Plasma circulating tumor deoxyribonucleic acid methylation enables noninvasive disease stratification beyond prostate-specific antigen in prostate cancer.

BACKGROUND: Accurate discrimination between localized and metastatic prostate cancer (PCa) remains a critical unmet need in clinical practice, particularly in patients with equivocal imaging findings or biologically high-risk disease. Circulating tumor DNA (ctDNA) methylation represents a stable epigenetic signal that may reflect tumor burden and disease state. We aimed to evaluate whether plasma ctDNA methylation markers can accurately discriminate disease state beyond conventional clinical parameters. METHODS: A total of 174 patients were included, comprising localized PCa (n = 73), metastatic hormone-sensitive PCa (mHSPC, n = 16), and metastatic castration-resistant PCa (mCRPC, n = 85). Candidate methylation markers (C2orf88 and HAPLN3) were identified using a machine learning-based approach and quantified in plasma ctDNA. Associations with metastatic disease were evaluated using logistic regression analyses, and diagnostic performance was assessed using receiver operating characteristic analyses. RESULTS: Plasma methylation levels of C2orf88 and HAPLN3 increased significantly with advancing disease stage (P < 0.05). In multivariable analysis, C2orf88 (OR 3.47, 95% CI 1.95-6.15; P < 0.001) and HAPLN3 (OR 2.82, 95% CI 1.65-4.82; P < 0.001) were independently associated with metastatic disease. The clinical model demonstrated modest discrimination (AUC range, 0.537-0.828), whereas methylation-based models achieved superior performance (AUC range, 0.807-0.843). The integrated two-marker signature provided the highest discriminatory accuracy (AUC = 0.913), indicating substantial incremental value over conventional clinical assessment. CONCLUSIONS: A two-marker ctDNA methylation signature enables accurate discrimination between localized and metastatic PCa. This plasma-based epigenetic biomarker captures clinically meaningful disease biology and offers a scalable, noninvasive approach for refined disease stratification.