A cfDNA-based DNA methylation classifier for distinguishing prostate cancer from benign prostatic hyperplasia.

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.