] were also assessed. Lastly, Koo and colleagues reported a panel composed of one mRNA, one miRNA, and one long non-coding RNA (lncRNA) (TMPRESS2:ERG, miR-107 and SChLAP1) [53] as potential risk biomarkers.
According to the risk assessment tools, the majority of the studies (16 papers) categorized risk taking into account the GS/International Society of Urological Pathology (ISUP) group grading [44,45,46,48,49,50,51,52,53,54,55,57,58,61,62,63,65]. One study based their approach on GS together with T stage [42], whereas three used those two variables in a panel that also included serum PSA [47,59,60]. Furthermore, risk assessment was only categorized according to serum PSA or presence of metastasis in one study [45]. Risk assessment tools such as the CAPRA Score [64], the European Association of Urology’s (EAU) risk tool [56], and the D’Amico system [43] were used in one study each.

3.2. Quality Assessment

The studies were evaluated for their risk of bias and concerns about their applicability using the QUADAS-2 tool (Figure 2 and Table 1).
The risk of bias was divided into patient selection, an index test, a reference standard, and flow/timing. About 46% showed a high patient selection bias, which was mainly associated with study design, whereas most of the studies presented a low bias for the index test, reference standard, and flow/timing. Regarding concerns about applicability, about 46% showed high concerns, whereas, for the index test and reference standard, 62.5% and 75% disclosed possible concerns about applicability, respectively.

3.3. Biomarkers Accuracy for PCa Pre-Treatment Risk Assessment

In Table 2, the analytical performance of the circulating biomarkers is depicted. Most studies only reported the AUC, ranging from 0.60 to 0.93 [42,43,44,45,46,47,49,51,52,60,61,63,65], with the maximum value reported by Johnson and colleagues using a 25-gene panel [47]. Considering only protein biomarkers (10/24), DHEA disclosed 96.0% specificity and 98.4% PPV, but only 33.7% sensitivity [54]. TCECs were more sensitive (71.0%), but their specificity and PPV were lower (63.0% and 18.0%, respectively) [55]. The panel PHI + tPSA + fPSA + TRAIL + IL-10 depicted a higher AUC which is usually reported for protein-based biomarkers (0.92) [53], while Spermine and the Fibronectin/Vitronectin panel reported an AUC of 0.66 [60,61]. Moreover, Sarcosine and Selenium status did not disclose significant differences between risk groups, being unsuitable for use as circulating biomarkers [58,59]. Unfortunately, the remaining five studies that reported on potentially relevant biomarkers did not state AUC values [54,55,56,57,62], and six studies did not present their analytical parameters [56,57,60,61,62,63]. Concerning mRNA biomarkers, the AUC range was 0.65–0.93 [42,43,44,45,46,47]. The DLX1 and HOXC6 panel was shown to have potential for pre-treatment risk stratification by two studies [44,48]. Rahnama’i and colleagues reported a specificity and PPV of 100%, although with quite a poor sensitivity and NPV (36.8% and 8.0%, respectively) [48], whereas the opposite was reported by Van Neste et al., who found a high sensitivity and NPV (91.0% and 94.0%, respectively) but only 36.0% specificity and 27.0% PPV [44]. MRC2 and S100A4 showed potential to stratify ISUP groups, serum PSA levels, and the presence of metastasis with an AUC range of 0.60–0.67, whereas the PCA3 and S100A4 panel only showed potential to stratify the last two groups with AUCs of 0.60 and 0.68, respectively [45]. All of the analytical parameters were shown to be higher than 58% for the GOLM1 + NKX3-1 + TRPM8 panel [42].
Furthermore, a smaller number of miRNAs-based studies reported potential biomarkers for pre-treatment risk assessment of PCa [49,50,51,52]. The miRNAs miR-221-3p and miR-222-3p, along with the cytokine sTWEAK, showed 85.7% sensitivity and 76.9% specificity [49]. The remaining three miRNAs-based studies did not disclose the values for the performance parameters [50,51,52]. Only two studies were based on DNA methylation [64,65] and one study reported a panel composed of an miRNA, a mRNA and lncRNA [53]. All three of these studies reported statistical differences between risk groups, but none disclosed the results for the performance parameters.

4. Discussion

Managing patients across the wide spectrum of PCa, ranging from indolent to metastatic and deadly disease, requires adequate tools for clinical risk assessment. Currently available tools, such as the D’Amico and CPG methods, make use of routinely available parameters but have suboptimal performance. Thus, novel tools based on biomarkers accessible in liquid biopsies remain an unmet need [66]. These novel tools must accurately discriminate between indolent and aggressive disease, minimizing overdiagnosis and overtreatment [67,68].
The use of non- or minimally invasive collection methods is key for the success of this new generation of risk stratification biomarkers. In this context, liquid biopsies constitute an optimal strategy to unveil biomarkers for the detection, stratification, prognostication, and monitoring of cancer [36,45,69]. Indeed, liquid biopsies enable the analysis of several types of biomolecules, including cell-free DNA/RNA, circulating tumor cells, microvesicles, and miRNAs, among others [36,37,45,69,70,71]. Compared to tissue biopsies, this minimally invasive technology better portrays tumor heterogeneity, being faster, more cost-effective, and enabling biomarkers assessment at multiple timepoints [70,72]. Based on these advantages and considering the limitations of existing tools, we conducted a systematic review on liquid biopsy-based biomarkers reported for pre-treatment risk stratification of PCa patients.
Focusing on the type of biomarkers assessed in the selected studies, biomarkers based on proteins, mRNA, miRNA, and DNA methylation have been reported. Among all these, proteins were clearly the most frequent type of biomarker reported, whereas DNA methylation was only addressed in two studies. In fact, all four types of biomarkers are considered to be very sensitive, enabling the detection of early events within carcinogenesis [37,39,73,74]. However, most protein-based biomarkers have not reached routine clinical practice due to failures in the validation step [75]. Regarding miRNAs, despite the well-stated potential application of miRNAs in algorithms for accurate cancer diagnosis, prognostication, and disease monitoring [76], the ubiquity of these biomolecules and the overlapping levels of them in cancer and control samples presents a major barrier to their application [77]. Moreover, notwithstanding the advantages of mRNA studies, underestimated pitfalls are reported for those biomarkers, precluding their wider use in the clinical context [78]. Concerning DNA methylation (me), this type of biomarker is the most studied epigenetic modification in humans and the first to be identified in cancer [36]. PCa-associated methylation changes are measurable in circulating cell-free DNA (in the three most studied liquid biopsies—urine, plasma, and serum), with GSTP1me being the most explored due to its high specificity (~90%) [79,80,81,82,83]. Moreover, DNA methylation-based markers may also have prognostic value [36]. Indeed, GSTP1me levels associated with ISUP grade and metastasis [84], together with RASSF2Ame, impacted non-organ-confined disease [80]. Of note, the only AUC reported in the DNA methylation-based studies included in this systematic review was 0.89, which unveils the potential of this type of marker. Despite the promising role of DNA methylation-based biomarkers, its role in PCa risk stratification remains poorly studied, entailing the need for more and larger studies. In fact, the reported results for pre-treatment risk stratification reveal that there is a long road ahead to finding a pre-treatment risk stratification biomarker that might be successfully introduced into clinical practice, and it is difficult at the moment to predict which type of marker will prove to be viable for that purpose.
There was also a diverse array of methods used to quantify the biomarkers, with 41.7% of the studies selecting qPCR, whereas only one used digital Polymerase Chain Reaction (dPCR). As previously described, dPCR allows for absolute quantification in a time- and cost-effective and accurate manner, which appears to improve biomarkers detection [37,85]. Additionally, the method’s precision and accuracy are increased by the time point of data collection [37,85,86]. This imbalance towards qPCR vs. dPCR probably derives from the longer experience which most labs have with the former technique, as dPCR is a more recent technology. Furthermore, multiplexing, which may be advantageous in this biomarker setting, is easier to accomplish in qPCR. Nonetheless, the unique characteristics of dPCR, including its superior sensitivity, make it a very attractive technology which may eventually rival qPCR. Nevertheless, more studies are required to better understand which biomarker and respective methodology will be useful for clinical practice.
Despite the great expectations for the biomarkers characterized in this systematic review, only two studies reported on performance [44,48]. Furthermore, biomarker diversity and the limited number of patients enrolled in each study without further multicentric validation remains a significative drawback to their implementation in clinical practice. Moreover, another major issue (and limitation) within the published studies results from the fact that only two studies based their approach on accepted clinical tools (the D’Amico-derived tool and a nomogram), with the majority using different strategies to those utilized in clinical practice to predict PCa patients’ outcome (e.g., only GS). The heterogeneity of clinical parameters used to stratify PCa pre-treatment risk reinforces the urgent need to unveil a highly accurate biomarker to standardize risk assessment. Nonetheless, this systematic review disclosed several interesting candidates, which are worthy of further investigation in larger and, ideally, multicentric patient cohorts.

