Stemness-based gastric cancer classification by machine learning for precision diagnosis and treatment of gastric cancer
Our machine learning approach performed an overall analysis of the relationship between the stemness of GC and the therapeutic effect, identified a promising stemness-based classification of GC to predict prognosis and treatment efficacy, and developed a predictive model to make the stemness-based classification accessible for clinical practice.
Authors
- Quan Jiang (ORCID: https://orcid.org/0000-0001-6473-1150)
- Tao Zhu (ORCID: https://orcid.org/0000-0002-2921-4067)
- Hao Chen (ORCID: https://orcid.org/0000-0002-6282-0237)
- Fenglin Liu (ORCID: https://orcid.org/0000-0003-3617-7126)
- Shixin Zhou (ORCID: https://orcid.org/0000-0002-1192-7616)
- Changxin Chen (ORCID: https://orcid.org/0000-0002-5444-4921)
- Chenyu Tian (ORCID: https://orcid.org/0009-0007-0781-4444)
Institutions
- Fujian Medical University (CN)
- Sun Yat-sen University (CN)
- Fudan University (CN)
- Fudan University Shanghai Cancer Center (CN)
- Zhongshan Hospital (CN)
- The First Affiliated Hospital, Sun Yat-sen University (CN)
Publication Details
- Journal
- Journal of Gastrointestinal Oncology
- Published
- 2025-10-01
- DOI
- https://doi.org/10.21037/jgo-24-665
- Primary Topic
- Gastric Cancer Management and Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 0.00