Integrated tumor tissue metabolomic and transcriptomic approaches for identifying diagnostic biomarkers in endometrial cancer with diabetes mellitus

Background Endometrial cancer (EC) patients with type 2 diabetes mellitus (DM) often exhibit a more aggressive tumor phenotype and poorer prognosis. However, the underlying metabolic and molecular mechanisms remain poorly understood. Objective: This study aimed to identify diagnostic biomarkers and dysregulated pathways in EC patients with diabetes (EC-DM) through integrated metabolomic and transcriptomic analyses of tumor tissues. Methods Tumor tissues from 20 EC patients (10 EC-DM, 10 non-DM) were analyzed. Untargeted metabolomics used LC–HRMS, and transcriptomics used RNA-seq in 12 patients. Differentially expressed metabolites and genes were identified via machine learning and DESeq2. Pathway and multi-omics integration were performed. Results Multivariate analysis revealed distinct metabolic profiles between EC-DM and EC-NDM groups. Machine learning identified robust DEMs including uric acid, D-malic acid, and guanosine 5′-diphosphate. Pathway analysis showed significant enrichment in purine metabolism, citrate cycle, arginine biosynthesis, and glycerophospholipid metabolism in EC-DM. Transcriptomics identified 1,123 DEGs, with enrichment in monocarboxylic acid metabolic process, terpenoid metabolism, and renin–angiotensin system. Integrated gene–metabolite interaction networks revealed key interactions involving “IL6”, “PPARg”, “PHGDH”, and purine metabolites. Conclusion Our study demonstrates that diabetes reprograms tumor metabolism in endometrial cancer, leading to distinct metabolic and transcriptional alterations. We identified potential diagnostic biomarkers and highlighted dysregulated pathways that may underlie the aggressive phenotype of EC-DM. These findings provide insights into the metabolic interplay between diabetes and endometrial cancer and offer candidates for further validation.