5. Conclusions

Several studies have been published with the purpose of identifying novel biomarkers for pre-treatment risk stratification of PCa patients. Overall, our findings support the potential of liquid biopsies as a promising minimally invasive tool for assessing those biomarkers, obviating the limitations and disadvantages of tissue biopsies. However, current consensus on the role of circulating biomarkers for pre-treatment PCa risk-stratification considers these biomarkers as complementary rather than alternative to the existing tools, although these have clear limitations. Nonetheless, standardization of risk categories and of technical protocols is notoriously lacking and precludes the translation of research findings into clinical practice. Thus, a seemingly long road lies ahead before we can perform adequate validation studies and demonstrate the superiority of this novel strategy over the currently available tools.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/cancers16071363/s1, Supplementary Materials S1: Search strategies of the systematic review and abbreviations.

Author Contributions

Conceptualization, J.P.S., R.H. and C.J.; methodology, J.P.S. and S.S.; validation, R.F.; formal analysis, J.P.S. and S.S.; investigation, J.P.S., S.S. and R.F.; data curation, J.P.S. and S.S.; writing—original draft preparation, J.P.S. and S.S.; writing—review and editing, R.L.-L., Á.D.-L., R.H. and C.J.; supervision, [email protected] Á.D.-L., R.H. and C.J. All authors have read and agreed to the published version of the manuscript.

