Machine learning developed an immune evasion signature for predicting prognosis and immunotherapy benefits in lung adenocarcinoma
Our study developed a novel IRS for LUAD patients, which served as an indicator for predicting the prognosis and immunotherapy response.
Authors
- Liangbin Wang (ORCID: https://orcid.org/0000-0001-6090-1968)
- Ke Shi (ORCID: https://orcid.org/0000-0001-6776-2710)
- Dongxiao Ding
- Junjie Ying (ORCID: https://orcid.org/0000-0002-0482-6796)
- Gang Huang (ORCID: https://orcid.org/0009-0004-7284-7953)
- Maofen Jiang
- Yaxing Shen (ORCID: https://orcid.org/0000-0003-0764-547X)
- Li Wang (ORCID: https://orcid.org/0000-0002-5595-2741)
- Chong Zhang (ORCID: https://orcid.org/0000-0001-5072-5219)
- Wenjun Shang (ORCID: https://orcid.org/0000-0002-9234-979X)
Institutions
- University of Nottingham Ningbo China (CN)
- Sun Yat-sen University (CN)
- Fudan University (CN)
- Zhongshan Hospital (CN)
- The First Affiliated Hospital, Sun Yat-sen University (CN)
- Ningbo No.6 Hospital (CN)
- First Affiliated Hospital Zhejiang University (CN)
Publication Details
- Journal
- Frontiers in Cell and Developmental Biology
- Published
- 2025-06-19
- DOI
- https://doi.org/10.3389/fcell.2025.1622345
- Primary Topic
- Cancer Immunotherapy and Biomarkers
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Natural Science Foundation of Ningbo
- Medical and Health Research Project of Zhejiang Province