Construction of a stromal cell-related prognostic signature based on a 101-combination machine learning framework for predicting prognosis and immunotherapy response in triple-negative breast cancer
This study reveals TNBC microenvironment heterogeneity and introduces a prognostic signature based on myCAF, VSMC, and Pericyte marker genes. MVPRS effectively predicts TNBC prognosis and immunotherapy response, providing guidance for personalized treatment. FN1 was validated as a key oncogene impacting TNBC progression and malignant phenotype, with potential as a therapeutic target.
Authors
- Yacheng Pan (ORCID: https://orcid.org/0009-0003-5083-6500)
- Fanrong Li (ORCID: https://orcid.org/0009-0006-9638-5604)
- Congnan Jin
- Liying Wang (ORCID: https://orcid.org/0000-0003-3075-6872)
- Zheng Zhang (ORCID: https://orcid.org/0000-0002-3544-1389)
- Yifeng Zhou (ORCID: https://orcid.org/0000-0002-8085-0261)
- Shenghua Zhang (ORCID: https://orcid.org/0000-0003-4550-4723)
- Binbin Guo
- Jieqiong Deng (ORCID: https://orcid.org/0000-0002-2334-8793)
Institutions
- Soochow University (CN)
- Nanjing Medical University (CN)
Publication Details
- Journal
- Frontiers in Immunology
- Published
- 2025-05-14
- DOI
- https://doi.org/10.3389/fimmu.2025.1544348
- Primary Topic
- Cancer Immunotherapy and Biomarkers
- Type
- article
- Field-Weighted Citation Impact
- 0.00