Prediction of Prostate Cancer Biochemical Recurrence After Radical Prostatectomy by Collagen Models Using Multiomic Profiles

This is the authors' abstract. We don't add key points for this paper.

BACKGROUND AND OBJECTIVE: The interplay between prostate cancer and the tumor microenvironment is well documented and of primary importance in disease evolution. Herein, we investigated the prognostic value of tissue and urinary collagen-related molecular signatures in predicting biochemical recurrence (BCR) after radical prostatectomy (RP). METHODS: A comprehensive analysis of 55 collagen-related features was conducted using transcriptomic datasets (n = 1393), with further validation at the proteomic level (n = 69). Additionally, a distinct cohort (n = 73) underwent a urine-based peptidomic analysis, culminating in the validation of a urine-derived prognostic model. Independent prognostic significance was assessed using Cox proportional hazards modeling, while the model's predictive performance was benchmarked against established clinical metrics. KEY FINDINGS AND LIMITATIONS: An expression analysis of 55 collagen-related transcripts identified 11 transcripts significantly associated with BCR (C-index: 0.55-0.72, p < 0.002). Multivariable models incorporating these transcripts enhanced prognostic accuracy, surpassing clinical variables (C-index: 0.66-0.89, p < 0.002). Proteomic validation confirmed five key collagen proteins, while a urine-based collagen model (C-index: 0.73, 95% confidence interval: 0.62-0.85) demonstrated a strong prognostic potential, although limited by small patient numbers. Additionally, tissue collagen-based models predicted overall survival with a significant prognostic value (C-index: 0.59-0.70, p < 0.01). CONCLUSIONS AND CLINICAL IMPLICATIONS: Collagen-based molecular signatures in both tissue and urine emerge as robust prognostic biomarkers for predicting BCR following RP.