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Bioinformatics Analysis Identifies Lipid Droplet-Associated Gene Signatures as Promising Prognostic and Diagnostic
Vijayalakshmi N Ayyagari1,2, Miao Li1, Paula Diaz-Sylvester1,2,3
1Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Southern Illinois University School of Medicine, Springfield, Illinois, USA.
Background:
Effective diagnostic and prognostic tools are critical for early detection and improved outcomes in endometrial cancer (EC). Although metabolic dysregulation plays a key role in EC pathogenesis, the clinical relevance of lipid droplet-associated genes (LDAGs) remains largely unexplored. This study aims to establish LDAG-based gene signatures with strong diagnostic and prognostic potential in EC.
Aims:
To identify LDAG signatures with prognostic and diagnostic utility in EC.
Methods And Results:
A curated set of LDAGs was systematically analyzed across publicly available EC datasets to identify differentially expressed LDAGs (DE-LDAGs). Survival-associated DE-LDAGs were then identified using univariate Cox regression. A four-gene prognostic model was developed through LASSO-based feature selection followed by multivariate Cox regression and validated using Kaplan-Meier survival and time-dependent receiver operating characteristic (ROC) analyses. From the same pool of survival-associated DE-LDAGs, a six-gene diagnostic model was constructed using LASSO, ROC analysis, and logistic regression. Model performance was evaluated using ROC curves and support vector machine (SVM) classification. Functional enrichment and protein-protein interaction (PPI) network analyses were conducted to assess the biological relevance of the identified genes. Our results demonstrate that the four-gene prognostic model (LMLN, LMO3, PRKAA2, and RAB10) stratified EC patients into high- and low-risk groups with significantly different survival outcomes (p < 0.05; time-dependent AUC > 0.70). The six-gene diagnostic model (AIFM2, ABCG1, LIPG, DGAT2, LPCAT1, and VCP) demonstrated near-perfect classification of tumor versus normal tissues (AUC ≈0.99 in ROC analysis; 99.8% accuracy in SVM analysis). Functional enrichment linked DE-LDAGs to lipid metabolism, ER stress response, cholesterol homeostasis, and autophagy, underscoring their biological relevance in EC pathobiology.
Conclusion:
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.