Funding

C.J. & R.H. research is funded by PI-189-CI-IPOP-22-2023-MethylProCancer granted by the Research Centre of Portuguese Oncology Institute of Porto. Á.D.-L. is funded by a contract “Juan Rodés” (JR17/00016) from “Instituto de Salud Carlos III” (ISCIII) and by Servizo Galego de Saúde (SERGAS). J.P.S. and S.S. are recipients of doctoral fellowships from FCT—Fundação para a Ciência e Tecnologia (2022.11060.BD and SFRH/BD/143717/2019, respectively).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Sung, H.; Ferlay, J.; Siegel, R.L.; Laversanne, M.; Soerjomataram, I.; Jemal, A.; Bray, F. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J. Clin. 2021, 71, 209–249. [Google Scholar] [CrossRef] [PubMed]
  2. Bento, M.J.; Rodrigues, J.; Leite Silva, P.; Calisto, R. Registo Oncológico Nacional de Todos os Tumores na População Residente em Portugal, em 2019. 2022. Available online: https://diariodarepublica.pt/dr/detalhe/despacho/13227-2023-835712442 (accessed on 1 April 2023).
  3. Cancer Genome Atlas Research Network. The Molecular Taxonomy of Primary Prostate Cancer. Cell 2015, 163, 1011–1025. [Google Scholar] [CrossRef]
  4. Grönberg, H.; Adolfsson, J.; Aly, M.; Nordström, T.; Wiklund, P.; Brandberg, Y.; Thompson, J.; Wiklund, F.; Lindberg, J.; Clements, M.; et al. Prostate cancer screening in men aged 50–69 years (STHLM3): A prospective population-based diagnostic study. Lancet Oncol. 2015, 16, 1667–1676. [Google Scholar] [CrossRef]
  5. Matoso, A.; Epstein, J.I. Defining clinically significant prostate cancer on the basis of pathological findings. Histopathology 2019, 74, 135–145. [Google Scholar] [CrossRef] [PubMed]
  6. Ström, P.; Nordström, T.; Aly, M.; Egevad, L.; Grönberg, H.; Eklund, M. The Stockholm-3 Model for Prostate Cancer Detection: Algorithm Update, Biomarker Contribution, and Reflex Test Potential. Eur. Urol. 2018, 74, 204–210. [Google Scholar] [CrossRef] [PubMed]
  7. Luo, C.; He, S.; Zhang, H.; He, S.; Qi, H.; Wei, A. Clinical and Biological Significance of DNA Methylation-Driven Differentially Expressed Genes in Biochemical Recurrence after Radical Prostatectomy. Front. Genet. 2022, 13, 727307. [Google Scholar] [CrossRef] [PubMed]
  8. Chen, J.-Y.; Wang, P.-Y.; Liu, M.-Z.; Lyu, F.; Ma, M.-W.; Ren, X.-Y.; Gao, X.-S. Biomarkers for Prostate Cancer: From Diagnosis to Treatment. Diagnostics 2023, 13, 3350. [Google Scholar] [CrossRef] [PubMed]
  9. Loeb, S.; Catalona, W.J. The Prostate Health Index: A new test for the detection of prostate cancer. Ther. Adv. Urol. 2014, 6, 74–77. [Google Scholar] [CrossRef] [PubMed]
  10. Popiolek, M.; Rider, J.R.; Andrén, O.; Andersson, S.-O.; Holmberg, L.; Adami, H.-O.; Johansson, J.-E. Natural History of Early, Localized Prostate Cancer: A Final Report from Three Decades of Follow-up. Eur. Urol. 2013, 63, 428–435. [Google Scholar] [CrossRef]
  11. Zelic, R.; Garmo, H.; Zugna, D.; Stattin, P.; Richiardi, L.; Akre, O.; Pettersson, A. Predicting Prostate Cancer Death with Different Pretreatment Risk Stratification Tools: A Head-to-head Comparison in a Nationwide Cohort Study. Eur. Urol. 2020, 77, 180–188. [Google Scholar] [CrossRef]
  12. Cooperberg, M.R.; Pasta, D.J.; Elkin, E.P.; Litwin, M.S.; Latini, D.M.; Du Chane, J.; Carroll, P.R. The University of California, San Francisco Cancer of the Prostate Risk Assessment score: A straightforward and reliable preoperative predictor of disease recurrence after radical prostatectomy. J. Urol. 2005, 173, 1938–1942. [Google Scholar] [CrossRef]
  13. D’Amico, A.V.; Whittington, R.; Malkowicz, S.B.; Schultz, D.; Blank, K.; Broderick, G.A.; Tomaszewski, J.E.; Renshaw, A.A.; Kaplan, I.; Beard, C.J.; et al. Biochemical outcome after radical prostatectomy, external beam radiation therapy, or interstitial radiation therapy for clinically localized prostate cancer. JAMA 1998, 280, 969–974. [Google Scholar] [CrossRef]
  14. Gnanapragasam, V.J.; Lophatananon, A.; Wright, K.A.; Muir, K.R.; Gavin, A.; Greenberg, D.C. Improving Clinical Risk Stratification at Diagnosis in Primary Prostate Cancer: A Prognostic Modelling Study. PLoS Med. 2016, 13, e1002063. [Google Scholar] [CrossRef]
  15. Graham, J.; Kirkbride, P.; Cann, K.; Hasler, E.; Prettyjohns, M. Prostate cancer: Summary of updated NICE guidance. BMJ 2014, 348, f7524. [Google Scholar] [CrossRef] [PubMed]
  16. Lukka, H.; Warde, P.; Pickles, T.; Morton, G.; Brundage, M.; Souhami, L. Controversies in prostate cancer radiotherapy: Consensus development. Can. J. Urol. 2001, 8, 1314–1322. [Google Scholar] [PubMed]
  17. Mohler, J.L.; Armstrong, A.J.; Bahnson, R.R.; D’Amico, A.V.; Davis, B.J.; Eastham, J.A.; Enke, C.A.; Farrington, T.A.; Higano, C.S.; Horwitz, E.M.; et al. Prostate Cancer, Version 1.2016. J. Natl. Compr. Cancer Netw. 2016, 14, 19–30. [Google Scholar] [CrossRef]
  18. Mottet, N.; Bellmunt, J.; Bolla, M.; Briers, E.; Cumberbatch, M.G.; De Santis, M.; Fossati, N.; Gross, T.; Henry, A.M.; Joniau, S.; et al. EAU-ESTRO-SIOG Guidelines on Prostate Cancer. Part 1: Screening, Diagnosis, and Local Treatment with Curative Intent. Eur. Urol. 2017, 71, 618–629. [Google Scholar] [CrossRef]
  19. Sanda, M.G.; Cadeddu, J.A.; Kirkby, E.; Chen, R.C.; Crispino, T.; Fontanarosa, J.; Freedland, S.J.; Greene, K.; Klotz, L.H.; Makarov, D.V.; et al. Clinically Localized Prostate Cancer: AUA/ASTRO/SUO Guideline. Part I: Risk Stratification, Shared Decision Making, and Care Options. J. Urol. 2018, 199, 683–690. [Google Scholar] [CrossRef] [PubMed]
