Comprehensive Analysis of a Novel Lipid Metabolism-Related Gene Signature for Predicting the Prognosis and Immune Landscape in Uterine Corpus Endometrial Carcinoma

Abstract

Lipid metabolism is important in various cancers. However, the association between lipid metabolism and uterine corpus endometrial carcinoma (UCEC) is still unclear. In this study, we collected clinicopathologic parameters and the expression of lipid metabolism-related genes (LMRGs) from the Cancer Genome Atlas (TCGA). A lipid metabolism-related risk model was built and verified. The risk score was developed based on 11 selected LMRGs. The expression of 11 LMRGs was confirmed by qRT-PCR in clinical samples. We found that the model was an independent prediction factor of UCEC in terms of multivariate analysis. The overall survival (OS) of low-risk group was higher than that in the high-risk group. GSEA revealed that MAPK signaling pathway, ERBB signaling pathway, ECM receptor interaction, WNT pathway, and TGF-β signaling pathway were enriched in the high-risk group. Low-risk group was characterized by high tumor mutation burden (TMB) and showed sensitive response to immunotherapy and chemotherapy. In brief, we built a lipid metabolism gene expression-based risk signature which can reflect the prognosis of UCEC patients and their response to chemotherapeutics and immune therapy.