A bioinformatics analysis to evaluate the prognostic value of stemness-related genes in gastric cancer

BACKGROUND: This study aimed to identify potential stemness-related targets in gastric cancer (GC) in order to support the development of new treatment strategies and improve patient survival. METHODS: Using the edgeR package, we identified stemness-related differentially expressed genes (DEGs) using GSE112631 and the stemness-related signaling pathways in the Gene Set Enrichment Analysis (GSEA) database. Lasso-penalized Cox regression analysis and multivariate Cox regression analysis tested by Akaike Information Criterion (AIC) were used to screen out survival genes in order to construct a prognostic model. We verified the accuracy of our prognostic model using a nomogram and receiver operating characteristic (ROC) curve analysis. Patients were divided into two groups based on the median risk score, and functional enrichment analysis was used to explore the differences between the two groups. RESULTS: Eight genes were selected to establish a prognostic model of The Cancer Genome Atlas (TCGA) and a validation model of the GSE84437 dataset from the Genome Expression Omnibus (GEO). In both models, we found that the low risk score group had better overall survival (OS) than the high-risk score group. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways between the two risk groups were totally different. CONCLUSIONS: We used eight stemness-related genes to build a prognostic model. The high-risk score group had a worse prognosis compared to the low-risk score group.