Liquid Biomarkers in Prostate Cancer Diagnosis: Current Status and Emerging Prospects

Abstract

Prostate cancer (PCa) is a major health concern that necessitates appropriate diagnostic approaches for timely intervention. This review critically evaluates the role of liquid biopsy techniques, focusing on blood- and urine-based biomarkers, in overcoming the limitations of conventional diagnostic methods. The 4Kscore test and Prostate Health Index have demonstrated efficacy in distinguishing PCa from benign conditions. Urinary biomarker tests such as PCa antigen 3, MyProstateScore, SelectMDx, and ExoDx Prostate IntelliScore test have revolutionized risk stratification and minimized unnecessary biopsies. Emerging biomarkers, including non-coding RNAs, circulating tumor DNA, and prostate-specific antigen (PSA) glycosylation, offer valuable insights into PCa biology, enabling personalized treatment strategies. Advancements in non-invasive liquid biomarkers for PCa diagnosis may facilitate the stratification of patients and avoid unnecessary biopsies, particularly when PSA is in the gray area of 4 to 10 ng/mL.

INTRODUCTION

Prostate cancer (PCa) remains a substantial global health challenge and is the second most prevalent non-cutaneous malignancy among men in the United States. PCa is the second leading cause of cancer-related deaths in men, with approximately 268,490 new cases reported annually, leading to 34,500 fatalities [1]. The accurate diagnosis and classification of PCa are imperative for treatment planning to optimize outcomes and survival rates in patients. Owing to the favorable survival rates following appropriate treatment of PCa, accurate diagnosis and staging have gained considerable attention [2].

Prostate-specific antigen (PSA), a protein produced by the prostate gland, is commonly detected in the blood. The effectiveness of PSA as a tumor marker for PCa has been substantiated by extensive evidence [3]. Notably, a large-scale randomized controlled trial conducted by the European Randomized Study of Screening for Prostate Cancer (ERSPC) revealed that mortality due to PCa was reduced by 25% in men who underwent at least one PSA screening [4]. Based on the recommendations against routine screening by the U. S. Preventive Services Task Force, PCa incidence increased by 3% annually from 2014 to 2019 after two decades of decline in the United States [5], highlighting the nuanced impact of screening guidelines on disease outcomes. However, PSA screening has inherent limitations in terms of sensitivity and specificity. Furthermore, PSA is not tumor-specific; elevated PSA levels are observed in various prostate conditions, including benign prostatic hyperplasia and infections [6]. These constraints often warrant unnecessary biopsies and contribute to the diagnosis of clinically insignificant cancers [7]. Understanding these limitations is crucial for a comprehensive evaluation of the benefits and risks of PSA screening for PCa diagnosis.

Transrectal ultrasound (TRUS)-guided needle biopsy, the gold standard for diagnosing PCa, is associated with risks such as pain, bleeding, and sepsis. Indications for biopsy included elevated serum PSA levels, abnormal findings on digital rectal examination (DRE), positive family history, and clinical symptoms [8]. Advancements in medical imaging have contributed to the diagnosis and treatment of PCa. Multiparametric magnetic resonance imaging (mpMRI) has improved the diagnostic accuracy for cancer detection [9,10]. Coupled with targeted biopsy techniques guided by mpMRI enabling precise and less invasive biopsies focused on suspicious areas [11]. Although early studies suggested that mpMRI with targeted biopsy and TRUS-guided full biopsies demonstrate similar diagnostic efficiency, current practice often combines mpMRI with both targeted and systematic biopsies [12]. Despite the diagnostic potential of mpMRI, owing to the possibility of a missed diagnosis for clinically significant cancers [13], mpMRI is not considered an alternative to biopsy [14].

To address the limitations of conventional diagnostic approaches, there is a growing consensus on non-invasive liquid biopsies to complement existing diagnostic approaches to stratify patients and avoid unnecessary biopsies [15]. The U.S. Food and Drug Administration (FDA) or Clinical Laboratory Improvement Amendments have recognized tests such as 4Kscore test, Prostate Health Index (PHI), prostate-specific antigen 3 (PCA3), MyProstateScore (MPS), SelectMDx (MDx Health) and ExoDx Prostate IntelliScore (EPI) test for clinical application, thereby offering a promising avenue for improved PCa diagnosis and was recommended by NCCN and American Urological Association/Society of Urologic Oncology guidelines [16,17,18]. Ongoing research exploring innovative techniques, such as the detection of non-coding RNA and PSA glycosylation, holds exciting potential for more accurate and personalized PCa diagnosis and treatment strategies. This review analyzes the current status and new perspectives on liquid biomarkers for PCa diagnosis.

BLOOD DIAGNOSTIC BIOMARKERS

1. 4Kscore test

The 4Kscore test, developed by OPKO Health, is a blood test approved by the FDA in 2015 that utilizes four kallikrein proteins to assess the risk of aggressive PCa in men with elevated PSA levels. The 4Kscore test was included in the NCCN guidelines for PCa early detection, combining total PSA, free PSA, intact PSA, and human kallikrein 2 with patient age, DRE results, and previous prostate biopsy results, expanding upon PSA testing for enhanced accuracy [17]. Compared to previously reported findings, the 4Kscore test offers remarkable precision over PSA, demonstrating a higher area under the curve (AUC) [19]. This considerably influences crucial clinical decisions in an attempt to provide patient-specific PCa diagnosis and management.

In a large-scale trial with 366 men, the 4Kscore test (AUC 0.81) accurately detected aggressive PCa, outperforming a base model (AUC 0.74) across diverse populations [20]. Another study on 1,012 participants validated its diagnostic ability with an AUC of 0.82, highlighting its potential to reduce unnecessary biopsies by 30% to 58% with minimal impact on delayed diagnosis (1.3%–4.7% of Gleason ≥7 PCa cases), demonstrating exceptional accuracy in detecting aggressive PCa [21]. For patients under active surveillance, the 4Kscore test accurately predicted tumor reclassification with 89% sensitivity and 29% specificity in a study of 137 low-risk PCa patients, reducing unnecessary biopsies by 27% and enabling informed decisions regarding interventions [22]. When integrated with mpMRI, the 4Kscore test substantially reduces unnecessary biopsies [23]. This ensures that biopsies are performed only when essential, thereby minimizing patient discomfort and healthcare costs.

