Plasma proteomic profiling was used to discover a biochemical recurrence prediction model for prostate cancer.

Prostate cancer (PCa) is one of the most common malignancies in men. There is limited data available regarding potential minimally-invasive biomarkers for predicting PCa outcomes and disease monitoring. Here, we investigate the proteomic profile of plasma in 222 PCa patients and 159 healthy controls. Integrative analyses of the proteome profile and clinical features identified protein networks related to International Society of Urological Pathology (ISUP) grades and prostate-specific antigen (PSA). Proteome-based classification revealed three subtypes, PCa-I, PCa-II, and PCa-III, reflecting distinct clinical prognosis and molecular signatures. We develop a 17-protein panel and established a biochemical recurrence prediction model that effectively predicts biochemical recurrence for patients with PCa, which is better than ISUP grades and pathological stages. Finally, we validate the protein panel by parallel reaction monitoring (PRM) assay in an independent cohort. Collectively, this study portrays the plasma proteomic landscape of PCa cohort and provides a comprehensive resource for further biological and predictive research in PCa.