  20. Institute for Quality and Efficiency in Health Care (IQWiG), Localized Prostate Cancer: Low-Risk Prostate Cancer: Active Surveillance or Treatment? Available online: https://www.ncbi.nlm.nih.gov/books/NBK487255/ (accessed on 14 March 2023).
  21. McKay, R.R.; Feng, F.Y.; Wang, A.Y.; Wallis, C.J.D.; Moses, K.A. Recent Advances in the Management of High-Risk Localized Prostate Cancer: Local Therapy, Systemic Therapy, and Biomarkers to Guide Treatment Decisions. Am. Soc. Clin. Oncol. Educ. Book 2020, 40, e241–e252. [Google Scholar] [CrossRef]
  22. Farha, M.W.; Salami, S.S. Biomarkers for prostate cancer detection and risk stratification. Ther. Adv. Urol. 2022, 14, 17562872221103988. [Google Scholar] [CrossRef]
  23. Health Quality Ontario. Prolaris Cell Cycle Progression Test for Localized Prostate Cancer: A Health Technology Assessment. Ont. Health Technol. Assess Ser. 2017, 17, 1–75. [Google Scholar]
  24. Knezevic, D.; Goddard, A.D.; Natraj, N.; Cherbavaz, D.B.; Clark-Langone, K.M.; Snable, J.; Watson, D.; Falzarano, S.M.; Magi-Galluzzi, C.; Klein, E.A.; et al. Analytical validation of the Oncotype DX prostate cancer assay—A clinical RT-PCR assay optimized for prostate needle biopsies. BMC Genom. 2013, 14, 690. [Google Scholar] [CrossRef] [PubMed]
  25. Eklund, M.; Nordström, T.; Aly, M.; Adolfsson, J.; Wiklund, P.; Brandberg, Y.; Thompson, J.; Wiklund, F.; Lindberg, J.; Presti, J.C.; et al. The Stockholm-3 (STHLM3) Model can Improve Prostate Cancer Diagnostics in Men Aged 50–69 yr Compared with Current Prostate Cancer Testing. Eur. Urol. Focus 2018, 4, 707–710. [Google Scholar] [CrossRef] [PubMed]
  26. Nordström, T.; Eklund, M.; Grönberg, H. Future directions in prostate cancer testing: A comment upon results from the prospective population-based diagnostic study. Lancet Oncol. 2015, 16, 1667–1676. [Google Scholar] [CrossRef]
  27. Risør, B.W.; Tayyari Dehbarez, N.; Fredsøe, J.; Sørensen, K.D.; Pedersen, B.G. Cost-Effectiveness Analysis of Stockholm 3 Testing Compared to PSA as the Primary Blood Test in the Prostate Cancer Diagnostic Pathway: A Decision Tree Approach. Appl. Health Econ. Health Policy 2022, 20, 867–880. [Google Scholar] [CrossRef] [PubMed]
  28. Eggener, S.E.; Rumble, R.B.; Armstrong, A.J.; Morgan, T.M.; Crispino, T.; Cornford, P.; van der Kwast, T.; Grignon, D.J.; Rai, A.J.; Agarwal, N.; et al. Molecular Biomarkers in Localized Prostate Cancer: ASCO Guideline. J. Clin. Oncol. 2020, 38, 1474–1494. [Google Scholar] [CrossRef] [PubMed]
  29. Rawla, P. Epidemiology of Prostate Cancer. World J. Oncol. 2019, 10, 63–89. [Google Scholar] [CrossRef] [PubMed]
  30. Fernandes, M.C.; Yildirim, O.; Woo, S.; Vargas, H.A.; Hricak, H. The role of MRI in prostate cancer: Current and future directions. Magn. Reson. Mater. Phys. Biol. Med. 2022, 35, 503–521. [Google Scholar] [CrossRef] [PubMed]
  31. Hirahata, T.; Ul Quraish, R.; Quraish, A.U.; Ul Quraish, S.; Naz, M.; Razzaq, M.A. Liquid Biopsy: A Distinctive Approach to the Diagnosis and Prognosis of Cancer. Cancer Inform. 2022, 21, 11769351221076062. [Google Scholar] [CrossRef]
  32. Sosnowski, R.; Zagrodzka, M.; Borkowski, T. The limitations of multiparametric magnetic resonance imaging also must be borne in mind. Cent. European J. Urol. 2016, 69, 22–23. [Google Scholar] [CrossRef]
  33. Lianidou, E. Detection and relevance of epigenetic markers on ctDNA: Recent advances and future outlook. Mol. Oncol. 2021, 15, 1683–1700. [Google Scholar] [CrossRef] [PubMed]
  34. Palanca-Ballester, C.; Rodriguez-Casanova, A.; Torres, S.; Calabuig-Fariñas, S.; Exposito, F.; Serrano, D.; Redin, E.; Valencia, K.; Jantus-Lewintre, E.; Diaz-Lagares, A.; et al. Cancer Epigenetic Biomarkers in Liquid Biopsy for High Incidence Malignancies. Cancers 2021, 13, 3016. [Google Scholar] [CrossRef] [PubMed]
  35. Silva, R.; Moran, B.; Russell, N.M.; Fahey, C.; Vlajnic, T.; Manecksha, R.P.; Finn, S.P.; Brennan, D.J.; Gallagher, W.M.; Perry, A.S. Evaluating liquid biopsies for methylomic profiling of prostate cancer. Epigenetics 2020, 15, 715–727. [Google Scholar] [CrossRef] [PubMed]
  36. Constâncio, V.; Nunes, S.P.; Henrique, R.; Jerónimo, C. DNA Methylation-Based Testing in Liquid Biopsies as Detection and Prognostic Biomarkers for the Four Major Cancer Types. Cells 2020, 9, 624. [Google Scholar] [CrossRef] [PubMed]
  37. Sequeira, J.P.; Barros-Silva, D.; Ferreira-Torre, P.; Salta, S.; Braga, I.; Carvalho, J.; Freitas, R.; Henrique, R.; Jerónimo, C. OncoUroMiR: Circulating miRNAs for Detection and Discrimination of the Main Urological Cancers Using a ddPCR-Based Approach. Int. J. Mol. Sci. 2023, 24, 13890. [Google Scholar] [CrossRef] [PubMed]
  38. Whiting, P.F.; Rutjes, A.W.; Westwood, M.E.; Mallett, S.; Deeks, J.J.; Reitsma, J.B.; Leeflang, M.M.; Sterne, J.A.; Bossuyt, P.M.; Group, Q. QUADAS-2: A revised tool for the quality assessment of diagnostic accuracy studies. Ann. Intern. Med. 2011, 155, 529–536. [Google Scholar] [CrossRef] [PubMed]
  39. Salta, S.; Lobo, J.; Magalhães, B.; Henrique, R.; Jerónimo, C. DNA methylation as a triage marker for colposcopy referral in HPV-based cervical cancer screening: A systematic review and meta-analysis. Clin. Epigenetics 2023, 15, 125. [Google Scholar] [CrossRef] [PubMed]
  40. McInnes, M.D.F.; Moher, D.; Thombs, B.D.; McGrath, T.A.; Bossuyt, P.M.; Clifford, T.; Cohen, J.F.; Deeks, J.J.; Gatsonis, C.; Hooft, L.; et al. Preferred Reporting Items for a Systematic Review and Meta-analysis of Diagnostic Test Accuracy Studies: The PRISMA-DTA Statement. JAMA 2018, 319, 388–396. [Google Scholar] [CrossRef] [PubMed]
