Integrative machine learning models predict prostate cancer diagnosis and biochemical recurrence risk: Advancing precision oncology.

Abstract

Prostate cancer (PCa) ranks among the most prevalent cancers in men worldwide. Biochemical recurrence (BCR) presents a major clinical challenge in PCa management, with significant prognostic heterogeneity observed among patients post-recurrence. This study aimed to develop machine learning models for predicting both the diagnosis and prognosis of PCa patients. Using WGCNA, we initially identified 16 BCR-related target genes. Cluster analysis revealed these genes were significantly associated with PCa prognosis, drug sensitivity, and immune infiltration. We constructed a robust diagnostic model integrating multiple machine learning algorithms, demonstrating strong predictive capability for PCa. Furthermore, a BCR-related prognostic model built using the LASSO algorithm also yielded satisfactory performance. Among the differentially expressed BCR-associated prognostic genes, COMP emerged as a critical regulatory factor. Both in vitro and in vivo experiments confirmed COMP's role in influencing PCa progression. Additionally, COMP demonstrates significant potential as a dual biomarker for both the diagnosis and recurrence prediction of PCa.