In a comprehensive analysis of 20 studies, the 4Kscore test consistently demonstrated exceptional predictive accuracy for PCa across diverse clinical scenarios (Table 1) [19,21,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41]. Comparative analyses with existing models, such as the Rotterdam Prostate Cancer Risk Calculator (RPCRC), revealed that 4Kscore and RPCRC had similar high AUCs (0.88 vs. 0.87; p=0.41) [42]. Notably, the 4Kscore test discriminates between racial and ethnic groups. Darst et al [24] reported that the 4Kscore test has the highest discriminative ability in native Hawaii, followed by Latinos, Japanese, Whites, and African Americans. An AUC of 0.777 (95% confidence interval [CI]: 0.747–0.807) was observed for all ethnic groups in PCa with Gleason score ≥8 and 0.782 (95% CI: 0.756–0.808) for aggressive PCa, emphasizing its universal clinical utility. The AUC for overall PCa was the lowest for African Americans (0.686, 95% CI: 0.637–0.735) and highest for native Hawaiians (0.875, 95% CI: 0.804–0.942).

Table 1. Studies on diagnostic performance of the 4Kscore test.

Author	Year	Number of patients	Endpoint	Outcome
Vickers et al [25]	2010	2,914	Predictive accuracy for PCa	AUC: 0.764 (95% CI: 0.739–0.788)
Gupta et al [26]	2010	925	Predictive accuracy for PCa in men with previous negative biopsy	AUC: 0.68 (95% CI: 0.62–0.74) in any PCa; 0.87 (95% CI: 0.81–0.94) in HGPC
Benchikh et al [27]	2010	262	Predictive accuracy for PCa	AUC: 0.782 (95% CI: 0.719–0.845) in any PCa; 0.870 (95% CI: 0.807–0.933) in HGPC
Vickers et al [28]	2010	1,501	Predictive accuracy for PCa	AUC: 0.713 (95% CI: 0.682–0.743)
Vickers et al [29]	2010	1,241	Predictive accuracy for PCa	AUC: 0.678 (95% CI: 0.643–0.714) in any PCa; 0.800 (95% CI: 0.732–0.859) in HGPC
Vickers et al [30]	2011	11,063	Predictive accuracy for PCa	AUC: 0.751 (95% CI: 0.726–0.777) for any PCa; 0.803 (95% CI: 0.774–0.831) for clinical stage >T2 PCa; 0.824 (95% CI: 0.785–0.858) for clinical stage >T3 or has evidence of metastasis PCa
Vedder et al [33]	2014	708	Predictive value added by 4Kscore test to the ERSPC multivariable prediction models	AUC: 0.78 (95% CI: 0.69–0.85)
Nordström et al [32]	2015	531	Predictive accuracy for PCa	AUC: 0.718 (95% CI: 66.8–76.7)
Parekh et al [21]	2015	1,012	Predictive accuracy for Gleason ≥7	AUC: 0.821 (95% CI: 0.790–0.852)
Bryant et al [19]	2015	6,129	Predictive accuracy for PCa	AUC: 0.719 (95% CI: 0.704–0.734) for any-grade PCa; 0.820 (95% CI: 0.802–0.838) for HGPC
Borque-Fernando et al [34]	2016	51	Predictive accuracy for PCa	AUC: 0.794 (95% CI: 0.657–0.894)
Braun et al [36]	2016	749	Predictive accuracy for HGPC	AUC: 0.683 (95% CI: 0.644–0.722) in any PCa; 0.777 (95% CI: 0.736–0.819) in HGPC
Kim et al [35]	2017	946	Predictive accuracy for HGPC	AUC: 0.786 (95% CI: 0.748–0.816)
Lin et al [38]	2017	718	Predictive accuracy for HGPC	AUC: 0.783 (95% CI: 0.691–0.871) in the initial surveillance biopsy; 0.754 (95% CI: 0.657–0.838) in reclassification in subsequent biopsies
Assel et al [37]	2019	1,632	Predictive accuracy for PCa	AUC: 0.743 (95% CI: 0.722–0.763) for any PCa; 0.746 (95% CI: 0.717–0.774) for HGPC
Darst et al [24]	2020	2,358	Discriminative ability across racial/ethnic groups	AUC: 0.777 (95% CI: 0.747–0.807) for Gleason 8+PCa; 0.782 (95% CI: 0.756–0.808) for aggressive PCa
Haese et al [39]	2020	2,330	Predictive accuracy for PCa	AUC: 0.718 for biopsy GG 3+3 patients; 0.659 for biopsy GG 3+4 patients
Wysock et al [40]	2020	128	Ability to detect csPCa	AUC: 0.830 (95% CI: 0.710–0.949)
Fredsøe et al [41]	2022	234	Predictive accuracy for PCa	AUC: 0.763 (95% CI: 0.696–0.829)

AUC: area under the curve, CI: confidence interval, csPCa: clinically significant prostate cancer, ERSPC: European Randomized Study of Screening for Prostate Cancer, HGPC: high-grade prostate cancer, PCa: prostate cancer.

A systematic review comparing the 4Kscore test with other diagnostic methods demonstrated the superiority of the 4Kscore test in predicting biopsy histopathology and metastatic/aggressive disease, reducing unnecessary biopsies (ranging from 25% to 82%), and avoiding overtreatment (14%) [43]. Its integration promises personalized PCa treatment, decision optimization, and improved patient outcomes.

2. Prostate Health Index

PHI was introduced by Beckman Coulter as an FDA-approved automated immunoassay for PCa detection. The PHI, calculated from total PSA, free PSA, and [-2] proPSA values, demonstrates strong diagnostic potential across diverse patient groups. PHI is endorsed in NCCN guidelines for men aged ≥50 years with PSA levels between 4 and 10 µg/L and a non-suspicious DRE, following extensive studies [17,44]. PHI outperforms traditional markers, particularly in the PSA range of 2 to 10 µg/L, and efficiently distinguishes PCa from benign conditions thereby reducing unnecessary biopsies [45].

The diagnostic accuracy of PHI has been assessed across diverse populations (Table 2) [32,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78]. A study by Park et al (2018) [46] involving 246 Korean men demonstrated an AUC of 0.76 (95% CI: 0.69–0.84) in patients with total PSA 3.5 to 10 ng/mL and an impressive AUC of 0.97 (95% CI: 0.94–0.99) in Gleason score ≥7 PCa. A study by Ito et al [78] involving 363 men with PSA levels below 10 ng/mL demonstrated that PHI was superior to PSA for detecting clinically significant PCa, with an AUC of 0.789 (95% CI: 0.737–0.835) and 0.577 (95% CI: 0.518–0.637), respectively. Chiu et al (2019) [47], in their study on 2,488 patients that compared Asian and European populations, demonstrated an AUC of 0.71 (95% CI: 0.66–0.76) and 0.74 (95% CI: 0.70–0.79) for European and Asian patients, respectively.

