Development and validation of a survival model for esophageal adenocarcinoma based on autophagy-associated genes

Autophagy is a highly conserved catabolic process which has been implicated in esophageal adenocarcinoma (EAC). We sought to investigate the biological functions and prognostic value of autophagy-related genes (ARGs) in EAC. A total of 21 differentially expressed ARGs were identified between EAC and normal samples. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were then applied for the differentially expressed ARGs in EAC, and the protein–protein interaction (PPI) network was established. Cox survival analysis and Lasso regression analysis were performed to establish a prognostic prediction model based on nine overall survival (OS)-related ARGs (CAPN1, GOPC, TBK1, SIRT1, ARSA, BNIP1, ERBB2, NRG2, PINK1). The 9-gene prognostic signature significantly stratified patient outcomes in The Cancer Genome of Atlas (TCGA)-EAC cohort and was considered as an independently prognostic predictor for EAC patients. Moreover, Gene set enrichment analysis (GSEA) analyses revealed several important cellular processes and signaling pathways correlated with the high-risk group in EAC. This prognostic prediction model was confirmed in an independent validation cohort (GSE13898) from The Gene Expression Omnibus (GEO) database. We also developed a nomogram with a concordance index of 0.78 to predict the survival possibility of EAC patients by integrating the risk signature and clinicopathological features. The calibration curves substantiated favorable concordance between actual observation and nomogram prediction. Last but not least, Erb-B2 Receptor Tyrosine Kinase 2 (ERBB2), a member of the prognostic gene signature, was identified as a potential therapeutic target for EAC patients. To sum up, we established and verified a novel prognostic prediction model based on ARGs which could optimize the individualized survival prediction in EAC