A Multiplex RT-QPCR Assay of ONECUT2, EZH2, and BRCA2 for the Detection of Clinically Significant Prostate Cancer in...

Background/Objectives: Needle biopsy-based molecular assays that are sensitive, cost-effective, and reproducible could improve prostate cancer (PCa) diagnosis and risk stratification. We developed a compact, biologically informed gene expression signature panel (ONECUT2, EZH2, BRCA2) for cancer detection and aggressiveness stratification. Methods: We designed and validated a multiplex real-time RT-qPCR assay for ONECUT2, EZH2, and BRCA2. Biomarker selection was guided by The Cancer Genome Atlas Prostate Adenocarcinoma (TCGA-PRAD) and Gene Expression Omnibus (GEO) cohort analyses. The assay was applied to 979 ex vivo needle biopsy cores from 255 radical prostatectomy specimens; machine-learning models (Random Forest, AdaBoost, Elastic Net) generated an RNA signature. Diagnostic and prognostic performance was assessed by the area under the receiver operating characteristic curve (AUC), model comparisons by DeLong's test, and biochemical recurrence-free survival (BCR) by Kaplan-Meier and Cox proportional hazards analyses. Reproducibility based on sample pairs was assessed by intraclass correlation coefficients (ICC). Results: The three-gene expression signature (ONECUT2, EZH2, and BRCA2) robustly discriminated malignant from non-malignant tissue (AUC = 0.91) and identified clinically significant cancer (Gleason Grade Group [GG] ≥ 2 vs. GG 0-1, AUC = 0.89) within biopsy cores. However, it had limited performance for advanced patient-level features (e.g., pathological GG3-5, AUC = 0.63). In contrast, ONECUT2 gene-body methylation significantly stratified pathological aggressiveness (pathological GG3-5, AUC = 0.79; aggressive disease, AUC = 0.75) and demonstrated superior assay stability (methylation ICC 0.58-0.70 vs. RNA ICC 0.38-0.61). Furthermore, ONECUT2 DNA methylation significantly stratified BCR-free survival (hazard ratio = 3.90, p = 0.023), whereas the RNA signature showed a modest trend (hazard ratio = 1.88, p = 0.053). Notably, integrating DNA methylation with the three-gene expression signature (combined model) significantly improved predictive accuracy for adverse features (e.g., extraprostatic extension, ΔAUC = 0.20, p = 0.02) compared to the three-gene expression signature alone. Conclusions: While a multiplex RT-qPCR panel (ONECUT2, EZH2, and BRCA2) excels at core-level cancer detection, it lacks the resolution for aggressiveness classification. In contrast, ONECUT2 DNA methylation provides superior analytical reproducibility, robust stratification of cancer aggressiveness, and long-term recurrence risk estimation. These findings suggest that integrating transcriptional and epigenetic markers into a multimodal framework could provide complementary information to refine risk stratification in PCa.