Table 2. Studies on diagnostic performance of PHI.

Author	Year	Number of patients	Endpoint	Outcome
Jansen et al [51]	2010	756	Diagnostic value of PCa	AUC: 0.750 (95% CI: 0.704–0.791)
Ferro et al [52]	2012	251	Diagnostic value of PCa	AUC: 0.77; sensitivity: 0.85 (95% CI: 0.74–0.94); specificity: 0.61 (95% CI: 0.49–0.72)
Guazzoni et al [54]	2012	350	Diagnostic performance of predicting pathologic outcomes of RP	AUC: 0.740 in Gleason sum ≥7; AUC: 0.686 in Gleason sum upgrading from 6 to 7; 0.799 of tumor volume <5 mL
Lazzeri et al [55]	2012	222	Diagnostic value of PCa in men who accepted repeat biopsy	AUC: 0.67 (95% CI: 0.61–0.73)
Loeb et al [53]	2013	892	Diagnostic value of PCa	AUC: 0.704
Scattoni et al [56]	2013	211	Diagnostic value of PCa	AUC: 0.57 (95% CI: 0.47–0.68) in initial biopsy; 0.63 (95% CI: 0.50–0.75) in repeat biopsy
Ferro et al [57]	2013	300	Diagnostic value of PCa	AUC: 0.77 (95% CI: 0.72–0.83)
Lazzeri et al [58]	2013	1,026	Diagnostic value of PCa	AUC: 0.73 (95% CI: 0.66–0.80)
Stephan et al [59]	2013	1,362	Diagnostic value of PCa	AUC: 0.74 (95% CI: 0.68–0.80)
Filella et al [60]	2014	354	Predictive performance of aggressiveness of PCa	AUC: 0.833 (95% CI: 0.775–0.892) in Gleason score ≥7 PCa; 0.866 (95% CI: 0.787–0.942) in PCa T2 or T3
Nordström et al [32]	2015	531	Diagnostic value of PCa	AUC: 0.711 (95% CI: 0.66–0.762)
Loeb et al [61]	2015	658	Diagnostic value of PCa	AUC: 0.708 (95% CI: 0.668–0.747) for any PCa; 0.698 (95% CI: 0.651–0.745) for HGPC
Abrate et al [62]	2015	965	Diagnostic value of PCa	AUC: 0.687 (95% CI: 0.638–0.733) in patients BMI <25 kg/m2; 0.671 (95% CI: 0.625–0.715) in patients BMI 25–29.9 kg/m2; 0.839 (95% CI: 0.768–0.895) in patients BMI ≥30 kg/m2
de la Calle et al [63]	2015	956	Diagnostic value of PCa	AUC: 0.815 (95% CI: 0.771–0.858) with aggressive PCa; 0.783 (95% CI: 0.734–0.832) with Gleason score ≥7 Pca
Cantiello et al [48]	2016	188	Diagnostic value added to the Epstein criteria (Model 1: PSA density, number of positive cores and % of core involvement)	AUC: 0.92 (95% CI: 0.87–0.95) for PHI add to the Epstein criteria
Foley et al [65]	2016	250	Diagnostic value of HGPC	AUC: 0.712 (95% CI: 0.646–0.777) for any PCa; 0.776 (95% CI: 0.710–0.841) for HGPC
Chiu et al [66]	2016	135	Diagnostic value of PCa in Chinese men	AUC: 0.800 (95% CI: 0.725–0.875) in HGPC; 0.818 (95% CI: 0.746–0.890) for tumor volume >0.5 mL
Tosoian et al [64]	2017	345	Diagnostic value of PCa	AUC: 0.722 (95% CI: 0.636–0.807) for any PCa; 0.767 (95% CI: 0.681–0.852) for GG2 (GS 3+4=7) PCa
Tosoian et al [79]	2017	118	Diagnostic value of csPCa	AUC: 0.76 (95% CI: 0.68–0.85)
Sriplakich et al [67]	2018	101	Diagnostic value of PCa in men with a total PSA of 4–10 ng/mL and a negative DRE	AUC: 0.758 (95% CI: 0.6–0.9)
Park et al [46]	2018	246	Diagnostic value of PCa in Korean men	AUC: 0.76 (95% CI: 0.69–0.84) in patients with total PSA 3.5–10 ng/mL; 0.97 (95% CI: 0.94–0.99) in Gleason score ≥7 PCa
Hsieh et al [68]	2018	154	Diagnostic value of PCa	AUC: 0.77 (95% CI: 0.68–0.87)
Tang et al [69]	2018	140	Predictive performance of predicting pathologic outcomes of RP	AUC: 0.767 (95% CI: 0.685–0.849) with Gleason >6 PCa
Chiu et al [47]	2019	2,488	Diagnostic value of PCa between Asian and European	AUC: 0.71 (95% CI: 0.66–0.76) for European; 0.74 (95% CI: 0.70–0.79) for Asian
Cheng et al [70]	2019	213	Diagnostic value of PCa	0.772 (95% CI: 0.678–0.867)
Ito et al [78]	2020	363	Diagnostic value of PCa	AUC: 0.767 (95% CI: 0.717–0.813)
Nassir et al [50]	2020	194	Diagnostic value of PCa	AUC: 0.87 (95% CI: 0.825–0.948) for discrimination between Pca and normal men; 0.733 (95% CI: 0.644–0.822) for discrimination between PCa and BPH
Fan et al [49]	2021	164	Diagnostic value of csPCa in MRI-TRUS fusion biopsy	AUC: 0.792 (95% CI: 0.707–0.877) in patients with PI-RADS 4/5 lesions; AUC: 0.884 (95% CI: 0.792–0.976) in the patients with PI-RADS 3 lesions
Stephan et al [71]	2021	1,057	Diagnostic value of PCa	AUC: 0.789 (95% CI: 0.76–0.82) in men with PSA values 1–8 ng/mL
Chiu et al [72]	2021	412	Predictive value csPCa	AUC: 0.72 for PCa; 0.77 for csPCa
Babajide et al [73]	2021	293	Diagnostic value of PCa	AUC: 0.63 (95% CI: 0.54–0.73)
Garrido et al [74]	2021	237	Diagnostic value of PCa from non-Beckman Coulter manufacturers (Roche and Abbott)	AUC: 0.776 (95% CI: 0.716–0.836) with Beckman Coulter Access®; 0.785 (95% CI: 0.726–0.844) with Roche cobas®; 0.778 (95% CI: 0.718–0.838) with Abbott Architect®
Filella et al [75]	2022	455	Diagnostic value of PCa	AUC: 0.766 (95% CI: 0.725–0.804)
Yáñez-Castillo et al [76]	2023	140	Diagnostic value of PCa	AUC: 0.77 (95% CI: 0.73–0.81)
Rius Bilbao et al [77]	2023	559	Diagnostic value of PCa	AUC: 0.78 (95% CI: 0.73–0.83) in overall PCa; 0.79 (95% CI: 0.7–0.87) in patients PSA >10 ng/mL

