Novel Diagnostic Biomarkers of Prostate Cancer: An Update

From molecular mechanisms of prostate cancer to translational applications: based on multi-omics fusion analysis and intelligent medicine

Prostate cancer is the most common cancer in men worldwide and has a high mortality rate. The complex and heterogeneous development of prostate cancer has become a core obstacle in the treatment of prostate cancer. Simultaneously, the issues of overtreatment in early-stage diagnosis, oligometastasis and dormant tumor recognition, as well as personalized drug utilization, are also specific concerns that require attention in the clinical management of prostate cancer. Some typical genetic mutations have been proved to be associated with prostate cancer's initiation and progression. However, single-omic studies usually are not able to explain the causal relationship between molecular alterations and clinical phenotypes. Exploration from a systems genetics perspective is also lacking in this field, that is, the impact of gene network, the environmental factors, and even lifestyle behaviors on disease progression. At the meantime, current trend emphasizes the utilization of artificial intelligence (AI) and machine learning techniques to process extensive multidimensional data, including multi-omics. These technologies unveil the potential patterns, correlations, and insights related to diseases, thereby aiding the interpretable clinical decision making and applications, namely intelligent medicine. Therefore, there is a pressing need to integrate multidimensional data for identification of molecular subtypes, prediction of cancer progression and aggressiveness, along with perosonalized treatment performing. In this review, we systematically elaborated the landscape from molecular mechanism discovery of prostate cancer to clinical translational applications. We discussed the molecular profiles and clinical manifestations of prostate cancer heterogeneity, the identification of different states of prostate cancer, as well as corresponding precision medicine practices. Taking multi-omics fusion, systems genetics, and intelligence medicine as the main perspectives, the current research results and knowledge-driven research path of prostate cancer were summarized.

2022 Update on Prostate Cancer Epidemiology and Risk Factors-A Systematic Review

CONTEXT

Prostate cancer (PCa) is one of the most common cancers worldwide. Understanding the epidemiology and risk factors of the disease is paramount to improve primary and secondary prevention strategies.

OBJECTIVE

To systematically review and summarize the current evidence on the descriptive epidemiology, large screening studies, diagnostic techniques, and risk factors of PCa.

EVIDENCE ACQUISITION

PCa incidence and mortality rates for 2020 were obtained from the GLOBOCAN database of the International Agency for Research on Cancer. A systematic search was performed in July 2022 using PubMed/MEDLINE and EMBASE biomedical databases. The review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines and was registered in PROSPERO (CRD42022359728).

EVIDENCE SYNTHESIS

Globally, PCa is the second most common cancer, with the highest incidence in North and South America, Europe, Australia, and the Caribbean. Risk factors include age, family history, and genetic predisposition. Additional factors may include smoking, diet, physical activity, specific medications, and occupational factors. As PCa screening has become more accepted, newer approaches such as magnetic resonance imaging (MRI) and biomarkers have been implemented to identify patients who are likely to harbor significant tumors. Limitations of this review include the evidence being derived from meta-analyses of mostly retrospective studies.

CONCLUSIONS

PCa remains the second most common cancer among men worldwide. PCa screening is gaining acceptance and will likely reduce PCa mortality at the cost of overdiagnosis and overtreatment. Increasing use of MRI and biomarkers for the detection of PCa may mitigate some of the negative consequences of screening.

PATIENT SUMMARY

Prostate cancer (PCa) remains the second most common cancer among men, and screening for PCa is likely to increase in the future. Improved diagnostic techniques can help reduce the number of men who need to be diagnosed and treated to save one life. Avoidable risk factors for PCa may include factors such as smoking, diet, physical activity, specific medications, and certain occupations.

