PSA Based Biomarkers, Imagistic Techniques and Combined Tests for a Better Diagnostic of Localized Prostate Cancer

Advancements in Biomarkers of Prostate Cancer: A Review

Prostate cancer (PCa) is one of the most prevalent and deadly cancers among men, particularly affecting men of African descent and contributing significantly to cancer-related morbidity and mortality worldwide. The disease varies widely, from slow-developing forms to highly aggressive or potentially fatal variants. Accurate risk stratification is crucial for making therapeutic decisions and designing adequate clinical trials. This review assesses a broad spectrum of diagnostic and prognostic biomarkers, many of which are incorporated into clinical guidelines, including the Prostate Health Index (PHI), 4Kscore, STHLM3, PCA3, SelectMDx, ExoDx Prostate Intelliscore (EPI), and MiPS. It also highlights emerging biomarkers with preclinical support, such as urinary non-coding RNAs and DNA methylation patterns. Additionally, the review explores the role of tumor-associated microbiota in PCa, offering new insights into its potential contributions to disease understanding. By examining the latest advancements in PCa biomarkers, this review enhances understanding their roles in disease management.

SIGNIFICANCE OF OSTEOPONTIN FOR PREDICTING AGGRESSIVENESS OF PROSTATE CANCER

BACKGROUND

Effective prediction of the course of prostate cancer (PCa) and the stratification of treatment tactics largely depend on the use of prognostic markers that reflect the molecular and biological features of tumors. In view of the important role of matricellular proteins in the modulation of the growing tumor and metastasis of the hormone-dependent neoplasms, the aim of the work was to study the expression of osteopontin (OPN) at the protein and mRNA levels in the PCa tissue in order to assess the significance of this protein for predicting the aggressiveness of PCa.

MATERIALS AND METHODS

The work is based on the analysis of the results of the examination and treatment of 83 patients with PCa of stages II-IV. The study of OPN expression at the level of mRNA and protein in the PCa tissue was carried out using methods of the real time polymerase chain reaction and immunohistochemistry, respectively.

RESULTS

The OPN expression in the PCa tissue was 1.6 times (p < 0.05) higher in patients with regional lymph node metastases compared to patients without metastases. In patients with a Gleason score of < 7, the OPN expression in the tumor tissue was 1.4 times lower (p < 0.05) than in patients with poorly differentiated PCa. In patients with a high risk of tumor progression, the OPN expression level was 1.4 and 2.1 times higher (p < 0.05) compared to patients with a moderate and low risk of PCa progression. The patients with a high OPN expression level in the PCa tissue had significantly decreased 2-year recurrence-free survival rate (by 25%).

CONCLUSIONS

The obtained results indicate the expediency of using OPN expression indicators in the tumor tissue to predict the PCa aggressiveness and assess the risk of its recurrence.

Engaging disparities in prostate cancer: Piloting an interactive, virtual workshop to educate providers on shared decision-making for underserved populations

OBJECTIVE

Shared decision-making (SDM) is an approach to patient-centered care that is strongly recommended when counseling patients for screening and treatment of prostate cancer. However, providers report lack of comfort with SDM and particularly in disparate populations. We report our experience designing and piloting an online workshop to educate practicing urologists on SDM in diverse populations. Our objective was to create a valued interactive SDM workshop to help urologists learn to lead SDM discussions with men form underserved populations. Therefore, we tested the hypothesis that urologists would agree or strongly agree that we met our learning objectives on postcourse survey.

MATERIALS AND METHODS

With the support of the American Urologic Association, we developed a case-based workshop with interactive role-playing to demonstrate and teach integration of SDM into clinical care. Cases were centered around screening and treatment decisions for localized prostate cancer in diverse patients. Brief surveys were used to track success with learning objectives and urologists' satisfaction with the workshop.

RESULTS

The session included 14 participants from 6 countries. A postworkshop survey indicated that 100% of respondents (8 of 8) "strongly agreed" that the activity met learning objectives, and 100% rated the session as "good" (1), "very good" (1), or "excellent" (6). Participants' knowledge also improved on shared decision-making concepts and the knowledge was maintained one month after the workshop.