AUC: area under the curve, CI: confidence interval, csPCa: clinically significant prostate cancer, DRE: digital rectal examination, HGPC: high-grade prostate cancer, MRI: magnetic resonance imaging, PCa: prostate cancer, PHI: Prostate Health Index, PSA: prostate-specific antigen, RP: radical prostatectomy, TRUS: transrectal ultrasound, BPH: benign prostatic hyperplasia.

Moreover, integration of the PHI into multimodal approaches, such as MRI, and its combination with established clinical parameters has significantly enhanced its diagnostic utility [79,80]. Cantiello et al (2016) [48] demonstrated its ability to outperform traditional risk calculators, with an AUC of 0.92 (95% CI: 0.87–0.95) when added to the Epstein criteria, highlighting its clinical relevance. Furthermore, a study by Fan et al (2021) [49] on MRI-TRUS fusion biopsy revealed an AUC of 0.792 (95% CI: 0.707–0.877) in patients with Prostate Imaging Reporting and Data System (PIRADS) 4/5 lesions and an AUC of 0.884 (95% CI: 0.792–0.976) in those with PI-RADS 3 lesions.

Nassir et al [50] demonstrated that the PHI was more effective in discriminating between patients with PCa and healthy men (AUC 0.887, 95% CI: 0.825–0.948) than in differentiating between PCa and patients with benign prostatic hyperplasia (BPH) (AUC 0.639, 95% CI: 0.546–0.733), which is in concordance with the findings of Lazzeri et al [81]. Notably, the excellent discriminative ability of PHI between men with PCa and healthy men is beneficial for PCa diagnosis.

The PHI-based nomograms, incorporating clinical parameters, demonstrated robust predictive accuracy, outperforming traditional risk calculators [82]. Moreover, economic analyses underscore the cost-effectiveness of PHI, especially with optimized pricing [83]. The PHI has emerged as a critical diagnostic tool with high accuracy, broad applicability, and cost efficiency. Its integration into PCa management ensures the implementation of precise, efficient, and effective cost-reduction strategies.

URINE DIAGNOSTIC BIOMARKERS

1. Prostate-specific antigen 3

PCA3, a non-coding prostate-specific RNA, is notably overexpressed in PCa tissue and serves as a biomarker of PCa; the PCA3 test was approved by the FDA in 2012 [84,85]. This molecular test is groundbreaking as it helps determine the necessity for repeat prostate biopsies in individuals with previous negative biopsy results, and its inclusion in the NCCN guidelines further underscores its clinical importance. PCA3 offers a precise diagnostic target, with over 95% of primary prostate tumors exhibiting substantial overexpression that significantly outperforms PSA in terms of predictive value and specificity [86]. Studies elucidating the diagnostic accuracy and clinical utility of PCA3 is summarized in Table 3 [33,48,52,56,57,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110]. A prospective study by Tinzl et al [87] demonstrated excellent clinical performance with an 82% sensitivity, 76% specificity, and an AUC of 0.87 (95% CI: 0.81–0.92), indicating its high predictive value. Chevli et al [99] analyzed 3,073 men and showed that PCA3 is a better predictor of PCa than PSA, with an AUC of 0.697 vs. 0.599 (p<0.01).

Table 3. Studies on diagnostic performance of PCA3.

