Prostate Int. 2026 Sep;14(3):
236-246
Background: Differentiating benign prostatic hyperplasia (BPH) from prostate cancer (PCa) remains a diagnostic challenge due to overlapping clinical features and the limited specificity of prostate-specific antigen (PSA) testing. Recent studies highlight the promise of DNA methylation patterns in cell-free DNA (cfDNA) as non-invasive biomarkers for prostate disease stratification. This study aimed to develop a targeted methylation-based diagnostic model capable of distinguishing PCa from BPH using cfDNA. We hypothesized that specific genomic regions exhibit consistent hyper- or hypomethylation signatures that could serve as robust diagnostic markers.
Materials and methods: We applied a custom panel targeting 748 predefined differentially methylated regions (DMRs) to cfDNA from 365 participants (PCa: 230; BPH: 135). Methylation was quantified by targeted enzymatic methyl-sequencing. Following feature selection, a 43 DMR-based model was built using a stacking ensemble framework (Random Forest + XGBoost with stacking). Data were split by stratified sampling into training/test sets and a validation cohort. Uncertainty was quantified via 1,000-iteration non-parametric bootstraps. Functional relevance was assessed by gene ontology (GO) enrichment stratified by genomic context.
Results: The diagnostic model exhibited robust discriminative performance across both test and validation cohorts. In the test set, the model achieved an area under the receiver operating characteristic curve (ROC-AUC) of 0.99 and overall accuracy of 0.95, with balanced sensitivity (0.96) and specificity (0.93). Consistent results were observed in the validation set, with a ROC-AUC of 0.98, overall accuracy of 0.93, sensitivity of 0.94, and specificity of 0.93. Importantly, in the validation cohort, the model retained a high sensitivity of 0.94 (95 % CI: 0.84-0.98), underscoring its potential clinical applicability. GO analysis revealed promoter-proximal hypermethylation enriched for developmental programs, transcription factor activity, and chromatin remodeling, whereas gene body hypermethylation mapped to RNA metabolic processes and intracellular transport, supporting biological plausibility.
Conclusions: Integrating cfDNA methylation with machine learning delivers a minimally invasive, clinically actionable assay that robustly differentiates PCa from BPH. These results motivate prospective, multi-center validation and position cfDNA methylation profiling as a scalable strategy for prostate disease stratification.
Keywords: benign prostatic hyperplasia; biomarker; cfDNA methylation; machine learning; non-invasive diagnostics; prostate cancer