Prognostic risk modeling of endometrial cancer using programmed cell death-related genes: a comprehensive machine learning approach
This study presents a novel, comprehensive approach to endometrial cancer prognosis, integrating machine learning and molecular insights to provide a more precise risk stratification tool with potential clinical translation.
Authors
- Feng Pan (ORCID: https://orcid.org/0000-0002-3788-7894)
- Caiyun Fang
- Zhizhong Huang (ORCID: https://orcid.org/0000-0002-3581-7331)
- Xueqin Liu (ORCID: https://orcid.org/0000-0002-4292-4506)
- Xu Liu
- Tianshu Chen (ORCID: https://orcid.org/0000-0002-2566-4394)
- Yuhan Yang (ORCID: https://orcid.org/0000-0003-1715-2345)
- Zaiyu Xiao (ORCID: https://orcid.org/0000-0003-4459-5168)
- K. Gong
- Wenguang Huang
Institutions
- Hubei University of Medicine (CN)
- Taihe Hospital (CN)
Publication Details
- Journal
- Discover Oncology
- Published
- 2025-03-08
- DOI
- https://doi.org/10.1007/s12672-025-02039-8
- Citations
- 4
- Primary Topic
- Cancer-related molecular mechanisms research
- Type
- article
- Field-Weighted Citation Impact
- 3.75