Metabolic-Pathway-Based Subtyping in Endometrial Carcinoma: An Integrated Study Based on Multi-omics Analysis and Machine Learning Algorithms

Endometrial cancer (EC), the second most common malignancy in the female reproductive system, has garnered increasing attention for its genomic heterogeneity, but understanding of its metabolic characteristics is still poor. We explored metabolic dysfunctions in EC through comprehensive multi-omics analysis (RNA-seq datasets from The Cancer Genome Atlas (TCGA), Cancer Cell Line Encyclopedia (CCLE), and GEO datasets; the Clinical Proteomic Tumor Analysis Consortium (CPTAC) proteomics; CCLE metabolomics) to develop useful molecular targets for precision therapy. Unsupervised consensus clustering was performed to categorize EC patients into three metabolism-pathways-based subgroups (MPS). These MPS subgroups had distinct clinical prognoses, transcriptomic and genomic alterations, immune microenvironment landscape, and unique patterns of chemotherapy sensitivity. Moreover, the MPS2 subgroup has a better response to immunotherapy. Finally, three machine learning algorithms (LASSO, random forest, and stepwise multivariate Cox regression) were used for developing a prognostic “Metagene” signature based on metabolic molecules. Thus, a thirteen-hub-gene-based classifier was constructed to predict patients’ MPS subtype offering a more accessible and practical approach. This metabolism-based classification system can potentially enhance prognostic predictions and guide clinical strategies for immunotherapy and metabolism-targeted therapy in EC.