Clinical utility of current biomarkers for prostate cancer detection

ProsTAV, a clinically useful test in prostate cancer: an extension study

PURPOSE

To assess the clinical performance of ProsTAV®, a blood-based test based on telomere associate variables (TAV) measurement, to support biopsy decision-making when diagnosing suspicious prostate cancer (PCa).

METHODS

Preliminary data of a prospective observational pragmatic study of patients with prostate-specific antigen (PSA) levels 3-10 ng/ml and suspicious PCa. Results were combined with other clinical data, and all patients underwent prostate biopsies according to each center's routine clinical practice, while magnetic resonance imaging (MRI) before the prostate biopsy was optional. Sensitivity, specificity, positive and negative predicted values, and subjects where biopsies could have been avoided using ProsTAV were determined.

RESULTS

The mean age of the participants (n = 251) was 67.4 years, with a mean PSA of 5.90 ng/ml, a mean free PSA of 18.9%, and a PSA density of 0.14 ng/ml. Digital rectal examination was abnormal in 21.1% of the subjects, and according to biopsy, the prevalence of significant PCa was 47.8%. The area under the ROC curve of ProsTAV was 0.7, with a sensitivity of 0.90 (95% CI, 0.85-0.95) and specificity of 0.27 (95% CI, 0.19-0.34). The positive and negative predictive values were 0.53 (95% CI, 0.46-0.60) and 0.74 (95% CI, 0.62-0.87), respectively. ProsTAV could have reduced the biopsies performed by 27% and showed some initial evidence of a putative benefit in the diagnosis pathway combined with MRI.

CONCLUSIONS

ProsTAV increases the prediction capacity of significant PCa in patients with PSA between 3 and 10 ng/ml and could be considered a complementary tool to improve the patient diagnosis pathway.

Rho GTPase activating protein 21-mediated regulation of prostate cancer associated 3 gene in prostate cancer cell

The overexpression of the prostate cancer antigen 3 (PCA3) gene is well-defined as a marker for prostate cancer (PCa) diagnosis. Although widely used in clinical research, PCA3 molecular mechanisms remain unknown. Herein we used phage display technology to identify putative molecules that bind to the promoter region of PCA3 gene and regulate its expression. The most frequent peptide PCA3p1 (80%) was similar to the Rho GTPase activating protein 21 (ARHGAP21) and its binding affinity was confirmed using Phage Bead ELISA. We showed that ARHGAP21 silencing in LNCaP prostate cancer cells decreased PCA3 and androgen receptor (AR) transcriptional levels and increased prune homolog 2 (PRUNE2) coding gene expression, indicating effective involvement of ARHGAP21 in androgen-dependent tumor pathway. Chromatin immunoprecipitation assay confirmed the interaction between PCA3 promoter region and ARHGAP21. This is the first study that described the role of ARHGAP21 in regulating the PCA3 gene under the androgenic pathway, standing out as a new mechanism of gene regulatory control during prostatic oncogenesis.

The impact of obesity and sexual behavior on prostate cancer risk is mediated by testosterone levels: a mendelian randomization study and mediation analysis

Background

The relationship between obesity, sexual behavior, and prostate cancer (PCa) has been widely debated, contributing to a lack of understanding of its potential mechanisms and hindering the development of effective prevention measures.

Purpose

The aim of this study was to examine the causal effect of body mass index (BMI), age at first sexual intercourse (AFS), and bioavailable testosterone levels on PCa while also quantifying the potential roles of mediators.

Method

We conducted a Mendelian randomization (MR) study using summary statistics from genome-wide associations of BMI (152,893 European males), AFS (182,791 European males), bioavailable testosterone (184,205 European males), and PCa (79,148 cases, 61,106 controls, European ancestry). Inverse-variance weighted method, weighted median method, MR-Egger regression, Least Absolute Shrinkage and Selection Operator (LASSO), and outlier test were used for MR analyses. Reverse MR and mediation analysis were performed. Data analyses were conducted from December 2022 to July 2023.

