Abstract
BACKGROUND: Efferocytosis is involved in the occurrence and development of various malignancies, but its role in endometrial cancer (EC) remains unclear. This study aims to employ bioinformatics methods to identify potential therapeutic targets associated with efferocytosis-related genes in EC. METHODS: Transcriptomic and clinical data of EC were obtained from The Cancer Genome Atlas (TCGA) database. Efferocytosis-related genes were curated from published literature. Prognostic genes were identified using random forest and least absolute shrinkage and selection operator (LASSO) regression. A Cox risk score model and nomogram were constructed, followed by random cohort partitioning, internal validation, immune infiltration analysis, and drug sensitivity profiling. RESULTS: A risk score model incorporating oxidized low-density lipoprotein receptor 1 (OLR1), sodium-dependent sulfate (SDS), lysosomal-associated protein transmembrane 5 (LAPTM5), and Src-like adaptor (SLA) effectively stratified patients into high- and low-risk groups. The nomogram, based on risk score, age, and histological grade, accurately predicted the 1-, 3-, and 5-year survival rates of EC patients. Immune infiltration analysis revealed enhanced immune cell activity in the low-risk group. Additionally, 12 drugs sensitive to EC were identified. CONCLUSIONS: This study establishes a prognostic model based on efferocytosis-associated genes for EC, providing clinical guidance for the prognosis and treatment of EC patients.