Machine learning-driven identification of exosome- related biomarkers in head and neck squamous cell carcinoma
This research identifies new exosome-related indicators for HNSCC through machine learning methodologies. The suggested biomarkers, particularly ANGPTL1, exhibit significant promise for diagnostic and prognostic uses. The investigation of the immunological microenvironment yields insights into immune modulation in HNSCC, presenting novel avenues for therapeutic targeting.
Authors
- Jianhua Wei
- Yunwei Li (ORCID: https://orcid.org/0000-0002-5410-4505)
- Yaodong He (ORCID: https://orcid.org/0000-0003-0961-4079)
- Jiaqi Tang (ORCID: https://orcid.org/0000-0002-3544-3456)
- Huan Li (ORCID: https://orcid.org/0000-0001-5125-069X)
- Yan Wang (ORCID: https://orcid.org/0000-0001-9417-0550)
- Rong Liu (ORCID: https://orcid.org/0000-0001-5170-6474)
- Zhenyan Zhao
- Zihui Yang (ORCID: https://orcid.org/0000-0003-1457-5107)
Institutions
- Air Force Medical University (CN)
Publication Details
- Journal
- Frontiers in Immunology
- Published
- 2025-05-22
- DOI
- https://doi.org/10.3389/fimmu.2025.1590331
- Citations
- 5
- Primary Topic
- Extracellular vesicles in disease
- Type
- article
- Field-Weighted Citation Impact
- 4.03