Expression of Lipid Metabolism Genes Is Correlated With Immune Microenvironment and Predicts Prognosis in Endometrial Carcinoma

Abstract

Objective Endometrial carcinoma (EC) is one of the most prevalent types of gynecologic cancer. The purpose of this work was to identify the metabolic-related biological characteristics of endometrial cancer and to investigate the immune-related molecular pathways of carcinogenesis in endometrial cancer. Methods Data from The Cancer Genome Atlas (TCGA) were utilized to identify lipid metabolism-related genes (LMRGs) with significant correlations to the prognosis of EC patients. Enrichment of functional pathways within the LMRGs was studied. LASSO and Cox regression analysis were conducted to identify LMRGs that were significantly associated with the prognosis of EC patients. We created a prognostic signature and proved its effectiveness in both training and validation groups. In addition, we constructed a complete nomogram consisting of risk models and clinical variables to estimate the survival probability of EC patients. Results ACOT11, CYP1A2, GDPD5, MOGAT3, OLAH, PIASS4, PIP5K1C, PLPP2, and SRD5A1 were discovered to be strongly associated with the clinical outcomes of EC patients. On the basis of these nine LMRGs, we generated and validated our predictive signature using the training and validation cohorts. In addition to being independent of other clinical factors, the nine-LMRG signature distinguished between patients at high- and low-risk for EC and predict EC patient's probability of survival. Statistically, the nomogram exhibited a high correlation between survival forecasts and observations. In the high-risk group, immune/stromal scores were lower and there was a higher density of several kinds of immune cells. Conclusions The LMRG's prognostic model and comprehensive nomogram could guide therapeutic choices in clinical practice, and explore the underlying mechanisms involved in EC progression.