Construction and validation of prognostic model based on autophagy-related lncRNAs in gastric cancer

Gastric cancer (GC) is one of the most common cancer worldwide. Although emerging evidence indicates that autophagy-related long non-coding RNA (lncRNA) plays an important role in the progression of GC, the prognosis of GC based on autophagy is still deficient. The Cancer Genome of Atlas stomach adenocarcinoma (TCGA-STAD) dataset was downloaded and separated into a training set and a testing set randomly. Then, 24 autophagy-related lncRNAs were found strongly associated with the survival of the TCGA-STAD dataset. 11 lncRNAs were selected to build the risk score model through the least absolute shrinkage and selection operator (LASSO) regression. Every patient got a risk score (RS), and patients were separated into a high-risk group and a low-risk group due to the median RS. The multivariate Cox analysis showed that the RS could be an independent prognosis predictor. The Kaplan-Meier survival analysis and the Receiver Operating Characteristic (ROC) curve indicated the model had an excellent prediction effect. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed that the mRNAs in the prognostic network were mainly involved in the autophagy and ubiquitin-like protein ligase binding. Gene Set Enrichment Analysis (GSEA) analysis uncovered that the differentially expressed genes (DEGs) in the high-risk group partially participated in the ECM receptor interaction and other signaling pathways. Our results indicated that the risk score model based on the autophagy-related lncRNAs performed well in the prediction of prognosis for patients with GC.