Identification and characterization of metabolic subtypes of endometrial cancer using systems-level approach

Abstract

Endometrial cancer (EC) is the most common gynaecological cancer worldwide. Understanding the metabolic adaptation and its heterogeneity in tumor tissues may provide new insights and help in cancer diagnosis, prognosis, and treatment. In this study, we investigated metabolic alterations of EC to understand the variations in the metabolism within tumor samples. We integrated the TCGA transcriptomics data of EC (RNA-Seq) with the human genome-scale metabolic model (HMR2.0) and performed unsupervised learning to identify the metabolic subtypes of EC and uncover the underlying dysregulated metabolic pathways and reporter metabolites in each subtype. The relationship between metabolic subtypes and clinical variables was explored. Further, we characterized each subtype at the molecular level and correlated the subtype-specific metabolic changes occurring at the transcriptome level with the genomic alterations. EC patients are stratified into two robust metabolic subtypes (cluster-1 and cluster-2) that significantly correlate to patient survival, tumor stages, mutation, and copy number variations. We observed co-activation of pentose phosphate pathway and one-carbon metabolism along with genes involved in controlling estrogen levels in cluster-2, which is linked to poor survival. PNMT and ERBB2 are also upregulated in cluster-2 samples and present in the same chromosome locus 17q12, which is amplified. PTEN and TP53 mutations show mutually exclusive behavior between subtypes and display a difference in survival. This work identifies metabolic subtypes with distinct characteristics at the transcriptome and genome levels, highlighting the metabolic heterogeneity within EC.