  41. Salameh, J.P.; Bossuyt, P.M.; McGrath, T.A.; Thombs, B.D.; Hyde, C.J.; Macaskill, P.; Deeks, J.J.; Leeflang, M.; Korevaar, D.A.; Whiting, P.; et al. Preferred reporting items for systematic review and meta-analysis of diagnostic test accuracy studies (PRISMA-DTA): Explanation, elaboration, and checklist. BMJ 2020, 370, m2632. [Google Scholar] [CrossRef]
  42. De Souza, M.F.; Kuasne, H.; Barros-Filho, M.D.C.; Cilião, H.L.; Marchi, F.A.; Fuganti, P.E.; Rogatto, S.R.; Cólus, I.M.D.S. Circulating mRNA signature as a marker for high-risk prostate cancer. Carcinogenesis 2020, 41, 139–145. [Google Scholar] [CrossRef]
  43. Connell, S.P.; Yazbek-Hanna, M.; McCarthy, F.; Hurst, R.; Webb, M.; Curley, H.; Walker, H.; Mills, R.; Ball, R.Y.; Sanda, M.G.; et al. A four-group urine risk classifier for predicting outcomes in patients with prostate cancer. BJU Int. 2019, 124, 609–620. [Google Scholar] [CrossRef] [PubMed]
  44. Van Neste, L.; Hendriks, R.J.; Dijkstra, S.; Trooskens, G.; Cornel, E.B.; Jannink, S.A.; de Jong, H.; Hessels, D.; Smit, F.P.; Melchers, W.J.G.; et al. Detection of High-grade Prostate Cancer Using a Urinary Molecular Biomarker–Based Risk Score. Eur. Urol. 2016, 70, 740–748. [Google Scholar] [CrossRef] [PubMed]
  45. Alvarez-Cubero, M.J.; Arance, E.; de Santiago, E.; Sanchez, P.; Sepúlveda, M.R.; Marrero, R.; Lorente, J.A.; Gonzalez-Cabezuelo, J.M.; Cuenca-Lopez, S.; Cozar, J.M.; et al. Follow-Up Biomarkers in the Evolution of Prostate Cancer, Levels of S100A4 as a Detector in Plasma. Int. J. Mol. Sci. 2022, 24, 547. [Google Scholar] [CrossRef]
  46. Connell, S.P.; Mills, R.; Pandha, H.; Morgan, R.; Cooper, C.S.; Clark, J.; Brewer, D.S.; The Movember GAP1 Urine Biomarker Consortium. Integration of Urinary EN2 Protein & Cell-Free RNA Data in the Development of a Multivariable Risk Model for the Detection of Prostate Cancer Prior to Biopsy. Cancers 2021, 13, 2102. [Google Scholar] [CrossRef] [PubMed]
  47. Johnson, H.; Guo, J.; Zhang, X.; Zhang, H.; Simoulis, A.; Wu, A.H.B.; Xia, T.; Li, F.; Tan, W.; Johnson, A.; et al. Development and validation of a 25-Gene Panel urine test for prostate cancer diagnosis and potential treatment follow-up. BMC Med. 2020, 18, 376. [Google Scholar] [CrossRef]
  48. Rahnama’i, M.S.; Bach, C.; Schulze-Hagen, M.; Kuhl, C.K.; Vögeli, T.A. Can the predictive value of multiparametric MRI for prostate cancer be improved by a liquid biopsy with SelectMDx? Cancer Rep. 2021, 4, e1396. [Google Scholar] [CrossRef] [PubMed]
  49. Ruiz-Plazas, X.; Altuna-Coy, A.; Alves-Santiago, M.; Vila-Barja, J.; García-Fontgivell, J.F.; Martínez-González, S.; Segarra-Tomás, J.; Chacón, M.R. Liquid Biopsy-Based Exo-oncomiRNAs Can Predict Prostate Cancer Aggressiveness. Cancers 2021, 13, 250. [Google Scholar] [CrossRef] [PubMed]
  50. Martínez-González, L.J.; Sánchez-Conde, V.; González-Cabezuelo, J.M.; Antunez-Rodríguez, A.; Andrés-León, E.; Robles-Fernandez, I.; Lorente, J.A.; Vázquez-Alonso, F.; Alvarez-Cubero, M.J. Identification of MicroRNAs as Viable Aggressiveness Biomarkers for Prostate Cancer. Biomedicines 2021, 9, 646. [Google Scholar] [CrossRef] [PubMed]
  51. Ramirez-Garrastacho, M.; Berge, V.; Linē, A.; Llorente, A. Potential of miRNAs in urinary extracellular vesicles for management of active surveillance in prostate cancer patients. Br. J. Cancer 2022, 126, 492–501. [Google Scholar] [CrossRef]
  52. Kim, M.Y.; Shin, H.; Moon, H.W.; Park, Y.H.; Park, J.; Lee, J.Y. Urinary exosomal microRNA profiling in intermediate-risk prostate cancer. Sci. Rep. 2021, 11, 7355. [Google Scholar] [CrossRef]
  53. Koo, K.M.; Dey, S.; Trau, M. Amplification-Free Multi-RNA-Type Profiling for Cancer Risk Stratification via Alternating Current Electrohydrodynamic Nanomixing. Small 2018, 14, e1704025. [Google Scholar] [CrossRef] [PubMed]
  54. Miyoshi, Y.; Kawahara, T.; Uemura, H. A high serum dehydroepiandrosterone concentration is a predictor of candidates for active surveillance in men with serum prostate-specific antigen < 10 ng/mL. BMC Cancer 2022, 22, 1169. [Google Scholar] [CrossRef]
  55. Bhakdi, S.C.; Suriyaphol, P.; Thaicharoen, P.; Grote, S.T.K.; Komoltri, C.; Chaiyaprasithi, B.; Charnkaew, K. Accuracy of Tumour-Associated Circulating Endothelial Cells as a Screening Biomarker for Clinically Significant Prostate Cancer. Cancers 2019, 11, 1064. [Google Scholar] [CrossRef] [PubMed]
  56. Delkov, D.; Yoanıdu, L.; Tomov, D.; Stoyanova, R.; Dechev, I.; Uzunova, Y. Oncometabolites in urine—A new opportunity for detection and prognosis of the clinical progress of verified prostate cancer-a pilot study. Turk. J. Med. Sci. 2022, 52, 699–706. [Google Scholar] [CrossRef] [PubMed]
  57. Mahmud, I.; Pinto, F.G.; Rubio, V.Y.; Lee, B.; Pavlovich, C.P.; Perera, R.J.; Garrett, T.J. Rapid Diagnosis of Prostate Cancer Disease Progression Using Paper Spray Ionization Mass Spectrometry. Anal. Chem. 2021, 93, 7774–7780. [Google Scholar] [CrossRef] [PubMed]
  58. Ankerst, D.P.; Liss, M.; Zapata, D.; Hoefler, J.; Thompson, I.M.; Leach, R.J. A case control study of sarcosine as an early prostate cancer detection biomarker. BMC Urol. 2015, 15, 99. [Google Scholar] [CrossRef] [PubMed]
