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

Abstract

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.