Establishment and verification of prognostic model for gastric cancer based on autophagy-related genes

Autophagy played a significant role in the development of cancer. In this study, we explored the value of autophagy-associated genes in gastric cancer. RNA sequencing and clinical information containing 375 gastric cancer and 32 normal tissues were gathered from the TCGA portal. Then we stochastically allocated the autophagy-associated genes (AAGs) to training and testing groups. Next, we screened the discrepantly expressed AAGs and the prognostic AAGs by Cox regression analysis and Lasso regression analysis. Afterwards, we structured the model by using the prognostic AAGs and plotted Kaplan-Meier (KM) and receiver operating characteristic (ROC) curves to verify the performance of models in both groups. Besides, we utilized Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses to explore the molecular mechanisms of AAGs in gastric cancer. Finally, we demonstrated discrepant expression of AAGs within gastric cancer and non-tumor tissues at protein level with immunohistochemistry. 28 discrepantly expressed AAGs were screened from the TCGA database which contained 375 gastric cancer and 32 non-tumor samples. Cox and Lasso regression analyses were performed in training group and then we got 5 prognostic AAGs to establish the prognostic model. The patients who had high risk possessed worse overall survival (OS) both in training group (5-year OS, 47.6% vs 23.1%; P < 0.0001) and test group (5-year OS, 49.2% vs 0%, P=0.019). The proportion under ROC curves (AUC) were significant both in training group and test group (5-year AUC, 0.736 vs 0.809). Through this study, we constructed a model for gastric cancer patients which may provide individual treatment and superior prognosis.