A mitophagy-related prognostic model for risk stratification and immune landscape characterization in endometrial cancer

Endometrial cancer (EC) is highly heterogeneous, highlighting the need for improved approaches to prognosis and risk stratification. In this study, we developed a mitophagy-related prognostic model using transcriptomic and clinical data from 579 EC patients in The Cancer Genome Atlas and 4922 GeneCards-derived candidate mitophagy-associated protein-coding genes. Multiple bioinformatic methods were applied, including weighted gene co-expression network analysis (WGCNA), support vector machine-recursive feature elimination (SVM-RFE), Random Forest, eXtreme Gradient Boosting (XGBoost), and LASSO Cox regression, to identify key differentially expressed mitophagy-related genes (DEMGs). CDC20, IQGAP3, SFRP4, SLC22A3, and TGFBR3 were ultimately selected as key hub DEMGs. The prognostic model stratified patients into groups with significantly different overall survival (hazard ratio = 1.79, 95% confidence interval = 1.17–2.75, p = 0.008), although the time-dependent AUC values indicated moderate predictive performance. Internal validation using train/test analysis and bootstrap resampling supported the stability of the risk association, but external validation remains necessary. High-risk scores were associated with advanced clinicopathological features, TCGA molecular subtype distribution, and distinct immune characteristics. Drug sensitivity analysis suggested differential sensitivity to several candidate agents; however, these results should be interpreted as hypothesis-generating rather than direct evidence of treatment response. Integrating mitophagy-related signatures with established clinicopathological and molecular factors may contribute to future EC risk stratification.