Development and Validation of a 5-Gene Autophagy-Based Prognostic Index in Endometrial Carcinoma

Background Endometrial carcinoma (EC) is the most common gynecological malignancy worldwide, and 15–20% of patients with EC have a rapid relapse within 3 years. This study aims to develop an autophagy-related genes (ARGs) signature to predict the prognosis of EC. Material/Methods In our study, differentially expressed ARGs were identified by “edgeR” package in R and pathway enrichment analysis was performed to explore biological functions. Univariate and multivariate Cox regression analyses were employed to build autophagy signature. Gene set enrichment analysis (GSEA), Kaplan-Meier curve analysis, and ROC curve analysis were conducted to compare the differences between the high- and low-risk groups. Results A total of 60 differentially expressed ARGs (DEARGs) including 34 upregulated and 26 downregulated DEARGs were identified from the TCGAUCEC dataset, with the adjusted P<0.05 and |Fold Change| >1.5. By using univariate and multivariate Cox regression analyses, ERBB2, PRKAB2, GRID2, NRG3, CDKN2A were identified to construct a prognostic signature with AUC 0.673, 0.719, and 0.791, at 1-, 3- and 5-years, respectively. Patients with EC were divided into low- or high-risk group by median risk score, and GSEA showed that low-risk group was enriched in adjacent cells communication pathways while high-risk group was involved in metabolism and immune pathways. The nomograms could also help to guide personal prognostic prediction and therapeutic strategies in EC. Conclusions Our study not only determine 5 ARGs signature that could predict the prognosis of EC but also provide novel insights into the underlying mechanisms of autophagy.