Author	Year	Number of patients	Endpoint	Outcome
Hessels et al [88]	2003	108	Ability to identify PCa	AUC: 0.72 (95% CI: 0.58, 0.85)
Tinzl et al [87]	2004	201	Ability to identify PCa	AUC: 0.87 (95% CI: 0.81, 0.92)
Groskopf et al [91]	2006	143	Ability to identify PCa	AUC: 0.746 (95% CI, 0.574, 0.918)
Chun et al [92]	2009	1,206	Predictive value of HGPC	AUC: 0.633 for PCA3 assay score cut-off threshold of 17; 0.624 for PCA3 cut-off of 24; 0.620 for PCA3 cut-off of 35
Roobol et al [90]	2010	721	Diagnostic value of PCa	AUC: 0.635 (95% CI: 0.582, 0.689)
Pepe et al [93]	2012	118	Diagnosis ability in PCa with cut-off of 35 vs. 20	AUC: 0.678 for PCA3 ≥20; 0.634 for PCA3 ≥35
Klatte et al [94]	2012	205	Diagnostic value of PCa and impact of age	AUC: 0.754 (95% CI: 0.684, 0.824) for PCA3 alone; 0.771 (95% CI: 0.702, 0.840) with age-specific PCA3 score
Wu et al [95]	2012	103	Diagnostic value of PCa	AUC: 0.64 (95% CI: 0.53, 0.75)
Ferro et al [52]	2012	251	Diagnostic value of PCa	AUC: 0.71; sensitivity: 0.81 (95% CI: 0.68, 0.91); specificity: 0.57 (95% CI: 0.45, 0.69)
Crawford et al [96]	2012	1,962	Diagnostic value of PCa	AUC: 0.706 (95% CI: 0.673, 0.739)
Cornu et al [97]	2013	291	Diagnostic value of PCa	AUC: 0.66 (95% CI: 0.60, 0.72)
Scattoni et al [56]	2013	211	Diagnostic value of PCa	AUC: 0.69 (95% CI: 0.59, 0.79) in initial biopsy; 0.72 (95% CI: 0.59, 0.84) in repeat biopsy
Ruffion et al [98]	2013	601	Diagnostic value of PCa	AUC: 0.780 (95% CI: 0.743, 0.818)
Ferro et al [57]	2013	300	Diagnostic value of PCa	AUC: 0.73 (95% CI: 0.68, 0.79)
Ochiai et al [100]	2013	647	Diagnostic value of PCa in Asian men	AUC: 0.742; sensitivity 66.5%; specificity 71.6%
Chevli et al [99]	2014	3,073	Diagnostic value of PCa	AUC: 0.697 for all PCa; 0.682 for HGPC
Leyten et al [89]	2014	497	Diagnostic value of PCa	AUC: 0.720 (95% CI: 0.67, 0.77) in all PCa; 0.53 (95% CI: 0.44, 0.61) in patients Gleason score ≥7; 0.48 (95% CI: 0.39, 0.56) in patients clinical tumor stage T3–T4
Vedder et al [33]	2014	708	Predictive value added to the ERSPC multivariable prediction models	AUC: 0.62 (95% CI: 0.52, 0.73)
Wei et al [101]	2014	859	Diagnostic value of PCa	PPV: 80% (95% CI: 72%, 86%); NPV: 88% (95% CI: 81%, 93%); AUC 0.79 for initial prostate biopsy of any cancer; 0.69 for repeat prostate biopsy of any cancer; 0.78 for initial prostate biopsy of high-grade cancer; 0.79 for repeat prostate biopsy of high-grade cancer AUC: 0.775
Capoluongo et al [102]	2014	734	Evaluation diagnostic performance of PCA3	AUC: 0.775
Merola et al [103]	2015	407	Diagnostic value of PCa	AUC: 0.865
Zheng et al [104]	2015	52	Diagnostic value of PCa	AUC: 0.831; sensitivity: 82.4%; specificity: 77.8%
Tomlins et al [105]	2016	1,244	Diagnostic value of HGPC	AUC: 0.726
Feibus et al [106]	2016	304	Diagnostic value of PCa	AUC: 0.804 (95% CI: 0.752, 0.855)
Cantiello et al [48]	2016	188	Diagnostic value added to the Epstein criteria (Model 1: PSA density, number of positive cores and % of core involvement)	AUC: 0.77 (95% CI: 0.70, 0.83) for PCA3 add to the Epstein criteria
Cao et al [107]	2018	271	Diagnostic value of PCa	AUC: 0.729 (95% CI: 0.65, 0.78)
Ankerst et al [109]	2019	1,225	Diagnostic value of PCa	AUC: 0.713; 0.764 for updated for PCPTRC with PCA3 included: 0.771 for updated PCPTRC with both PCA3 and T2:ERG included
Newcomb et al [110]	2019	782	Diagnostic value of PCa	AUC: 0.611 (95% CI: 0.553, 0.668)
Gan et al [108]	2022	124	Diagnostic value of PCa	AUC: 0.7829 (95% CI: 0.7021, 0.8633)

AUC: area under the curve, CI: confidence interval, ERSPC: European Randomized Study of Screening for Prostate Cancer, HGPC: high-grade prostate cancer, NPV: negative predictive value, PCa: prostate cancer, PCA3: prostate cancer antigen 3, PCPTRC: Prostate Cancer Prevention Trial Risk Calculator, PPV: positive predictive value.

Furthermore, the strategic amalgamation of PCA3 with multiple biomarkers is promising for clinical application in a new era of precision PCa detection and characterization. Leyten et al (2014) [89] demonstrated that AUC increased from 0.799 (ERSPC risk calculator), to 0.833 (ERSPC risk calculator plus PCA3), to 0.842 (ERSPC risk calculator plus PCA3 plus TMPRSS2-ERG gene fusions) to predict PCa. In addition, TMPRSS2-ERG was useful in predicting prognosis. This prospective study supports the idea that a biomarker panel combining PCA3 and TMPRSS2-ERG could lead to a significant reduction in prostate biopsies.

2. MyProstateScore

MPS, previously named as Mi-Prostate Score, measures total serum PSA and post-DRE urine expression of PCA3 and the TMPRSS2:ERG fusion gene developed by Tomlins et al [105,111]. MPS represents a pivotal advancement in PCa risk assessment, aiming to enhance diagnostic precision, and is included in the NCCN guidelines to improve PCa diagnosis. TMPRSS2:ERG fusion-positive tumors correlated strongly with higher Gleason scores [112]. Studies demonstrating the diagnostic value of MPS, as well as the combination of PCA3 and TMPRSS2:ERG fusion gene, are summarized in Table 4 [89,97,105,106,109,111,113,114].

Table 4. Studies on diagnostic performance of MPS, including the studies combining PCA3 and TMPRSS2:ERG fusion gene.

Author	Year	Number of patients	Endpoint	Outcome patients
Tomlins et al [111]	2011	1,312	Diagnostic value of PCa	AUC: 0.71
Salami et al [113]	2013	45	Diagnostic value of PCa	AUC: 0.77 (95% CI: 0.61, 0.90)
Cornu et al [97]	2013	291	Diagnostic value of PCa	AUC: 0.67 (95% CI: 0.61, 0.73)
Leyten et al [89]	2014	497	Diagnostic value of PCa	AUC: 0.59 (95% CI: 0.53, 0.64) in all PCa; 0.64 (95% CI: 0.56, 0.71) in patients Gleason score ≥7; 0.60 (95% CI: 0.52, 0.68) in patients clinical tumor stage T3–T4
Tomlins et al [105]	2016	1244	Diagnostic value of PCa	AUC: 0.751 for all group of PCa; 0.772 for HGPC (CI is not mentioned in paper)
Feibus et al [106]	2016	304	Diagnostic value of PCa	AUC: 0.781 (95% CI: 0.730, 0.838)
Ankerst et al [109]	2019	1,225	Diagnostic value of PCa	AUC: 0.628 for MPS; 0.771 for updated PCPTRC with both PCA3 and MPS included
Cani et al [114]	2022	126	Diagnostic value of PCa	AUC: 0.80 (95% CI: 0.74, 0.87) for retrained MPS; 0.72 (95% CI: 0.60, 0.84) for Hi−grade MPS

AUC: area under the curve, CI: confidence interval, PCa: prostate cancer, HGPC: high-grade prostate cancer, MPS: MyProstateScore, PCA3: prostate cancer antigen 3, PCPTRC: Prostate Cancer Prevention Trial Risk Calculator.

