Bioinformatics Analysis Identifies Lipid Droplet‐Associated Gene Signatures as Promising Prognostic and Diagnostic Models for Endometrial Cancer
This study provides the first comprehensive analysis of LDAGs in EC, establishing robust prognostic and diagnostic gene signatures with strong biological relevance. These signatures support a metabolism-driven framework for EC classification and may offer potential clinical utility in early detection, risk stratification, and personalized treatment.
Authors
- Laurent Brard (ORCID: https://orcid.org/0000-0003-0065-1826)
- Vijayalakshmi N. Ayyagari (ORCID: https://orcid.org/0000-0003-1001-7044)
- Paula Díaz‐Sylvester (ORCID: https://orcid.org/0000-0002-1531-1958)
- Kathleen Groesch (ORCID: https://orcid.org/0000-0003-0124-7761)
- Ejaz Shah
- Miao Li (ORCID: https://orcid.org/0000-0002-7148-0940)
- Teresa Wilson (ORCID: https://orcid.org/0000-0002-2797-6017)
Institutions
- Southern Illinois University School of Medicine (US)
Publication Details
- Journal
- Cancer Reports
- Published
- 2025-08-01
- DOI
- https://doi.org/10.1002/cnr2.70313
- Citations
- 1
- Primary Topic
- Cancer, Lipids, and Metabolism
- Type
- article
- Field-Weighted Citation Impact
- 0.94