Integrating machine learning and multi-omics analysis to explore Treg-associated programmed cell death features in clear cell renal cell carcinoma
We explored the significance of Treg and programmed cell death characteristics in the ccRCC tumor microenvironment and established clinically translatable tools for ccRCC diagnosis, prognosis, and personalized therapy selection, thus promoted the application of explainable machine learning models in precision oncology. Furthermore, We have identified SLC11A1 as a highly promising therapeutic target for ccRCC.
Authors
- Huan Wu (ORCID: https://orcid.org/0000-0003-2752-8035)
- Haojie Dai (ORCID: https://orcid.org/0009-0005-8399-8551)
- Lian Sheng
- Kui Liu (ORCID: https://orcid.org/0000-0002-7493-2244)
- Jiawei Wang
- Xi Zhang
- Heng Li
- Shaohua He
- Yang Lv
- Jian Li
- Hongqi Chen
- Lu Yin
- Qiang Li
Institutions
- Wuhu Fourth People Hospital (CN)
- Wujiang District People's Hospital (CN)
- Wuhu No 1 People's Hospital (CN)
- Suzhou Traditional Chinese Medicine Hospital (CN)
- Community Health Center (US)
- Jiangsu Province Hospital (CN)
- Jiangsu Shengze Hospital (CN)
- Nanjing Medical University (CN)
Publication Details
- Journal
- Cancer Cell International
- Published
- 2026-01-09
- DOI
- https://doi.org/10.1186/s12935-025-04133-x
- Citations
- 5
- Primary Topic
- Renal cell carcinoma treatment
- Type
- article
- Field-Weighted Citation Impact
- 182.21