Results

The results showed that genetic liability to BMI was protective of PCa (OR, 0.82; 95% CI: 0.74-0.91; P = 3.29 × 10-4). Genetic liability to later AFS (OR, 1.28; 95% CI: 1.08-1.53; P = 5.64 × 10-3) and higher bioavailable testosterone levels (OR = 1.11, 95% CI: 1.01-1.24, P = 0.04) were associated with an increased risk of PCa. All of these potential causal effects could only be forwarded and were not affected by prostate specific antigen (PSA) screening. After controlling for bioavailable testosterone levels, the causal impact of BMI and AFS on PCa was no longer significant. The mediation analysis suggested that the causal influence of AFS/BMI on PCa relied on bioavailable testosterone levels.

Conclusion

In conclusion, the difference between the univariable and multivariable MR results suggested that the causal influence of BMI and AFS on PCa relied on bioavailable testosterone levels. Further work is needed to identify other risk factors and to elucidate the specific mechanisms that underlie this causal pathway.

miRNA Expression Patterns in Early- and Late-Stage Prostate Cancer Patients: High-Throughput Analysis

Prostate cancer (PCa) is one of the most common types of cancer among men. To date, there have been no specific markers identified for the diagnosis and prognosis or response to treatment of this disease. Thus, there is an urgent need for promising markers, which may be fulfilled by small non-coding RNAs known as microRNAs (miRNAs). Therefore, the present study aimed to investigate the miRNA profile in tissue samples obtained from patients with PCa using microarrays, followed by reverse transcriptase quantitative PCRs (RT-qPCRs). In the discovery phase, 754 miRNAs were screened in tissues obtained from patients (n = 46) with PCa in early and late stages. Expression levels of miRNA-324-3p, miRNA-429, miRNA-570, and miRNA-616 were found to be downregulated, and miRNA-423-5p expression was upregulated in patients with early-stage cancer compared to the late-stage ones. These five miRNAs were further validated in an independent cohort of samples (n = 39) collected from patients with PCa using RT-qPCR-based assays. MiRNA-324-3p, miRNA-429, miRNA-570, and miRNA-616 expression levels remained significantly downregulated in early-stage cancer tissues compared to late-stage tissues. Remarkably, for a combination of three miRNAs, PSA levels and Gleason scores were able to discriminate between patients with early-stage PCa and late-stage PCa, with an AUC of 95%, a sensitivity of 86%, and a specificity close to 94%. Thus, the data obtained in this study suggest a possible involvement of the identified miRNAs in the pathogenesis of PCa, and they may also have the potential to be developed into diagnostic and prognostic tools for PCa. However, further studies with a larger cohort are needed.

Clinical analysis of EV-Fingerprint to predict grade group 3 and above prostate cancer and avoid prostate biopsy

BACKGROUND

There is an unmet clinical need for minimally invasive diagnostic tests to improve the detection of grade group (GG) ≥3 prostate cancer relative to prostate antigen-specific risk calculators. We determined the accuracy of the blood-based extracellular vesicle (EV) biomarker assay (EV Fingerprint test) at the point of a prostate biopsy decision to predict GG ≥3 from GG ≤2 and avoid unnecessary biopsies.

METHODS

This study analyzed 415 men referred to urology clinics and scheduled for a prostate biopsy, were recruited to the APCaRI 01 prospective cohort study. The EV machine learning analysis platform was used to generate predictive EV models from microflow data. Logistic regression was then used to analyze the combined EV models and patient clinical data and generate the patients' risk score for GG ≥3 prostate cancer.

