Development of a senescence-related lncRNA signature in endometrial cancer based on multiple machine learning models
The srlncRNA signature (AL121906.2, AP002761.4, BX322234.1, LINC00662, LINC00908, VIM-AS1, and ZNF236-DT) could guide prognosis prediction and treatment choices for EC patients.
Authors
- Xintong Cai (ORCID: https://orcid.org/0000-0001-7903-2416)
- Xingming Ye (ORCID: https://orcid.org/0000-0002-3040-7325)
- Xuemei Lei (ORCID: https://orcid.org/0000-0003-0973-0491)
- Aihua Guo
- Xin Jiang (ORCID: https://orcid.org/0000-0002-7288-8278)
- Yanhong Li (ORCID: https://orcid.org/0000-0001-5844-370X)
- Yang Sun (ORCID: https://orcid.org/0000-0002-1587-9797)
- Jie Lin (ORCID: https://orcid.org/0009-0000-1139-463X)
- Fengle Jiang (ORCID: https://orcid.org/0009-0002-7822-6416)
Institutions
- Fujian Medical University (CN)
- Center for High Pressure Science & Technology Advanced Research (CN)
- Fujian Provincial Cancer Hospital (CN)
- First Affiliated Hospital of Zhengzhou University (CN)
Publication Details
- Journal
- Frontiers in Genetics
- Published
- 2025-11-26
- DOI
- https://doi.org/10.3389/fgene.2025.1687922
- Primary Topic
- Cancer-related molecular mechanisms research
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Natural Science Foundation of Fujian Province