Integrative single-cell and machine learning analysis predicts lactylation-driven therapy resistance in prostate cancer: a molecular docking and experiments-validated framework for treatment optimization
The prognostic model effectively predicts the progression-free interval (PFI) and drug response, with accurate risk stratification for PCa patients. Our findings highlight the potential of risk genes in the development of personalized treatment strategies and enhancing PCa prognostic assessment.
Authors
- Qingfu Deng (ORCID: https://orcid.org/0009-0008-4836-9495)
- Xinyao Zhu (ORCID: https://orcid.org/0000-0002-4507-0833)
- Yang Zeng (ORCID: https://orcid.org/0000-0002-4568-9847)
- Qilong Wu
- Tao Zhou (ORCID: https://orcid.org/0000-0002-9782-7324)
- Juan Wang (ORCID: https://orcid.org/0000-0003-0767-5687)
- Zhiyu Liu (ORCID: https://orcid.org/0000-0002-6024-7836)
- Yuqi Li (ORCID: https://orcid.org/0000-0003-4629-4027)
Institutions
- Southwest Medical University (CN)
- Affiliated Hospital of Southwest Medical University (CN)
- Sichuan Mianyang 404 Hospital (CN)
- Affiliated Hospital of North Sichuan Medical College (CN)
Publication Details
- Journal
- Frontiers in Immunology
- Published
- 2025-09-02
- DOI
- https://doi.org/10.3389/fimmu.2025.1647384
- Primary Topic
- Prostate Cancer Treatment and Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00