Combining Molecular Subtypes with Multivariable Clinical Models Has the Potential to Improve Prediction of Treatment Outcomes in Prostate Cancer at Diagnosis

Clinical management of prostate cancer is challenging because of its highly variable natural history and so there is a need for improved predictors of outcome in non-metastatic men at the time of diagnosis. In this study we calculated the model score from the leading clinical multivariable model, PREDICT prostate, and the poor prognosis DESNT molecular subtype, in a combined expression and clinical dataset that were taken from malignant tissue at prostatectomy (n = 359). Both PREDICT score (p < 0.0001, IQR HR = 1.59) and DESNT score (p < 0.0001, IQR HR = 2.08) were significant predictors for time to biochemical recurrence. A joint model combining the continuous PREDICT and DESNT score (p < 0.0001, IQR HR = 1.53 and 1.79, respectively) produced a significantly improved predictor than either model alone (p < 0.001). An increased probability of mortality after diagnosis, as estimated by PREDICT, was characterised by upregulation of cell-cycle related pathways and the downregulation of metabolism and cholesterol biosynthesis. The DESNT molecular subtype has distinct biological characteristics to those associated with the PREDICT model. We conclude that the inclusion of biological information alongside current clinical prognostic tools has the potential to improve the ability to choose the optimal treatment pathway for a patient.

A Novel Nomogram Based on Initial Features to Predict BPH Progression

Objectives: The aim of this study was to establish a tool to identify patients at risk for pharmaceutical and surgical interventions for benign prostatic hyperplasia (BPH)-related lower urinary tract symptoms (LUTS) over a 10 year follow-up. Methods: The data of patients with mild to moderate male LUTS undergoing phytotherapy from January to December 2010 were reviewed. Patients were followed for 10 years through medical visits and telephone consultations. The outcomes were (1) treatment switch from phytotherapy or no therapy to alpha-blockers or 5α-reductase inhibitors (5-ARI), and (2) clinical progression (acute urinary retention or need for surgery). Two calibrated nomograms (one for each outcome) were constructed on significant predictors at multivariate analysis. Results: A total of 107 patients with a median age of 55 years at presentation were included; 47% stopped or continued phytotherapy, while 53% switched to alpha-blockers and/or 5-ARI after a median time of 24 months. One-third in the second group experienced clinical progression after a median time of 54 months. Age, symptom score, peak flow rate (Qmax), prostate-specific antigen (PSA), and post-void residual volume were significantly associated with the outcomes. According to our nomograms, patients switching therapy or progressing clinically had average scores of 75% and 40% in the dedicated nomograms, respectively, as compared to 25% and <5% in patients who did not reach any outcome. Conclusions: We developed a nomogram to predict the risk of pharmaceutical or surgical interventions for BPH-related LUTS at 10 years from presentation. On the basis of our models, thresholds of >75% and >40% for high risk and <25% and <5% for low risk of pharmaceutical or surgical interventions, respectively, can be proposed.

The Potential Diagnostic and Prognostic Value of Circulating MicroRNAs in the Assessment of Patients With Prostate Cancer: Rational and Progress

Prostate cancer (P.C.) is one of the most frequent diagnosed cancers among men and the first leading cause of death with an annual incidence of 1.4 million worldwide. Prostate-specific antigen is being used for screening/diagnosis of prostate disease, although it is associated with several limitations. Thus, identification of novel biomarkers is warranted for diagnosis of patients at earlier stages. MicroRNAs (miRNAs) are recently being emerged as potential biomarkers. It has been shown that these small molecules can be circulated in body fluids and prognosticate the risk of developing P.C. Several miRNAs, including MiR-20a, MiR-21, miR-375, miR-378, and miR-141, have been proposed to be expressed in prostate cancer. This review summarizes the current knowledge about possible molecular mechanisms and potential application of tissue specific and circulating microRNAs as diagnosis, prognosis, and therapeutic targets in prostate cancer.

CALCB rs3829222 T/T Genotype and Low Expression of CALCB Are High-Risk Factors for Adenoid Cystic Carcinoma of Salivary Gland

Objectives

To investigate the relationship between polymorphisms of calcitonin-related peptide gene II (beta-calcitonin gene-related peptide (βCGRP), CALCB) and serum CGRP levels in salivary adenoid cystic carcinoma.

Materials and Methods

Using the polymerase chain reaction (PCR) technique, the full-length amplification and genotype analysis of CALCB genes were performed in 39 patients with adenoid cystic carcinoma of salivary gland and 158 normal controls. The gene frequencies of major genotype of CALCB in adenoid cystic carcinoma of salivary gland and normal control group were analyzed. Enzyme-linked immunosorbent assay (ELISA) was used to evaluate serum calcitonin gene-related peptide (CGRP) and its concentration of alpha and beta subtypes.

