Machine learning identifies a DNA repair-related risk model for bladder cancer and functionally characterizes ZWINT as a potential oncogenic factor.

Background: Bladder cancer (BCa) is characterized by substantial molecular and clinical heterogeneity, hindering accurate prognostic assessment and treatment stratification. This study aimed to develop a machine learning-based prognostic model based on DNA repair-related genes and to investigate the functional relevance of representative genes in BCa. Methods: To address this, we constructed a DNA repair-related risk score (DRRS) using 101 machine learning algorithms based on transcriptomic profiles from BCa cohorts. The prognostic value, immune relevance, and therapeutic implications of DRRS were systematically evaluated. ZW10 interacting kinetochore protein (ZWINT), selected as a representative gene within the DRRS, was subjected to functional characterization through in vitro experiments. Results: DRRS effectively stratified BCa patients into high- and low-risk groups with distinct survival outcomes, immune microenvironment features, and associations with responses to chemotherapy and immunotherapy. Functional assays revealed that ZWINT knockdown suppressed cell proliferation, migration, and invasion, induced apoptosis, and caused G1 phase cell cycle arrest. Transcriptomic analysis and Western blot validation demonstrated increased phosphorylation of p65 following ZWINT knockdown, indicating activation of the NF-κB signaling pathway, potentially as part of a feedback mechanism associated with apoptosis. Conclusions: This study establishes a machine learning-based DNA repair signature for BCa and functionally validates ZWINT as a potential oncogenic driver. The findings provide new insight into BCa progression and highlight DRRS and ZWINT as promising biomarkers and therapeutic targets for treatment stratification.