Construction and Evaluation of a Risk Score Model for Autophagy-Related Genes in Esophageal Adenocarcinoma

BACKGROUND: Several studies have suggested the importance of autophagy during esophageal adenocarcinoma (EAC) development. This study aimed to explore the autophagy-related genes correlated with overall survival in patients with EAC. MATERIAL/METHODS: The RNA-seq expression profiles and clinical data of patients with EAC were screened using The Cancer Genome Atlas (TCGA) database. Screening of autophagy-related genes was conducted using the human autophagy database (HADb). Bioinformatic analysis was conducted and included the following: univariate cox, lasso regression, and multivariate cox regression analysis; building overall survival assessment of the prognosis model; drawing the model of receiver operating characteristic (ROC) curve and determining the area under the curve; and a C-index reliability index assessment model through Kaplan-Meier screening of statistically significant genes in the model. The screening results were verified via Oncomine differential expression analysis. Gene set enrichment analysis (GSEA) was further used to analyze the molecular biological functions and related pathways of the gene model. RESULTS: Through cox regression and ROC analysis, the model showed that the risk score could accurately and independently predict the prognosis of EAC. The screening identified 4 genes: DAPK1, BECN1, ATG5, and VAMP7. GSEA showed that the high and low expression levels of the 4 genes were mainly enriched in biological functions, such as cell production and regulation, and metabolic pathways that maintain cell activity. CONCLUSIONS: Our research found that autophagy was involved in the process of EAC development and that several autophagy-related genes may provide prognostic information and clinical application value for patients with EAC.