  59. Outzen, M.; Tjønneland, A.; Larsen, E.H.; Friis, S.; Larsen, S.B.; Christensen, J.; Overvad, K.; Olsen, A. Selenium status and risk of prostate cancer in a Danish population. Br. J. Nutr. 2016, 115, 1669–1677. [Google Scholar] [CrossRef] [PubMed]
  60. Goetze, S.; Schüffler, P.; Athanasiou, A.; Koetemann, A.; Poyet, C.; Fankhauser, C.D.; Wild, P.J.; Schiess, R.; Wollscheid, B. Use of MS-GUIDE for identification of protein biomarkers for risk stratification of patients with prostate cancer. Clin. Proteom. 2022, 19, 9. [Google Scholar] [CrossRef] [PubMed]
  61. Chiu, P.K.; Fung, Y.H.; Teoh, J.Y.; Chan, C.H.; Lo, K.L.; Li, K.M.; Tse, R.T.; Leung, C.H.; Wong, Y.P.; Roobol, M.J.; et al. Urine spermine and multivariable Spermine Risk Score predict high-grade prostate cancer. Prostate Cancer Prostatic Dis. 2021, 24, 542–548. [Google Scholar] [CrossRef]
  62. Biggs, C.N.; Siddiqui, K.M.; Al-Zahrani, A.A.; Pardhan, S.; Brett, S.I.; Guo, Q.Q.; Yang, J.; Wolf, P.; Power, N.E.; Durfee, P.N.; et al. Prostate extracellular vesicles in patient plasma as a liquid biopsy platform for prostate cancer using nanoscale flow cytometry. Oncotarget 2016, 7, 8839–8849. [Google Scholar] [CrossRef]
  63. Chen, H.; Zhou, J.; Luo, J.; Wu, Y.; Qian, Y.; Shi, Y.; Qu, F.; Shi, B.; Ding, J.; Cui, X. Serum multi-cytokines screening identifies TRAIL and IL-10 as probable new biomarkers for prostate health index diagnostic utility adjustment in grey zone aggressive prostate cancer detection: A single-center data in China. Front. Immunol. 2022, 13, 901176. [Google Scholar] [CrossRef] [PubMed]
  64. Brikun, I.; Nusskern, D.; Freije, D. An expanded biomarker panel for the detection of prostate cancer from urine DNA. Exp. Hematol. Oncol. 2019, 8, 13. [Google Scholar] [CrossRef] [PubMed]
  65. Connell, S.P.; O’Reilly, E.; Tuzova, A.; Webb, M.; Hurst, R.; Mills, R.; Zhao, F.; Bapat, B.; Cooper, C.S.; Perry, A.S.; et al. Development of a multivariable risk model integrating urinary cell DNA methylation and cell-free RNA data for the detection of significant prostate cancer. Prostate 2020, 80, 547–558. [Google Scholar] [CrossRef] [PubMed]
  66. Mello-Grand, M.; Bruno, A.; Sacchetto, L.; Cristoni, S.; Gregnanin, I.; Dematteis, A.; Zitella, A.; Gontero, P.; Peraldo-Neia, C.; Ricotta, R. Two novel ceramide-like molecules and miR-5100 levels as biomarkers improve prediction of prostate cancer in gray-zone PSA. Front. Oncol. 2021, 11, 769158. [Google Scholar] [CrossRef] [PubMed]
  67. Drago, D.; Andolfo, A.; Mosca, E.; Orro, A.; Nocera, L.; Cucchiara, V.; Bellone, M.; Montorsi, F.; Briganti, A. A novel expressed prostatic secretion (EPS)-urine metabolomic signature for the diagnosis of clinically significant prostate cancer. Cancer Biol. Med. 2021, 18, 604–615. [Google Scholar] [CrossRef] [PubMed]
  68. Shin, S.; Park, Y.H.; Jung, S.-H.; Jang, S.-H.; Kim, M.Y.; Lee, J.Y.; Chung, Y.J. Urinary exosome microRNA signatures as a noninvasive prognostic biomarker for prostate cancer. Npj Genom. Med. 2021, 6, 45. [Google Scholar] [CrossRef]
  69. Constâncio, V.; Barros-Silva, D.; Jerónimo, C.; Henrique, R. Known epigenetic biomarkers for prostate cancer detection and management: Exploring the potential of blood-based liquid biopsies. Expert Rev. Mol. Diagn. 2019, 19, 367–375. [Google Scholar] [CrossRef]
  70. Sequeira, J.P.; Constâncio, V.; Lobo, J.; Henrique, R.; Jerónimo, C. Unveiling the World of Circulating and Exosomal microRNAs in Renal Cell Carcinoma. Cancers 2021, 13, 5252. [Google Scholar] [CrossRef] [PubMed]
  71. Di Meo, A.; Bartlett, J.; Cheng, Y.; Pasic, M.D.; Yousef, G.M. Liquid biopsy: A step forward towards precision medicine in urologic malignancies. Mol. Cancer 2017, 16, 80. [Google Scholar] [CrossRef]
  72. Hou, J.; Li, X.; Xie, K.P. Coupled liquid biopsy and bioinformatics for pancreatic cancer early detection and precision prognostication. Mol. Cancer 2021, 20, 34. [Google Scholar] [CrossRef]
  73. Alharbi, R.A. Proteomics approach and techniques in identification of reliable biomarkers for diseases. Saudi J. Biol. Sci. 2020, 27, 968–974. [Google Scholar] [CrossRef] [PubMed]
  74. Čelešnik, H.; Potočnik, U. Blood-Based mRNA Tests as Emerging Diagnostic Tools for Personalised Medicine in Breast Cancer. Cancers 2023, 15, 1087. [Google Scholar] [CrossRef] [PubMed]
  75. Hanash, S.M. Why have protein biomarkers not reached the clinic? Genome Med. 2011, 3, 66. [Google Scholar] [CrossRef]
  76. 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. [Google Scholar] [CrossRef]
  77. 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]
  78. Rázga, F.; Némethová, V. Gene expression patterns as predictive biomarkers in hematology-oncology: Principal hurdles on the road to the clinic. Haematologica 2017, 102, e31–e32. [Google Scholar] [CrossRef] [PubMed]
  79. Ellinger, J.; Haan, K.; Heukamp, L.C.; Kahl, P.; Büttner, R.; Müller, S.C.; von Ruecker, A.; Bastian, P.J. CpG Island hypermethylation in cell-free serum DNA identifies patients with localized prostate cancer. Prostate 2008, 68, 42–49. [Google Scholar] [CrossRef] [PubMed]
  80. Payne, S.R.; Serth, J.; Schostak, M.; Kamradt, J.; Strauss, A.; Thelen, P.; Model, F.; Day, J.K.; Liebenberg, V.; Morotti, A.; et al. DNA methylation biomarkers of prostate cancer: Confirmation of candidates and evidence urine is the most sensitive body fluid for non-invasive detection. Prostate 2009, 69, 1257–1269. [Google Scholar] [CrossRef]
