Metabolic Landscape of Endometrial Cancer: Insights into Pathway Dysregulation and Metabolic Features

Background: Metabolic reprogramming is increasingly recognized as a hallmark of endometrial cancer, yet tissue-based metabolic signatures remain insufficiently defined. Methods: Untargeted metabolomics was performed on paired endometrial cancer (n = 10) and adjacent normal tissues (n = 10). Differential metabolites were identified through multivariate and univariate analyses. KEGG enrichment characterized altered pathways, while Random Forest and SVM were used for machine-learning-based feature prioritization. ROC analyses were conducted to evaluate the discriminative potential of selected metabolites. Results: 300 metabolites were significantly altered. Tumor tissues showed increased sphingolipid metabolism, glutathione metabolism, and arachidonic acid metabolism, alongside decreased bile acid, phenylalanine, and steroid biosynthesis. Machine learning converged on six key metabolites that demonstrate strong tissue-discriminative capacity. Conclusions: Endometrial cancer exhibits a distinct metabolic profile characterized by lipid remodeling and redox adaptation. The six metabolites identified through machine-learning-based analyses represent candidate metabolic features associated with endometrial cancer and provide a foundation for future mechanistic studies and validation in larger, independent cohorts.