Machine learning integration with multi-omics data constructs a robust prognostic model and identifies PTGES3 as a therapeutic target for precision oncology in lung adenocarcinoma
Our findings highlight the potential of LS score-based molecular subtyping to improve treatment strategies for lung adenocarcinoma and emphasize PTGES3's role in new therapeutic development.
Authors
- Yan Zhuang (ORCID: https://orcid.org/0000-0002-6816-3071)
- K. Weng
- Lin Ruan (ORCID: https://orcid.org/0000-0002-0951-5006)
- Jing Li (ORCID: https://orcid.org/0000-0003-2504-1629)
- Yutong Chen (ORCID: https://orcid.org/0000-0003-1186-3947)
- Weiyu Zhang (ORCID: https://orcid.org/0000-0002-8928-6636)
- Yiming Zeng (ORCID: https://orcid.org/0000-0001-5635-3006)
- Liming Lin (ORCID: https://orcid.org/0000-0002-0259-5269)
Institutions
- Fujian Medical University (CN)
- Second Affiliated Hospital of Fujian Medical University (CN)
- Fujian Provincial Hospital (CN)
- Putian University (CN)
Publication Details
- Journal
- Frontiers in Immunology
- Published
- 2025-09-29
- DOI
- https://doi.org/10.3389/fimmu.2025.1651270
- Citations
- 1
- Primary Topic
- Cancer Immunotherapy and Biomarkers
- Type
- article
- Field-Weighted Citation Impact
- 1.05