Identification of an autophagy-related gene expression prognostic model in endometrial carcinoma patients

Abstract

Background Autophagy, as a lysosomal degradation pathway, has been reported to be involved in various pathologies, including cancer. However, the expression profiles of autophagy-related genes (ARGs) in endometrial cancer (EC) remain poorly understood. Methods In this study, we analyzed the expression of MRGs using The Cancer Genome Atlas (TCGA) data to screen differentially expressed MRGs (DE-MRGs) significantly correlated to EC patients’ prognosis. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DE-MRGs were investigated. LASSO algorithm and Cox regression analysis were performed to select MRGs closely related to EC patients’ outcomes. A prognostic signature was developed and the efficacy were validated in part of and the entire TCGA EC cohort. Moreover, we developed a comprehensive nomogram including the risk model and clinical features to predict EC patients' survival probability. Results Ninety-four ARGs significantly dysregulated in EC samples compared with the normal control samples. Functional enrichment analysis showed these differentially expressed ARGs (DE-ARGs) were highly enriched in apoptosis, P53 signaling pathway, and various cancer development. Among the 94 DE-ARGs, we subsequently screen out four-ARGs closely related to EC patients outcomes, which are ERBB2, PTEN, TP73 and ARSA. Based on the expression and coefficiency of 4 DE-ARGs, we developed a prognostic signature and further validated its efficacy in part of and the entire TCGA EC cohort. The four ARGs signature was independent of other clinical features, and was proved to effectively distinguish high- or low-risk EC patients and predicted patients' OS accurately. Moreover, the nomogram showed the excellent consistency between the prediction and actual observation in terms of patients' 3- and 5-year survival rates. Conclusions It was suggested that the ARG prognostic model and the comprehensive nomogram may guide the precise outcome prediction and rational therapy in clinical practice.