Tomlins et al [111] reported that MPS outperformed conventional PSA markers, with an AUCs ranging from 0.71 to 0.79 in various cohorts, indicating its diagnostic efficiency. Notably, MPS demonstrates exceptional predictive accuracy, particularly for Gleason grade ≥2 cancer [105,106]. Despite its robust performance in diagnosing PCa, especially in distinguishing aggressive variants, TMPRSS2:ERG showed limitations in predicting recurrence and mortality in men who underwent radical prostatectomy. This suggests that MPS may have a limited prognostic impact on surgically treated patients [115].

Tosoian et al [116] reported that MPS test, with cut of value >40, prevented 67% of biopsies with a negative predictive value (NPV) of 95%, minimizing unnecessary biopsies. Tosoian et al [117] also reported that, in men (n=121) with equivocal mpMRI (PI-RADS 3), the AUC for predicting Gleason grade ≥2 PCa was 0.55 for PSA, 0.62 for PSA density, and 0.73 for MPS. Economically, MPS has greater value than MRI when used as a reflex test before prostate biopsy, especially in patients with intermediate PSA levels (4–10 ng/mL) [118]. By offering both diagnostic accuracy and economic feasibility, MPS is a valuable diagnostic tool.

3. SelectMDx

Leyten et al (2015) [119] showed that HOXC6, TDRD1 and DLX1 are overexpressed in urine samples from Gleason score ≥7 PCa and can be beneficial for identifying patients with aggressive PCa, regardless of PSA values. The urine-based test after DRE measures three biomarkers: DLX1 (progression gene), HOXC6 (cell proliferation gene), KLK3 (reference gene), and clinical risk factors (age, DRE, PSA, and prostate volume, which can be calculated from the TRUS measurements substituted into the formula: height×width×length×0.523); it was introduced as SelectMDx and was included in the 2018 European Association of Urology (EAU) clinical guidelines. The ability to integrate messenger RNA (mRNA) markers and clinical risk factors offers a comprehensive understanding of PCa risk [120]. Although SelectMDx is more sensitive but less specific than mpMRI for PCa detection [121], it optimizes healthcare resources through precise patient stratification, leading to a considerable reduction in unnecessary biopsies and marking a paradigm shift in diagnosis [122]. Studies demonstrating the diagnostic accuracy of SelectMDx are listed in Table 5 [40,121,122,123,124,125,126,127,128,129,130,131]. The ability of SelectMDx to accurately identify high-grade PCa (HGPC) is of potential concern, and its sensitivity, specificity, and predictive values have proven its efficacy across diverse patient populations [132]. Furthermore, SelectMDx is cost-efficient, alleviating the burden of unnecessary medical interventions on patients in European countries [133].

Table 5. Studies on diagnostic performance of SelectMDx.

Author	Year	Number of patients	Endpoint	Outcome
Hendriks et al [123]	2017	172	Predictive accuracy of PCa	AUC: 0.83 (95% CI: 0.77–0.89)
Haese et al [124]	2019	1,955	Predictive accuracy of csPCa	AUC 0.85 (95% CI: 0.83–0.88)
Roumiguié et al [126]	2020	117	Predictive accuracy of PCa	AUC 0.73 (95% CI: 0.64–0.83)
Wysock et al [40]	2020	128	Predictive accuracy of csPCa	AUC: 0.672 (95% CI: 0.517–0.828; p=0.036)
Pepe et al [127]	2020	125	Predictive accuracy of csPCa	Sensitivity: 55.6%; Specificity: 65.8%; PPV: 27.8%; NPV: 87%
Maggi et al [121]	2021	310	Predictive accuracy of PCa and csPCa	Predicting PCa: AUC: 0.80 (95% CI: 0.76–0.85); sensitivity: 86.5% (95% CI: 78.5–91.9); specificity: 73.8% (95% CI: 64.7–79.3); Predicting csPCa: AUC: 0.75 (95% CI: 0.70–0.81); sensitivity: 87.1% (95% CI: 76.2–93.5); specificity: 63.7% (95% CI: 57.5–69.4)
Hendriks et al [122]	2021	599	Predictive accuracy of HGPC	NPV 92% (95% CI: 0.88–0.95); PPV 44% (95% CI: 0.39–0.50); Sensitivity, 90% (95% CI: 0.85–0.94)
Busetto et al [128]	2021	52	Predictive accuracy of PCa	NPV 97.0% (95% CI: 0.911–1.000); PPV 84.2% (95% CI: 0.678–1.000); Sensitivity, 94.1% (95% CI: 0.707–1.000); Specificity, 91.4% (95% CI: 0.767–0.977)
Lendínez-Cano et al [129]	2021	163	Predictive accuracy of csPCa	AUC: 0.63 (95% CI: 0.56–0.71); Sensitivity: 76.9% (95% CI: 63.2–87.5); Specificity: 49.6% (95% CI: 39.9–59.2); NPV: 82.09% (95% CI: 70.8–90.4); PPV: 41.67% (95% CI: 31.7–52.2)
Fiorella et al [130]	2021	86	Predictive accuracy for predicting pathological progression in PCa active surveillance	AUC: 0.714 (95% CI: 0.603–0.825)
Katzendorn et al [131]	2022	74	Accuracy for detection of PCa	AUC 0.76
Margolis et al [125]	2022	1,212	Predictive accuracy of PCa	AUC 0.70 (95% CI: 0.67–0.73)

AUC: area under the curve, CI: confidence interval, csPCa: clinically significant prostate cancer, HGPC: high-grade prostate cancer, NPV: negative predictive value, PCa: prostate cancer, PPV: positive predictive value.

The predictive accuracy of SelectMDx has been substantiated by extensive research. Hendriks et al [123] demonstrated an AUC of 0.83 (95% CI: 0.77–0.89) for predicting PCa by SelectMDx. Haese et al [124] achieved an AUC of 0.85 (95% CI: 0.83–0.88) for predicting clinically significant PCa with 93% sensitivity, 47% specificity, and 95% NPV. Visser et al [134] reported that of 5157 patients from 10 European countries, SelectMDx results were negative in 40.72% of cases, indicating a substantial number of patients could avoid the need for biopsy. A prospective study demonstrated that the SelectMDx test, as a risk stratification tool for biopsy-naïve men, avoided 38% of unnecessary biopsies and missed only 10% of HGPC. If the use of mpMRI is limited or expensive, a good alternative strategy is to use mpMRI only for SelectMDx test-positive patients [122].