RESULTS

The EV-Fingerprint test was evaluated using the area under the curve (AUC) in discrimination of GG ≥3 from GG ≤2 and benign disease on initial biopsy. EV-Fingerprint identified GG ≥3 cancer patients with high accuracy (0.81 AUC) at 95% sensitivity and 97% negative predictive value. Using a 7.85% probability cutoff, 95% of men with GG ≥3 would have been recommended a biopsy while avoiding 144 unnecessary biopsies (35%) and missing four GG ≥3 cancers (5%). Conversely, a 5% cutoff would have avoided 31 unnecessary biopsies (7%), missing no GG ≥3 cancers (0%).

CONCLUSIONS

EV-Fingerprint accurately predicted GG ≥3 prostate cancer and would have significantly reduced unnecessary prostate biopsies.

Advancements in MRI-Based Radiomics and Artificial Intelligence for Prostate Cancer: A Comprehensive Review and Future Prospects

The use of multiparametric magnetic resonance imaging (mpMRI) has become a common technique used in guiding biopsy and developing treatment plans for prostate lesions. While this technique is effective, non-invasive methods such as radiomics have gained popularity for extracting imaging features to develop predictive models for clinical tasks. The aim is to minimize invasive processes for improved management of prostate cancer (PCa). This study reviews recent research progress in MRI-based radiomics for PCa, including the radiomics pipeline and potential factors affecting personalized diagnosis. The integration of artificial intelligence (AI) with medical imaging is also discussed, in line with the development trend of radiogenomics and multi-omics. The survey highlights the need for more data from multiple institutions to avoid bias and generalize the predictive model. The AI-based radiomics model is considered a promising clinical tool with good prospects for application.

Clinical Application of Prostatic Exosomal Protein and Prostate-Specific Antigen Levels in the Detection of Prostate-Related Diseases

OBJECTIVE

Our aim was to study the use of prostatic exosomal protein (PSEP) and prostate-specific antigen (PSA) in diagnosis of prostate-related diseases.

METHODS

A total of 54 cases of acute prostatitis (AP), 72 cases of chronic prostatitis (CP), and 36 cases of prostate cancer (PCa) were enrolled. Levels of PSEP and PSA were analyzed.

RESULTS

The positive rate and level of PSEP in CP was highest (both P < .05). The total PSA (tPSA) level in PCa was the highest (P < .05), followed by AP and CP. The free PSA (fPSA) level was lowest in CP (P < .05); fPSA/tPSA in AP was the highest (P < .05). The PSEP level in type II CP was higher than in type IIIa and type IIIb (both P < .05), and it was higher in type IIIa than in type IIIb (P < .05). The tPSA level in type IIIb was the lowest in the 3 types (both P < .05). The fPSA/tPSA in type IIIb was the highest in the 3 types (P < .05).

CONCLUSION

The PSEP combined with PSA better distinguishes prostate-related diseases.

Canine-Inspired Chemometric Analysis of Volatile Organic Compounds in Urine Headspace to Distinguish Prostate Cancer in Mice and Men

Canines can identify prostate cancer with high accuracy by smelling volatile organic compounds (VOCs) in urine. Previous studies have identified VOC biomarkers for prostate cancer utilizing solid phase microextraction (SPME) gas chromatography-mass spectrometry (GC-MS) but have not assessed the ability of VOCs to distinguish aggressive cancers. Additionally, previous investigations have utilized murine models to identify biomarkers but have not determined if the results are translatable to humans. To address these challenges, urine was collected from mice with prostate cancer and men undergoing prostate cancer biopsy and VOCs were analyzed by SPME GC-MS. Prior to analysis, SPME fibers/arrows were compared, and the fibers had enhanced sensitivity toward VOCs with a low molecular weight. The analysis of mouse urine demonstrated that VOCs could distinguish tumor-bearing mice with 100% accuracy. Linear discriminant analysis of six VOCs in human urine distinguished prostate cancer with sensitivity = 75% and specificity = 69%. Another panel of seven VOCs could classify aggressive cancer with sensitivity = 78% and specificity = 85%. These results show that VOCs have moderate accuracy in detecting prostate cancer and a superior ability to stratify aggressive tumors. Furthermore, the overlap in the structure of VOCs identified in humans and mice shows the merit of murine models for identifying biomarker candidates.