  81. Sunami, E.; Shinozaki, M.; Higano, C.S.; Wollman, R.; Dorff, T.B.; Tucker, S.J.; Martinez, S.R.; Singer, F.R.; Hoon, D.S.B. Multimarker Circulating DNA Assay for Assessing Blood of Prostate Cancer Patients. Clin. Chem. 2009, 55, 559–567. [Google Scholar] [CrossRef]
  82. Wu, T.; Giovannucci, E.; Welge, J.; Mallick, P.; Tang, W.Y.; Ho, S.M. Measurement of GSTP1 promoter methylation in body fluids may complement PSA screening: A meta-analysis. Br. J. Cancer 2011, 105, 65–73. [Google Scholar] [CrossRef]
  83. Dumache, R.; Puiu, M.; Motoc, M.; Vernic, C.; Dumitrascu, V. Prostate cancer molecular detection in plasma samples by glutathione S-transferase P1 (GSTP1) methylation analysis. Clin. Lab. 2014, 60, 847–852. [Google Scholar] [CrossRef] [PubMed]
  84. Reibenwein, J.; Pils, D.; Horak, P.; Tomicek, B.; Goldner, G.; Worel, N.; Elandt, K.; Krainer, M. Promoter hypermethylation of GSTP1, AR, and 14-3-3σ in serum of prostate cancer patients and its clinical relevance. Prostate 2007, 67, 427–432. [Google Scholar] [CrossRef] [PubMed]
  85. Taylor, S.C.; Laperriere, G.; Germain, H. Droplet Digital PCR versus qPCR for gene expression analysis with low abundant targets: From variable nonsense to publication quality data. Sci. Rep. 2017, 7, 2409. [Google Scholar] [CrossRef]
  86. Sequeira, J.P.; Lobo, J.; Constâncio, V.; Brito-Rocha, T.; Carvalho-Maia, C.; Braga, I.; Maurício, J.; Henrique, R.; Jerónimo, C. DigiMir Test: Establishing a Novel Pipeline for MiR-371a Quantification Using Droplet Digital PCR in Liquid Biopsies From Testicular Germ Cell Tumor Patients. Front. Oncol. 2022, 12, 876732. [Google Scholar] [CrossRef] [PubMed]
Cancers 16 01363 g001
Figure 1. Flowchart of the literature analyzed in the study. From the 265 articles retrieved, 236 articles were screened and 31 full texts were assessed. A final list of 24 studies was included in the systematic review.
Figure 1. Flowchart of the literature analyzed in the study. From the 265 articles retrieved, 236 articles were screened and 31 full texts were assessed. A final list of 24 studies was included in the systematic review.
Cancers 16 01363 g001
Cancers 16 01363 g002
Figure 2. QUADAS-2 tool for assessment of the studies’ quality. Left panel represents the risk of bias and the right one the concerns regarding applicability.
Figure 2. QUADAS-2 tool for assessment of the studies’ quality. Left panel represents the risk of bias and the right one the concerns regarding applicability.
Cancers 16 01363 g002
Table 2. Studies’ characterization. A high heterogeneity in biomarkers and technologies used can be observed. However, it is possible to identify a tendency towards urine-based biomarkers, especially protein biomarkers, with qPCR being the most used technology. Abbreviations: AUC—area under the curve; BCR—biochemical recurrence; BPH—benign prostatic hyperplasia; csPCa—clinically significant Prostate Cancer; dPCR—digital Polymerase Chain Reaction; DRE—digital rectal exam; EAU—European Association of Urology; ISUP—International Society of Urological Pathology (ISUP) group grading; miRNA—microRNA; NA—Not applicable; ncsPCa—non clinically significant Prostate Cancer; NPV—negative predictive value; PPV—positive predictive value; pT—pathological T; qPCR—quantitative Polymerase Chain Reaction; RP—radical prostatectomy; Se—sensitivity; Sp—specificity.
Table 2. Studies’ characterization. A high heterogeneity in biomarkers and technologies used can be observed. However, it is possible to identify a tendency towards urine-based biomarkers, especially protein biomarkers, with qPCR being the most used technology. Abbreviations: AUC—area under the curve; BCR—biochemical recurrence; BPH—benign prostatic hyperplasia; csPCa—clinically significant Prostate Cancer; dPCR—digital Polymerase Chain Reaction; DRE—digital rectal exam; EAU—European Association of Urology; ISUP—International Society of Urological Pathology (ISUP) group grading; miRNA—microRNA; NA—Not applicable; ncsPCa—non clinically significant Prostate Cancer; NPV—negative predictive value; PPV—positive predictive value; pT—pathological T; qPCR—quantitative Polymerase Chain Reaction; RP—radical prostatectomy; Se—sensitivity; Sp—specificity.
Author, Year, JournalType of BiomarkerBiomarkerType of Liquid BiopsiesRisk StratificationMethodSampleAUCSe %Sp %PPV %NPV %
Souza et al., 2020, Carcin [42]mRNAGOLM1 + NKX3-1 + TRPM8PlasmaISUP < 4 and tumor stage < pT3a vs. ISUP ≥ 4 and/or tumor stage ≥ pT3aqPCR60 patients who have undergone RP0.7685.058.061.083.0
Connel et al., 2019, BJU Int [43]mRNAPUR (Prostate Urine Risk) panel—37 genesUrineNormal tissue vs. D’amico low-risk vs. D’Amico intermediate-risk vs. D’Amico High RiskNanostring535 first-catch post-DRE collected at diagnosis0.72NANANANA
Van Neste et al., 2016, Eur. Urol. [44]mRNAHOXC6 + DLX1UrineISUP = 1 vs. ISUP ≥ 2qPCRDiscovery set: 519 patients; Validation set: 386 patientsDS: 0.76; VS: 0.73DS: 91.0DS: 36.0DS: 27.0DS:94.0
Alvarez-Cubero et al., 2023, Int. J. Mol. Sci. [45]mRNAMRC2 + S100A4PlasmaISUP 1, 2 vs. ISUP 3, 4, 5dPCR20 patients with ISUP 1, 2 and 31 patients with ISUP 3, 4, 50.6564.565.074.154.2
PSA < 20 ng/mL vs. PSA > 20 ng/mL26 patients with PSA < 20 ng/mL and 25 patients with PSA > 20 ng/mL0.6020.0100.0100.056.5
No metastasis vs. Metastasis12 patients without metastasis and 34 patients with metastasis0.6758.875.087.039.1
PCA3 + S100A4PSA < 20 vs. PSA > 2027 patients with PSA < 20 ng/mL and 29 patients with PSA > 20 ng/mL0.6027.692.680.054.3
No metastasis vs. Metastasis16 patients without metastasis and 38 patients with metastasis0.6873.762.582.450.0