CONCLUSION

We successfully created and piloted an interactive online workshop to improve urologists' comfort using shared decision-making in caring for diverse patient populations. The course met its objectives and participant feedback for the course was positive. Sharing this process and framework for development of this intervention may inform future workshops that can be applied to medical students, residents, and providers.

A Neural Network Model Combining [-2]proPSA, freePSA, Total PSA, Cathepsin D, and Thrombospondin-1 Showed Increased Accuracy in the Identification of Clinically Significant Prostate Cancer

BACKGROUND

The Prostate Health Index (PHI) and Proclarix (PCLX) have been proposed as blood-based tests for prostate cancer (PCa). In this study, we evaluated the feasibility of an artificial neural network (ANN)-based approach to develop a combinatorial model including PHI and PCLX biomarkers to recognize clinically significant PCa (csPCa) at initial diagnosis.

METHODS

To this aim, we prospectively enrolled 344 men from two different centres. All patients underwent radical prostatectomy (RP). All men had a prostate-specific antigen (PSA) between 2 and 10 ng/mL. We used an artificial neural network to develop models that can identify csPCa efficiently. As inputs, the model uses [-2]proPSA, freePSA, total PSA, cathepsin D, thrombospondin, and age.

RESULTS

The output of the model is an estimate of the presence of a low or high Gleason score PCa defined at RP. After training on a dataset of up to 220 samples and optimization of the variables, the model achieved values as high as 78% for sensitivity and 62% for specificity for all-cancer detection compared with those of PHI and PCLX alone. For csPCa detection, the model showed 66% (95% CI 66-68%) for sensitivity and 68% (95% CI 66-68%) for specificity. These values were significantly different compared with those of PHI (p < 0.0001 and 0.0001, respectively) and PCLX (p = 0.0003 and 0.0006, respectively) alone.

CONCLUSIONS

Our preliminary study suggests that combining PHI and PCLX biomarkers may help to estimate, with higher accuracy, the presence of csPCa at initial diagnosis, allowing a personalized treatment approach. Further studies training the model on larger datasets are strongly encouraged to support the efficiency of this approach.

New Insights into the Multivariate Analysis of SER Spectra Collected on Blood Samples for Prostate Cancer Detection: Towards a Better Understanding of the Role Played by Different Biomolecules on Cancer Screening: A Preliminary Study

Simple Summary

In recent years, research on biofluids using Raman and SERS has expanded dramatically, indicating the enormous promise of this technology as a high-throughput tool for identifying cancer and other disorders. In the investigations thus far, researchers have concentrated on a specific illness or condition, but the techniques employed to acquire experimental spectra prevent direct comparison of the data. This necessitates comparative research of a variety of diseases and an increase in scientific cooperation to standardize experimental conditions. In our study, positive results were reached by applying a combined SERS multivariate analysis (MVA) to the urgent problem of prostate cancer diagnosis that was directly linked to real-world settings in healthcare. Moreover, in comparison to the prostate-specific antigen (PSA) test, which has a high sensitivity but limited specificity, our combined SERS-MVA method has greater specificity, which may assist in preventing the overtreatment of patients.

Abstract

It is possible to obtain diagnostically relevant data on the changes in biochemical elements brought on by cancer via the use of multivariate analysis of vibrational spectra recorded on biological fluids. Prostate cancer and control groups included in this research generated almost similar SERS spectra, which means that the values of peak intensities present in SERS spectra can only give unspecific and limited information for distinguishing between the two groups. Our diagnostic algorithm for prostate cancer (PCa) differentiation was built using principal component analysis and linear discriminant analysis (PCA-LDA) analysis of spectral data, which has been widely used in spectral data management in many studies and has shown promising results so far. In order to fully utilize the entire SERS spectrum and automatically determine the most meaningful spectral features that can be used to differentiate PCa from healthy patients, we perform a multivariate analysis on both the entire and specific spectral intervals. Using the PCA-LDA model, the prostate cancer and control groups are clearly distinguished in our investigation. The separability of the following two data sets is also evaluated using two alternative discrimination techniques: principal least squares discriminant analysis (PLS-DA) and principal component analysis—support vector machine (PCA-SVM).

Identification of the metabolic signatures of prostate cancer by mass spectrometry-based plasma and urine metabolomics analysis

OBJECTIVE

Prostate cancer (PCa) is one of the most commonly diagnosed cancers among men which is associated with profound metabolic changes. Systematic analysis of the metabolic alterations and identification of new biomarkers may benefit PCa diagnosis and a deep understanding of the pathological mechanism. The purpose of this study was to determine the metabolic features of PCa.