Results

Univariate logistic regression analysis showed that the CALCB rs2839222 T/T genotype was closely related to the occurrence of salivary adenoid cystic carcinoma, with a correlation coefficient of 3.89.

Conclusions

The serum CGRP concentration in the salivary adenoid cystic carcinoma group was 1.56 times that of the normal control group. The αCGRP subtype was significant, which was 3.02 times that of the normal control. The polymorphism of βCGRP gene is associated with genetic susceptibility to salivary adenoid cystic carcinoma, and serum CGRP and βCGRP can be used as novel markers of salivary adenoid cystic carcinoma.

Urinary microRNA-1913 to microRNA-3659 expression ratio as a non-invasive diagnostic biomarker for prostate cancer

PURPOSE

MicroRNAs (miRNAs) are small non-coding RNAs and are involved in the development, proliferation, and pathogenesis of prostate cancer (PCa). Urinary miRNAs are promising non-invasive biomarkers for PCa diagnosis because of their stability in urine. Here, we evaluated the diagnostic value of urinary miR-1913 to miR-3659 ratio in PCa patients and benign prostate hyperplasia (BPH) controls.

MATERIALS AND METHODS

Candidate miRNAs were identified from urinary microarray data and tested by real-time PCR. The urinary miR-1913 to miR-3659 expression ratio was selected and tested in 83 urine samples (44 PCa and 39 BPH) to confirm its validity as a non-invasive diagnostic biomarker for PCa.

RESULTS

The expression ratio of urinary miR-1913 to miR-3659 was significantly higher in PCa than in BPH (p=0.002) and showed a higher area under the receiver operating characteristic curve than prostate-specific antigen (PSA; 0.821 vs. 0.518) in patients within the PSA gray zone (tPSA: 3-10 ng/mL), with sensitivity of 75.0% and specificity of 78.6% (p=0.003).

CONCLUSIONS

The urinary miR-1913 to miR-3659 expression ratio was increased in PCa and may serve as a useful supplemental biomarker to PSA for the diagnosis of PCa, particularly in patients within the PSA gray zone.

LMTK2 as Potential Biomarker for Stratification between Clinically Insignificant and Clinically Significant Prostate Cancer

A set of prostate tumors tend to grow slowly and do not require active treatment. Therefore, stratification between patients with clinically significant and clinically insignificant prostate cancer (PC) remains a vital issue to avoid overtreatment. Fast development of genetic technologies accelerated development of next-generation molecular tools for reliable PC diagnosis. The aim of this study is to evaluate the diagnostic value of molecular biomarkers (CRISP3, LMTK2, and MSMB) for separation of PC cases from benign prostatic changes and more specifically for identification of clinically significant PC from all pool of PC cases in patients with rising PSA levels. Patients (n = 200) who had rising PSA (PSA II) after negative transrectal systematic prostate biopsy due to elevated PSA (PSA I) were eligible to the study. In addition to PSA concentration, PSA density was calculated for each patient. Gene expression level was measured in peripheral blood samples of cases applying RT-PCR, while MSMB (−57 C/T) polymorphism was identified by pyrosequencing. LMTK2 and MSMB significantly differentiated control group from both BPD and PC groups. MSMB expression tended to increase from the major alleles of the CC genotype to the minor alleles of the TT genotype. PSA density was the only clinical characteristic that significantly differentiated clinically significant PC from clinically insignificant PC. Therefore, LMTK2 expression and PSA density were significantly distinguished between clinically significant PC and clinically insignificant PC. PSA density rather than PSA can differentiate PC from the benign prostate disease and, in combination with LMTK2, assist in stratification between clinically insignificant and clinically significant PC.