Connel et al., 2021, Cancers [46]mRNA167 gene probes for cell-free RNAUrineISUP = 0 vs. ISUP = 1 vs. ISUP ≥ 2NanoString and ELISA207 first-catch post-DRE (77 no cancer finding, 130 PCa)0.89NANANANA
Johnson et al., 2020, BMC Medicine [47]mRNA25-gene panelUrineISUP > 2, staging ≥ T3, PSA > 20, biochemical recurrence after prostatectomy, metastasis at diagnosis/follow-up vs. otherqPCR163 ISUP = 1, 273 ISUP = 2 and 292 ISUP ≥ 30.93NANANANA
Rahnama’i et al., 2020, Cancer Reports [48]mRNADLX1 + HOXC6UrineISUP < 2 vs. ISUP ≥ 2qPCR39 PCa (1 with ISUP < 2 and 38 with ISUP ≥ 2)NA36.81001004.0
Ruiz-Plazas et al., 2021, Cancers [49]miRNAmiR-221-3p, miR-222-3p, sTWEAKSemenISUP 1, 2 vs. ISUP 3, 4, 5qPCR97 patients who have undergone RP0.8685.776.9NANA
Martínez-González et al., 2021, Biomedicines [50]miRNAmiR-23cPlasmaISUP ≤ 2 vs. ISUP > 2qPCR60 patients with PSA ≥ 4 ng/mL meeting the criteria for undergoing a prostate biopsyNANANANANA
Ramirez-Garrastacho et al., 2021, Brit. J. Can. [51]miRNAHsa-miR-186-5p + PSAUrineISUP 1 vs. 2 vs. 3qPCR60 PCa (20 from each ISUP group)0.78NANANANA
Hsa-miR-30e + PSA0.74NANANANA
Hsa-miR-320a-3p + PSA0.77NANANANA
Kim et al., 2021, Sci. Rep. [52]miRNAExomiR-26a-5pUrineISUP = 2 BCR vs. ISUP = 2 non-BCRqPCRDiscovery set: 21 non-BCR, 6 BCR; Validation set: 28 non-BCR, 26 BCR0.67NANANANA
ExomiR-532-5p0.67
ExomiR-99b-3p0.67
Koo et al., 2018, Small [53]mRNA, miRNA, lncRNATMPRSS2:ERG + miR-107 + SChLAP1UrineISUP ≤ 2 vs. ISUP > 2qPCR18 PCa samples (10 with ISUP ≤ 2 and 8 with ISUP > 2) and 2 HDNANANANANA
Miyoshi et al., 2022, BMC Cancer [54]ProteinDHEASerumBPH + ISUP ≤ 2 vs. ISUP > 3LC-MS/MS203 patients with PSA levels < 10 ng/mLNA33.796.098.416.9
Bhakdi et al., 2019, Cancers [55]ProteintCECsWhole Blood1st definition: csPCa vs. ncsPCa (ISUP ≥ 2 vs. ISUP < 2). 2nd definition: csPCa vs. ncsPCa (ISUP ≥ 3 vs. ISUP <3 )Haemocytometer and fluorescence microscopy146 PCa patientsNA1st: 75.0 2nd: 71.01st:67.0 2nd:63.01st:32.0 2nd:18.01st:93.0 2nd:95.0
Delkov et al., 2022, Turk J Med Sci [56]ProteinGABAUrineEAU stratification system: High-risk vs. Intermediate risk. High-Risk vs. Low-RiskHPLC-MS/MS101 PCa patients and 52 controlsNANANANANA
Mahmud et al., 2021, Anal. Chem. [57]ProteinThymidine glycolUrineISUP = 1 vs. ISUP ≥ 2PSI-MS40 PCa patients (10 from each ISUP) and 10 HDNANANANANA
Ankerst at al., 2015, BMC Urology [58]ProteinSarcosineSerumISUP = 1 vs. ISUP ≥ 2HPLC-electrospray ionization mass spectrometry246 cancer cases and 251 age-matched non-cancer casesnot significant. p-value > 0.05NANANANA
Outzen et al., 2016, Brit. J. Nut. [59]ProteinSeleniumPlasma>T3 or ISUP > 2 or N1 or M1, or “regional/distant” extent of disease or PSA > 15 VS High-grade PCa—ISUP > 4chemical analyses784 cases (525 advanced PCa, 170 high-grade PCa, 89 low-grade PCa)not significant. p-value > 0.05NANANANA
Goetze et al., 2022, Clin. Prot. [60]ProteinFibronectin + vitronectinSerumtotal PSA = 4–10, tumor stage = pT2, and ISUP ≤ 1 vs. total PSA > 10, tumor stage = pT3, and ISUP ≥ 2Development phase: MS-GUIDE/Validation: ELISADiscovery set: 78 patients; validation set: 263 patients0.66NANANANA
Chiu et al., 2021, Prost. Can. Prost. Dis. [61]ProteinSpermineUrineISUP 1 vs. ISUP 2, 3, 4, 5UPLC-MS/MS600 Patients0.82 (Spermine + prostate volume + PSA + age + DRE). 0.66 (spermine)NANANANA
Biggs et al., 2016, Oncotarget [62]ProteinCirculating Prostate MicroparticlesPlasmaISUP ≤ 2 vs. ISUP > 2Nanoscale flow cytometryHealthy volunteers = 22, BPH = 156, localized PCa = 256, CRPC = 67NANANANANA
Chen et al., 2022, Front. Immunol. [63]ProteinPHI + tPSA + fPSA + TRAIL + IL-10serumISUP = 1 vs. ISUP ≥ 2Luminex cytokine immunoassays79 aggressive PCa and 209 indolent PCa or BPH0.92NANANANA
Brikun et al., 2019, Exp Hematol Oncol [64]DNA methylation32 markersUrineCAPRA riskqPCR15 group 1 (low), 18 group 2 (high)—DRE samplesNANANANANA
10 group 1 (low), 18 group 2 (high)—first void samplesNANANANANA
Connell et al., 2020, The Prostate [65]DNA methylationGSTP1, SFRP2, IGFBP3, IGFBP7, APC, PTSG2 and 167 gene-probes of cell free RNAUrineISUP = 1 vs. ISUP = 2 vs. ISUP ≥ 3NanoString297 first-catch post-DRE (77 no cancer finding, 120 PCa)0.89NANANANA
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Sequeira, J.P.; Salta, S.; Freitas, R.; López-López, R.; Díaz-Lagares, Á.; Henrique, R.; Jerónimo, C. Biomarkers for Pre-Treatment Risk Stratification of Prostate Cancer Patients: A Systematic Review. Cancers 2024, 16, 1363. https://doi.org/10.3390/cancers16071363

AMA Style

Sequeira JP, Salta S, Freitas R, López-López R, Díaz-Lagares Á, Henrique R, Jerónimo C. Biomarkers for Pre-Treatment Risk Stratification of Prostate Cancer Patients: A Systematic Review. Cancers. 2024; 16(7):1363. https://doi.org/10.3390/cancers16071363

Chicago/Turabian Style

Sequeira, José Pedro, Sofia Salta, Rui Freitas, Rafael López-López, Ángel Díaz-Lagares, Rui Henrique, and Carmen Jerónimo. 2024. "Biomarkers for Pre-Treatment Risk Stratification of Prostate Cancer Patients: A Systematic Review" Cancers 16, no. 7: 1363. https://doi.org/10.3390/cancers16071363

APA Style

Sequeira, J. P., Salta, S., Freitas, R., López-López, R., Díaz-Lagares, Á., Henrique, R., & Jerónimo, C. (2024). Biomarkers for Pre-Treatment Risk Stratification of Prostate Cancer Patients: A Systematic Review. Cancers, 16(7), 1363. https://doi.org/10.3390/cancers16071363

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop