Integrative Analysis of Glycine, Serine, and Threonine Metabolism and the Immune Microenvironment in Endometrial Cancer: A Prognostic Model and Metabolic-Immune Framework for Precision Oncology

Background: Metabolic reprogramming is a hallmark of the pathogenesis and progression of endometrial carcinoma (EC). This study comprehensively analyzed the expression profiles of glycine, serine, and threonine (Gly/Ser/Thr) metabolism–related genes in EC. We also established a robust prognostic model and developed a molecular subtyping framework that integrates metabolic and immune characteristics based on the identified prognostic genes. The aims of this work are to enhance diagnostic precision and improve clinical management strategies for patients with EC. Methods: Untargeted metabolomic analysis was performed on 35 EC and 15 normal tissues. The Cancer Genome Atlas (TCGA) transcriptomic data were integrated with weighted gene co-expression network analysis (WGCNA) to identify EC-related metabolic genes and construct a prognostic model using Cox proportional hazards and least absolute shrinkage and selection operator (LASSO) regression analyses. The model was validated using an independent proteomic and single-cell dataset from our institution. Consensus clustering classified patients into three molecular subtypes, which were further characterized by gene set variation analysis (GSVA) and profiling of immune infiltration. Finally, key prognostic genes were validated by reverse transcription quantitative polymerase chain reaction (RT-qPCR) in EC and normal endometrial epithelial cells. Results: Metabolomic analysis revealed significant enrichment of the Gly/Ser/Thr metabolic pathways. WGCNA identified a tumor-associated metabolic module among 1741 pathway-related genes. A prognostic model comprising methylenetetrahydrofolate dehydrogenase 2 (MTHFD2), ribosomal protein S6 kinase A1 (RPS6KA1), and cyclin-dependent kinase inhibitor 2A (CDKN2A) was subsequently established. Consensus clustering based on risk scores stratified EC patients into three molecular subtypes: immunometabolic-suppressed (C1), proliferative-immunobalanced (C2), and immune-activated (C3). The C1 subtype had the poorest prognosis and was characterized by metabolic suppression and immune evasion. The C2 subtype showed a favorable prognosis and was defined by a “proliferation–immune balance” in which high proliferative activity coexisted with strong anti-tumor immunity. The C3 subtype was also associated with a favorable outcome, driven by upregulated DNA repair and oxidative phosphorylation pathways alongside infiltration of immune-active cells. RT-qPCR confirmed significant differences in the mRNA expression of MTHFD2, RPS6KA1, and CDKN2A between normal and EC cells (p < 0.05). Conclusion: This study developed a Gly/Ser/Thr pathway–based prognostic model for EC, based on the expression of MTHFD2, RPS6KA1, and CDKN2A as novel biomarkers. The resulting patient stratification framework holds significant clinical potential for guiding precise and personalized management of EC.