4. ExoDx Prostate IntelliScore test

Donovan et al [135] revealed that the sum of exosomal PCA3 and ERG RNA levels deribed from non-DRE urine had predictive value for PCa diagnosis. Based on this finding, McKiernan et al. developed the EPI test, a non-invasive urine exosome RNA-based assay that assesses the expression of three genes (PCA3, ERG, and SPDEF), which has emerged as a revolutionary tool for risk assessment and decision-making [136]. The robust diagnostic accuracy of the EPI test has been demonstrated, positioning it as a valuable tool for PCa risk assessment, particularly for ruling out cases and detecting HGPC (Table 6) [125,136,137,138,139]. In a study involving 229 men with previous negative results from biopsies, the EPI test with a cut point of 15.6 avoided 26% of unnecessary biopsies and missed only 2.1% of HGPC [137]. Clinical utility studies involving 1,094 patients and 72 urologists revealed a 30% increase in HGPC detection, emphasizing the impact of EPI on patient stratification [140]. Cost-effectiveness analyses revealed that the EPI test significantly improved quality-adjusted survival while reducing the number of unnecessary biopsies [141].

Table 6. Studies on diagnostic performance of ExoDx Prostate IntelliScore (EPI).

Author	Year	Number of patients	Endpoint	Outcome
McKiernan et al [136]	2016	499	Diagnostic accuracy of PCa	AUC 0.77 (95% CI: 0.71–0.83)
McKiernan et al [139]	2018	503	Diagnostic accuracy of HGPC	AUC (95% CI): 0.71 (0.66–0.76); Sensitivity: 90 (84.1–94.1); Specificity: 38.6 (33.4–43.9); NPV: 89.3 (83.1–93.7); PPV: 40.1 (35.0–45.4)
McKiernan et al [137]	2020	229	Diagnostic accuracy of PCa	AUC: 0.66 (95% CI: 0.55–0.78); NPV: 92% (95% CI: 0.81–0.97) 82%; sensitivity: 92% (95% CI: 0.81–0.97)
Margolis et al [125]	2022	1,212	Diagnostic accuracy of PCa	AUC 0.70 (95% CI: 0.67–0.73)
Kretschmer et al [138]	2022	2,066	Diagnostic accuracy of PCa	AUC 0.84 (95% CI: 0.73–0.96)

AUC: area under the curve, CI: confidence interval, HGPC: high-grade prostate cancer, PPV: positive predictive value, NPV: negative predictive value, PCa: prostate cancer.

In summary, EPI has emerged as a powerful tool for PCa risk assessment, effective in ruling out cases and detecting HGPC. The following sections delve into the historical context, diagnostic utility, key research findings, and cost considerations of EPI and conclude with its overall significance in PCa diagnostics.

5. Emerging diagnostic biomarkers in liquid biopsy

Recently, novel biomarkers that offer promising avenues for early detection, precise prognosis, and tailored treatment strategies have been identified. In addition to established markers, emerging biomarkers have attracted considerable attention among researchers and clinicians. These innovative biomarkers, including non-coding RNAs, PSA glycosylation, and DNA methylation patterns, are at the forefront of PCa diagnosis, heralding a new era in personalized medicine.

6. Non-coding RNA

More than 80% of human genome sequencing results in diverse non-coding RNA transcripts categorized into long non-coding RNAs (LncRNAs) (>200 nucleotides) and small non-coding RNAs (below this threshold) based on their size differences [142]. In recent decades, molecular biomarker studies have primarily focused on coding genes. However, extensive research has revealed that mRNAs often display less specific expression patterns across different tissues and disease stages. In contrast, non-coding RNAs exhibit high tissue and stage specificity for diseases [143]. This distinct characteristic is a key factor in the proposal of non-coding RNAs as molecular biomarkers of cancer.

7. LncRNAs

LncRNAs have been implicated in several biological and pathological processes, including chromatin reprogramming, transcriptional regulation, cell proliferation, tumorigenesis, and malignant transformation [144]. Because of their critical role in cancer, which may explain their tissue- and tumor-specific expression, lncRNAs have potential as molecular biomarkers [145,146].

The differential expression of some lncRNAs has been analyzed in patients with PCa, and their potential as biomarkers has been explored. Among these, PCA3 is considered a PCa biomarker approved by the FDA.

The importance of lncRNAs, including PCAT1, PCGEM1, SChLAP1, and PCAT6, in PCa is evident. PCAT1, a recently discovered lncRNA, is a promising biomarker. The elevated expression of PCAT1 is strongly associated with cancer infiltration depth, lymph node metastasis, distant metastasis, and TNM stage, providing a detailed description of cancer progression [147]. PCGEM1, another lncRNA, delves into the realm of genetic variation, revealing polymorphisms in its genes that influence PCa risk and opening avenues for exploring the interplay between genetics and lncRNA function [148]. SChLAP1, an overexpressed lncRNA, is a critical determinant of PCa aggressiveness, orchestrating cancer cell invasiveness and metastasis via antagonistic interactions with the SWI/SNF chromatin-modifying complex [149,150]. PCAT6, revealed through high-throughput analyses, unravels the m6A-induced PCAT6/IGF2BP2/IGF1R axis, providing insights on the intricate mechanisms underlying PCa bone metastasis and tumor growth [151].

In addition to lncRNA expression levels, cancer risk is linked to the increased prevalence of single-nucleotide polymorphisms (SNPs) within lncRNA genes in PCa. Specifically, SNPs in PCAT19, PCGEM1 and PRNCR1 have been identified as potential contributors to PCa susceptibility [148,152].

8. Small non-coding RNAs

Owing to their remarkable stability in bodily fluids, miRNAs have become one of the most extensively studied small non-coding RNAs [153,154,155]. In addition to their diagnostic significance, certain miRNAs, including miR-21, miR-221, and miR-375, are dysregulated in PCa and linked to the prognosis of patients with castration-resistant PCa [156,157]. Moreover, certain miRNAs regulate cellular sensitivity to ionization radiation, indicating that miRNAs can be therapeutic biomarkers [158].

The role of small non-coding RNAs in PCa extends beyond that of miRNAs. Deep sequencing analyses have revealed significant differential expression of small nucleolar RNAs and transfer RNAs in PCa [157]. In addition, the analysis of urinary exosomal small non-coding RNAs has paved the way for the development of advanced diagnostic tools. The miR Sentinel PCa Tests based on urinary exosomal small non-coding RNAs have demonstrated exceptional sensitivity and specificity for diagnosing and classifying PCa [159].

