Machine learning identifies a DNA repair-related risk model for bladder cancer and functionally characterizes ZWINT as a potential oncogenic factor
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.
Authors
- Zhiqiang Yang (ORCID: https://orcid.org/0000-0002-8951-9401)
- Wuning Mo
- Qisheng Su
- Haodong Liu
- Xiaohong Li
- Shijie Liang
Institutions
- Guangxi Medical University (CN)
- First Affiliated Hospital of GuangXi Medical University (CN)
Publication Details
- Journal
- Translational Andrology and Urology
- Published
- 2026-02-01
- DOI
- https://doi.org/10.21037/tau-2025-710
- Primary Topic
- Ferroptosis and cancer prognosis
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Massachusetts Department of Agricultural Resources
- National Natural Science Foundation of China