Machine learning-based identification of kbhb-affected tumor cell subsets as prognostic and therapeutic targets in breast cancer
Kbhb influences breast cancer heterogeneity, with SCGB2A2 + neoplastic cells serving as valuable prognostic indicators. Targeting these cells may improve therapeutic outcomes. Our model also supports machine learning-guided drug discovery for metabolically vulnerable subpopulations.
Authors
- Jiguang Han (ORCID: https://orcid.org/0000-0002-5973-2842)
- Quan Yuan (ORCID: https://orcid.org/0009-0003-8060-2158)
- Yan Sha (ORCID: https://orcid.org/0000-0002-4596-8432)
- Lin Ni (ORCID: https://orcid.org/0000-0003-3313-5289)
- Deng Lin (ORCID: https://orcid.org/0000-0001-6344-3552)
- Yu Hao
- Rongjie Ye (ORCID: https://orcid.org/0009-0003-8631-3648)
Institutions
- Fujian Medical University (CN)
- Harbin Medical University (CN)
- First Affiliated Hospital of Xiamen University (CN)
- Third Affiliated Hospital of Harbin Medical University (CN)
- Fuzhou General Hospital of Nanjing Military Command (CN)
Publication Details
- Journal
- Journal of Translational Medicine
- Published
- 2025-12-11
- DOI
- https://doi.org/10.1186/s12967-025-07555-3
- Citations
- 1
- Primary Topic
- HER2/EGFR in Cancer Research
- Type
- article
- Field-Weighted Citation Impact
- 1.05