METHODS

Plasma and urine metabolites from 89 prostate cancer (PCa) patients, 84 benign prostatic hyperplasia (BPH) patients, and 70 healthy males were analyzed using LC-MS/MS and GC-MS. The Orthogonalised Partial Least Squares Discriminant Analysis (OPLS-DA) was used to find the significantly changed metabolites. The clinical value of the candidate markers was examined by receiver operating characteristic curve analysis and compared with prostate-specific antigen (PSA).

RESULTS

Multivariate statistical analyses found a series of altered metabolites, which related to the urea cycle, tricarboxylic acid cycle (TCA), fatty acid metabolism, and the glycine cleavage system. Plasma Glu/Gln showed the highest predictive value (AUC = 0.984) when differentiating PCa patients from healthy controls, with a higher sensitivity than PSA (96.6% vs. 94.4%). Both Glu/Gln and PSA displayed a low specificity when differentiating PCa patients from BPH patients (<53.2%), while the combination of Glu/Gln and PSA can further increase the diagnostic specificity to 66.9%.

CONCLUSIONS

The present study showed the metabolic features of PCa, provided strong evidence that the amide nitrogen and the energy metabolic pathways could be a valuable source of markers for PCa. Several candidate markers identified in this study were clinically valuable for further assessment.

The Role and Significance of Bioumoral Markers in Prostate Cancer

The prostate is one of the most clinically accessible internal organs of the genitourinary tract in men. For decades, the only method of screening for prostate cancer (PCa) has been digital rectal examination of 1990s significantly increased the incidence and prevalence of PCa and consequently the morbidity and mortality associated with this disease. In addition, the different types of oncology treatment methods have been linked to specific complications and side effects, which would affect the patient's quality of life. In the first two decades of the 21st century, over-detection and over-treatment of PCa patients has generated enormous costs for health systems, especially in Europe and the United States. The Prostate Specific Antigen (PSA) is still the most common and accessible screening blood test for PCa, but with low sensibility and specificity at lower values (<10 ng/mL). Therefore, in order to avoid unnecessary biopsies, several screening tests (blood, urine, or genetic) have been developed. This review analyzes the most used bioumoral markers for PCa screening and also those that could predict the evolution of metastases of patients diagnosed with PCa.

Diagnostic Strategies for Treatment Selection in Advanced Prostate Cancer

Prostate Cancer (PCa) is a leading cause of morbidity and mortality among men worldwide. For most men with PCa, their disease will follow an indolent course. However, advanced PCa is associated with poor outcomes. There has been an advent of new therapeutic options with proven efficacy for advanced PCa in the last decade which has improved survival outcomes for men with this disease. Despite this, advanced PCa continues to be associated with a high rate of death. There is a lack of strong evidence guiding the timing and sequence of these novel treatment strategies. This paper focuses on a review of the strategies for diagnostic and the current evidence available for treatment selection in advanced PCa.

Optimized Identification of High-Grade Prostate Cancer by Combining Different PSA Molecular Forms and PSA Density in a Deep Learning Model

After skin cancer, prostate cancer (PC) is the most common cancer among men. The gold standard for PC diagnosis is based on the PSA (prostate-specific antigen) test. Based on this preliminary screening, the physician decides whether to proceed with further tests, typically prostate biopsy, to confirm cancer and evaluate its aggressiveness. Nevertheless, the specificity of the PSA test is suboptimal and, as a result, about 75% of men who undergo a prostate biopsy do not have cancer even if they have elevated PSA levels. Overdiagnosis leads to unnecessary overtreatment of prostate cancer with undesirable side effects, such as incontinence, erectile dysfunction, infections, and pain. Here, we used artificial neuronal networks to develop models that can diagnose PC efficiently. The model receives as an input a panel of 4 clinical variables (total PSA, free PSA, p2PSA, and PSA density) plus age. The output of the model is an estimate of the Gleason score of the patient. After training on a dataset of 190 samples and optimization of the variables, the model achieved values of sensitivity as high as 86% and 89% specificity. The efficiency of the method can be improved even further by training the model on larger datasets.