Possibilities and limitations of using low biomass samples for urologic disease and microbiome research

With the dogma of sterile urine no longer held as truth, numerous studies have implicated distinct changes in microbial diversity and composition to diseased subgroups in both benign and malignant urological diseases, ranging from overactive bladder to bladder and prostate cancer. Further facilitated by novel and effective techniques of urine culture and sequencing, analysis of the genitourinary microbiome holds high potential to identify biomarkers for disease and prognosis. However, the low biomass of samples included in microbiome studies of the urinary tract challenge researchers to draw definitive conclusions, confounded by technical and procedural considerations that must be addressed. Lack of samples and adequate true negative controls can lead to overestimation of microbial influence with clinical relevance. As such, results from currently available studies and assessment of their limitations required a thorough understanding. The purpose of this narrative review was to summarize notable microbiome studies in the field of urology with a focus on significant findings and limitations of study design. Methodological considerations in future research are also discussed.

Accuracy of novel urinary biomarker tests in the diagnosis of prostate cancer: A systematic review and network meta-analysis

Objective

The purpose of this study was to conduct a network meta-analysis comparing the diagnostic value of different urinary markers for prostate cancer.

Methods

As of June 2022, the literature was retrieved by searching Pubmed, EMBASE, Web of Science databases and other databases. The methodological quality of included studies was assessed using the Cochrane Collaboration’s risk of bias tool, and publication bias was assessed using funnel plots. The surface under the cumulative ranking curve (SUCRA) values ​​was used to determine the most effective diagnostic method and the data were analyzed accordingly using data analysis software.

Results

A total of 16 articles was included including 9952 patients. The ranking results of network meta-analysis showed that the diagnostic performance of the four urine markers Selectmdx, MIPS, PCA3 and EPI was better than that of PSA. Among them, the specificity, positive predictive value and diagnostic accuracy of Selectmdx ranked first in the SUCRA ranking (SUCRA values: 85.2%, 88.3%, 97.1%), and the sensitivity ranked second in the SUCRA ranking (SUCRA value: 54.4%), and the negative predictive value ranked fourth in SUCRA (SUCRA value: 51.6%). The most sensitive screening tool was MIPS (SUCRA value: 67.1%), and it was also the second screening tool ranked higher in specificity, positive predictive value, negative predictive value and diagnostic accuracy (SUCRA value: 56.5%, respectively)., 57.1%, 67.9%, 74.3%). The high negative predictive value SUCRA ranking is EPI (SUCRA value: 68.0%), its sensitivity ranks third (SUCRA value: 45.6%), and its specificity, positive predictive value and diagnostic accuracy are ranked fourth (SUCRA values are: 45%, 38.2%, 35.8%).

Conclusion

According to the network ranking diagram, we finally concluded that Selectmdx and MIPS can be used as the most suitable urine markers for prostate cancer screening and diagnosis. To further explore the diagnostic value of different urinary markers in the screening of PCa patients.

Systematic Review Registration

https://inplasy.com/, identifier INPLASY202290094.

Biomarkers for prostate cancer detection and risk stratification

Although prostate cancer (PCa) is the most commonly diagnosed cancer in men, most patients do not die from the disease. Prostate specific antigen (PSA), the most widely used oncologic biomarker, has revolutionized screening and early detection, resulting in reduced proportion of patients presenting with advanced disease. However, given the inherent limitations of PSA, additional diagnostic and prognostic tools are needed to facilitate early detection and accurate risk stratification of disease. Serum, urine, and tissue-based biomarkers are increasingly being incorporated into the clinical care paradigm, but there is still a limited understanding of how to use them most effectively. In the current article, we review test characteristics and clinical performance data for both serum [4 K score, prostate health index (phi)] and urine [SelectMDx, ExoDx Prostate Intelliscore, MyProstateScore (MPS), and PCa antigen 3 (PCA3)] biomarkers to aid decisions regarding initial or repeat biopsies as well as tissue-based biomarkers (Confirm MDx, Decipher, Oncotype Dx, and Polaris) aimed at risk stratifying patients and identifying those patients most likely to benefit from treatment versus surveillance or monotherapy versus multi-modal therapy.