Clinical utility of current biomarkers for prostate cancer detection

Although prostate-specific antigen (PSA) remains the most used test to detect prostate cancer (PCa), the limited specificity and an elevated rate of overdiagnosis are the main problems associated with PSA testing. Over the last three decades, a large body of evidence has indicated that PSA screening methods for PCa are problematic, although PSA screening significantly reduces PCa-specific mortality. A number of novel biomarkers have been introduced to overcome these limitations of PSA in the clinical setting. These biomarkers have demonstrated an increased ability to select patients for biopsy and identify men at risk for clinically significant PCa. Although a number of assays require further validation, initial data are promising. Forthcoming results will ultimately determine the clinical utility and commercial availability of these assays. Extensive efforts have recently been made to identify and commercialize novel PCa biomarkers for more effective detection of PCa, either alone or in combination with currently available clinical tools. This review highlights the role of existing and promising serum and urinary biomarkers for the detection and prognostication of PCa before prostate biopsy.

Expression of AR-V7 (Androgen Receptor Variant 7) Protein in Granular Cytoplasmic Structures Is an Independent Prognostic Factor in Prostate Cancer Patients

Simple Summary

The expression of the androgen receptor (AR) and its splice variant AR-V7 is crucial for prostate cancer (PCa) biology. An immunohistochemical staining was performed on a tissue microarray with specimens from 410 PCa patients. AR staining, neither in the nucleus nor in the cytoplasm was associated with prognosis. AR-V7 staining of the general cytoplasm was associated with a shorter relapse free survival (RFS), whereas AR-V7 staining of cytoplasmic granules was associated with a longer RFS. Further subgroup stratification for AR-V7 granular staining revealed it as an independent prognostic factor in younger patients (age ≤ 65), patients with negative CK20 staining and patients with perineural invasion. Altogether, AR-V7 protein detected in granular cytoplasmic structures is an independent prognostic factor for RFS in PCa patients.

Abstract

Prostate cancer (PCa) is the second most common cancer, causing morbidity and mortality among men world-wide. The expression of the androgen receptor (AR) and its splice variants is a crucial factor of prostate cancer biology that has not been comprehensively studied in PCa tumors. The aim of this study was to characterize the protein expression of the AR and its splice variant, AR-V7, and their subcellular distributions in PCa by immunohistochemistry and to correlate the results to the clinicopathological data and prognosis. Immunohistochemical staining for AR and AR-V7 was performed on a tissue microarray (TMA) with specimens from 410 PCa patients using an immunoreactive score (IRS) or only the percentage of AR-V7 staining in cytoplasmic granules. Nuclear or cytoplasmic AR staining was not associated with prognosis. AR-V7 staining was only occasionally observed in the nucleus. However, AR-V7 staining in the cytoplasm or in cytoplasmic granules was associated with relapse-free survival (RFS). AR-V7 staining of the cytoplasm was associated with a shorter RFS, whereas AR-V7 staining of cytoplasmic granules was associated with a longer RFS. In a multivariate Cox’s regression analysis, only negative (<5%) AR-V7 staining of cytoplasmic granules remained an independent prognostic factor for RFS (HR = 5.3; p = 0.006). In a further subgroup analysis by multivariate Cox’s regression analysis, AR-V7 was an independent prognostic factor in the following groups: age ≤ 65 (HR = 9.7; p = 0.029), negative CK20 staining (HR = 7.0; p = 0.008), and positive perineural invasion (HR = 3.7; p = 0.034). Altogether, AR-V7 protein in granular cytoplasmic structures is an independent prognostic factor for RFS in PCa patients.

Expression of GP88 (Progranulin) Protein Is an Independent Prognostic Factor in Prostate Cancer Patients

Prostate cancer, the second most common cancer, is still a major cause of morbidity and mortality among men worldwide. The expression of the survival and proliferation factor progranulin (GP88) has not yet been comprehensively studied in PCa tumors. The aim of this study was to characterize GP88 protein expression in PCa by immunohistochemistry and to correlate the findings to the clinico-pathological data and prognosis. Immunohistochemical staining for GP88 was performed by TMA with samples from 442 PCa patients using an immunoreactive score (IRS). Altogether, 233 cases (52.7%) with negative GP88 staining (IRS < 2) and 209 cases (47.3%) with positive GP88 staining (IRS ≥ 2) were analyzed. A significant positive correlation was found for the GP88 IRS with the PSA value at prostatectomy and the cytoplasmic cytokeratin 20 IRS, whereas it was negatively associated with follow-up times. The association of GP88 staining with prognosis was further studied by survival analyses (Kaplan-Meier, univariate and multivariate Cox's regression analysis). Increased GP88 protein expression appeared as an independent prognostic factor for overall, disease-specific and relapse-free survival in all PCa patients. Interestingly, in the subgroup of younger PCa patients (≤65 years), GP88 positivity was associated with a 3.8-fold (p = 0.004), a 6.0-fold (p = 0.008) and a 3.7-fold (p = 0.003) increased risk for death, disease-specific death and occurrence of a relapse, respectively. In the PCa subgroup with negative CK20 staining, GP88 positivity was associated with a 1.8-fold (p = 0.018) and a 2.8-fold increased risk for death and disease-specific death (p = 0.028). Altogether, GP88 protein positivity appears to be an independent prognostic factor for PCa patients.