These advances highlight the potential of small non-coding RNAs as robust biomarkers for diagnosing PCa. The integration of these innovative tests into clinical practice could revolutionize early detection, provide patients with timely and accurate prognostic information, and ultimately enhance PCa management.

9. PSA glycosylation

Recent advancements in glycobiology have revealed that aberrant glycosylation is fundamental to tumorigenesis, making glycoproteins with tumor-specific glycans potential cancer markers [160,161]. Proteins, including PSA, undergo diverse glycosylation patterns owing to alterations in their cellular pathways. PSA glycosylation is pivotal in PCa research and specifically focuses on α2,3-sialylated PSA and α1,6-fucosylated PSA [162,163,164,165,166,167]. Yoneyama et al [168] reported that the PCa detectability of α2,3-sialylated PSA density was superior to that of total PSA or PSA density (AUC: 0.7758 vs 0.6360 and 0.7509, respectively). Fujita et al [169] reported that the AUC for the detection of Gleason grade ≥2 by α1,6-fucosylated PSA index was 0.698 (95% CI: 0.600–0.796; p=0.0004), compared to 0.554 (95% CI: 0.445–0.664; p=0.33) for PSA. By concurrently analyzing α2,3-Sia-PSA and α1,6-Fuc-PSA (SF index), Hatano et al [170] demonstrated the superior diagnostic capabilities of the SF index for Gleason grade ≥2 PCa (AUC 0.842, 95% CI: 0.782–0.903), which outperformed single PSA tests. The ability of the SF index to complement conventional PSA tests enhances accuracy and reliability, underscoring its potential to revolutionize PCa diagnosis. PSA glycosylation represents a paradigm shift, offering a targeted approach for PCa detection and marking a new era in cancer biomarkers.

10. Cell-free DNA-derived diagnosis assay

Cell-free DNA facilitates real-time genomic profiling of cancer cells without tissue biopsy. In PCa, Cell-free DNA analysis enables the detection of crucial genomic alterations, including DNA methylation pattern changes, somatic mutations, copy number variants, and structural rearrangements [171]. The comprehensive characterization of these genomic changes within cell-free DNA not only establishes it as a potent diagnostic tool but also provides invaluable insights into the underlying biology of metastatic disease. By unraveling the intricate genomic landscape, a deeper understanding of the molecular intricacies that drive PCa progression can be obtained, enabling more precise and personalized therapeutic interventions.

11. DNA methylation biomarkers

Among the diverse array of emerging diagnostic biomarkers, DNA methylation patterns have attracted considerable attention. DNA methylation, an epigenetic modification involving the addition of a methyl group to cytosine residues, plays a pivotal role in the regulation of gene expression. Differences in methylation between normal and cancerous cells have been previously described [172]. In the context of PCa, aberrant DNA methylation patterns serve as potential diagnostic biomarkers [173].

The PCa genome has been extensively studied by identifying specific regions in which DNA methylation alterations are prevalent [174]. These changes in genes associated with crucial cellular processes provide valuable insights into disease progression and aggressiveness [175]. Substantial efforts have been made to develop non-invasive diagnostic assays by deciphering these methylation signatures. Liquid biopsy coupled with methylation-specific techniques allows the detection of epigenetic alterations in circulating tumor DNA or other bodily fluids, offering a minimally invasive yet highly accurate approach to diagnosing PCa [176].

The incorporation of DNA methylation biomarkers into liquid biopsy assays holds immense promise for tailoring personalized treatment strategies for PCa. These markers not only aid in early detection but also provide valuable information regarding disease prognosis and treatment response [177,178]. DNA methylation can distinguish PCa at different Gleason stages (Gleason 6 vs. Gleason 3+4: AUC=0.63; Gleason 6 vs. Gleason 4+3 and 8-10: AUC=0.87) [179]. Additionally, the noninvasive nature of liquid biopsy procedures reduces patient discomfort and offers a convenient avenue for regular monitoring, particularly in cases requiring active surveillance [180,181]. With further advancements, the identification of specific methylation profiles associated with different PCa subtypes may facilitate targeted therapies and revolutionize PCa management.

12. Genomic profiling

Integrated genomic profiling of prostate tumors has revealed a spectrum of clinically actionable molecular alterations, including modifications within the DNA damage repair pathway and PTEN/PI3K signaling. These genomic changes, which influence cell proliferation, tumorigenesis, and cell cycle pathways, are associated with adverse clinical outcomes in PCa. Advanced genomic profiling tests hold promise for the identification of innovative diagnostic, prognostic, and therapeutic molecular biomarkers [182]. Advanced genomic tests such as Oncotype Dx Prostate, Prolaris, and Decipher offer personalized insights for refining PCa diagnosis and treatment. These tests guide active surveillance and aid clinical decision-making [183]. However, their routine application is discouraged by the limited number of prospective studies evaluating their long-term impact on patient outcomes, including quality of life, treatment necessity, and survival.

CONCLUSIONS

The landscape of PCa diagnosis is evolving, emphasizing the need for an integrated, multimodal approach. The incorporation of various biomarkers and diagnostic tools, such as blood-based markers (e.g., 4Kscore test, PHI) and urine-based markers (e.g., SelectMDx and PCA3), offers a more comprehensive assessment of the disease. Ongoing optimization and standardization of assay thresholds will further expand their impact. Exciting emerging biomarkers such as non-coding RNAs, fucosylated PSA, and methylation signatures provide new avenues to unravel PCa biology in real time through liquid biopsy analysis.

However, validation in large cohorts, demonstrating added value over current standards, and assay consistency issues remain challenging. Addressing these gaps through collaborative research will accelerate clinical integration. With insights from basic science combined with technological innovations, liquid biopsy platforms can evolve beyond diagnosis to guide screening, monitoring, prognosis, and therapy selection for improved patient outcomes.

The integration of diverse biomarkers and diagnostic tools represents a shift in the diagnosis of PCa. Although promising, evidence for combined diagnostic approaches is lacking, highlighting a key area for future research. Continued research and collaborative efforts are essential to effectively integrating these diagnostics into personalized management strategies for PCa. Overcoming the current challenges is pivotal for realizing the potential benefits of this integrated approach with the goal of revolutionizing PCa management through early detection, informed risk stratification, and personalized treatment strategies.