A novel risk score model for stomach adenocarcinoma based on the expression levels of 10 genes

Stomach adenocarcinoma (STAD) accounts for 95% of cases of malignant gastric cancer, which is the third leading cause of cancer-associated mortality worldwide. The pathogenesis and effective diagnosis of STAD have become popular topics for research in the previous decade. In the present study, high-throughput RNA sequencing expression profiles and clinical data from patients with STAD were obtained from The Cancer Genome Atlas database and were used as a training dataset to screen differentially expressed genes (DEGs). Prognostic DEGs were identified using univariate Cox regression analysis and were further screened by the least absolute shrinkage and selection operator regularization regression algorithm. The resulting genes were used to construct a risk score model, the validation and effectiveness evaluation of which were performed on an independent dataset downloaded from the Gene Expression Omnibus database. Stratified and functional pathway (gene set enrichment) analyses were performed on groups with different estimated prognosis. A total of 92 genes significantly associated with STAD prognosis were obtained by univariate Cox regression analysis, and 10 prognosis-associated DEGs; hemoglobin b, chromosome 4 open reading frame 48, Dickkopf WNT signaling pathway inhibitor 1, coagulation factor V, serpin family E member 1, transmembrane protein 200A, NADPH oxidase organizer 1, C-X-C motif chemokine ligand 3, mannosidase endo-α-like and tripartite motif-containing 31; were selected for the development of the risk score model. The reliability of this prognostic method was verified using a validation set, and the results of multivariate Cox analysis indicated that the risk score may serve as an independent prognostic factor. In functional DEG analysis, eight Kyoto Encyclopedia of Genes and Genomes pathways were identified to be significantly associated with STAD risk factors. Thus, the 10-gene risk score model established in the present study was regarded as credible. This risk assessment tool may help identify patients with a high risk of STAD, and the proposed prognostic mRNAs may be useful in elucidating STAD pathogenesis.