An O-glycosylation-based prognostic signature for biochemical recurrence in prostate cancer identifies

Background: Prostate cancer (PCa) is frequently diagnosed in men, and biochemical recurrence (BCR) remains a persistent problem after clinical treatment. Commonly used indicators do not identify patients who are likely to relapse early. Protein O-linked glycosylation is an important post-translational modification involved in cancer progression, but its prognostic relevance and regulatory mechanisms in PCa remain insufficiently understood. Methods: Different machine-learning approaches were tested during model development. The final score O-glycosylation scores (OGs) was then examined for risk classification, nomogram construction, and prediction of drug sensitivity. Results: The O-glycosylation signature consistently stratified patients into distinct risk groups across multiple independent cohorts (P<0.01), and the derived risk score remained an independent predictor of BCR [hazard ratio (HR) >1.48, P<0.05]. A nomogram integrating the O-glycosylation signature with clinicopathological variables achieved good performance in estimating 1-, 3-, and 5-year BCR probabilities [area under the curve (AUC): 0.747-0.863]. Drug-sensitivity analysis suggested that patients with different OGs scores may show distinct predicted responses to candidate agents, including AZD7762, a checkpoint kinase inhibitor, and tigecycline, a glycylcycline antibiotic with reported antitumor activity. Conclusions: This study establishes an O-glycosylation-related prognostic model for BCR risk stratification in PCa and identifies