Visualization and Quantification of the Extracellular Matrix in Prostate Cancer Using an Elastin Specific Molecular Probe

Human prostate cancer (PCa) is a type of malignancy and one of the most frequently diagnosed cancers in men. Elastin is an important component of the extracellular matrix and is involved in the structure and organization of prostate tissue. The present study examined prostate cancer in a xenograft mouse model using an elastin-specific molecular probe for magnetic resonance molecular imaging. Two different tumor sizes (500 mm3 and 1000 mm3) were compared and analyzed by MRI in vivo and histologically and analytically ex vivo. The T1-weighted sequence was used in a clinical 3-T scanner to calculate the relative contrast enhancement before and after probe administration. Our results show that the use of an elastin-specific probe enables better discrimination between tumors and surrounding healthy tissue. Furthermore, specific binding of the probe to elastin fibers was confirmed by histological examination and laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS). Smaller tumors showed significantly higher signal intensity (p > 0.001), which correlates with the higher proportion of elastin fibers in the histological evaluation than in larger tumors. A strong correlation was seen between relative enhancement (RE) and Elastica-van Gieson staining (R2 = 0.88). RE was related to inductively coupled plasma-mass spectrometry data for Gd and showed a correlation (R2 = 0.78). Thus, molecular MRI could become a novel quantitative tool for the early evaluation and detection of PCa.

Identification of a novel non-invasive biological marker to overcome the shortcomings of PSA in diagnosis and risk stratification for prostate cancer: Initial prospective study of developmental endothelial locus-1 protein

Objective

This prospective study sought to clarify the developmental endothelial locus-1 (Del-1) protein as values of diagnosis and risk stratification of prostate cancer (PCa).

Design

From February 2017 to December 2019, a total 458 patients who underwent transrectal ultrasound guided prostate biopsy or surgery of benign prostatic hyperplasia agreed to research of Del-1 protein. We prospectively compared and analyzed the Del-1 protein and prostate specific antigen (PSA) in relation to the patients’ demographic and clinicopathological characteristics.

Results

Mean age was 68.86±8.55 years. Mean PSA and Del-1 protein was 21.72±89.37, 0.099±0.145, respectively. Two hundred seventy-six (60.3%) patients were diagnosed as PCa. Among them, 181 patients underwent radical prostatectomy (RP). There were significant differences in Del-1 protein between benign and PCa group (0.066±0.131 vs 0.121±0.149, respectively, p<0.001). When we set the cut-off value of del-1 protein as 0.120, in patients with 3≤PSA≤8, positive predictive value and specificity of Del-1 protein (≥0.120) for predicting PCa were 88.9% (56/63) and 93.5% (101/108), respectively. Among 181 patients who underwent RP, there were significant differences in Del-1 protein according to stage (pT2 vs pT3a vs ≥pT3b) (0.113±0.078, 0.171±0.121, 0.227±0.161, respectively, p<0.001) and to Gleason score (6 (3+3) or 7 (3+4) vs 7 (4+3) or 8 (4+4) vs 9 or 10) (0.134±0.103, 0.150±0.109, 0.212±0.178, respectively, P = 0.044). Multivariate analysis showed that PSA, Del-1 protein and high Gleason score (≥9) were the independent prognostic factors for predicting higher pT stage (≥3b). Furthermore, age, PSA and Del-1 protein were independent prognostic factors for predicting significant PCa.

Conclusion

Patients with PCa showed higher expression of Del-1 protein than benign patients. Del-1 protein increased with the stage and Gleason score of PCa. Collaboration with PSA, Del-1 protein can be a non-invasive useful marker for diagnosis and risk stratification of PCa.