Prostate-specific antigen glycoprofiling as diagnostic and prognostic biomarker of prostate cancer

The initial part of this review details the controversy behind the use of a serological level of prostate-specific antigen (PSA) for the diagnostics of prostate cancer (PCa). Novel biomarkers are in demand for PCa diagnostics, outperforming traditional PSA tests. The review provides a detailed and comprehensive summary that PSA glycoprofiling can effectively solve this problem, thereby considerably reducing the number of unnecessary biopsies. In addition, PSA glycoprofiling can serve as a prognostic PCa biomarker to identify PCa patients with an aggressive form of PCa, avoiding unnecessary further treatments which are significantly life altering (incontinence or impotence).

Prediction Medicine: Biomarkers, Risk Calculators and Magnetic Resonance Imaging as Risk Stratification Tools in Prostate Cancer Diagnosis

This review discusses the most recent evidence for currently available risk stratification tools in the detection of clinically significant prostate cancer (csPCa), and evaluates diagnostic strategies that combine these tools. Novel blood biomarkers, such as the Prostate Health Index (PHI) and 4Kscore, show similar ability to predict csPCa. Prostate cancer antigen 3 (PCA3) is a urinary biomarker that has inferior prediction of csPCa compared to PHI, but may be combined with other markers like TMPRSS2-ERG to improve its performance. Original risk calculators (RCs) have the advantage of incorporating easy to retrieve clinical variables and being freely accessible as a web tool/mobile application. RCs perform similarly well as most novel biomarkers. New promising risk models including novel (genetic) markers are the SelectMDx and Stockholm-3 model (S3M). Prostate magnetic resonance imaging (MRI) has evolved as an appealing tool in the diagnostic arsenal with even stratifying abilities, including in the initial biopsy setting. Merging biomarkers, RCs and MRI results in higher performances than their use as standalone tests. In the current era of prostate MRI, the way forward seems to be multivariable risk assessment based on blood and clinical parameters, potentially extended with information from urine samples, as a triaging test for the selection of candidates for MRI and biopsy.

An Ultra-Rapid Biosensory Point-of-Care (POC) Assay for Prostate-Specific Antigen (PSA) Detection in Human Serum

Prostate-specific antigen (PSA) is the established routine screening tool for the detection of early-stage prostate cancer. Given the laboratory-centric nature of the process, the development of a portable, ultra rapid high-throughput system for PSA screening is highly desirable. In this study, an advancedpoint-of-care system for PSA detection in human serum was developed based on a cellular biosensor where the cell membrane was modified by electroinserting a specific antibody against PSA. Thirty nine human serum samples were used for validation of this biosensory system for PSA detection. Samples were analyzed in parallel with a standard immunoradiometric assay (IRMA) and an established electrochemical immunoassay was used for comparison purposes. They were classified in three different PSA concentration ranges (0, <4 and ≥4 ng/mL). Cells membrane-engineered with 0.25 μg/mL anti-PSA antibody demonstrated a statistically lower response against the upper (≥4 ng/mL) PSA concentration range. In addition, the cell-based biosensor performed better than the immunosensor in terms of sensitivity and resolution against positive samples containing <4 ng/mL PSA. In spite of its preliminary, proof-of-concept stage of development, the cell-based biosensor could be used as aninitiative for the development of a fast, low-cost, and high-throughput POC screening system for PSA.