Pathway- and clinical-factor-based risk model predicts the prognosis of patients with gastric cancer.

Gastric cancer (GC) has a high incidence and mortality rate. If discovered late, GC tends to have a poor prognosis. Improvements in the prognostic accuracy of GC through combined analysis of multiple relevant genes and clinical factors may solve this problem. In the present study, GSE62254 (including 300 GC tissues), obtained from the Gene Expression Omnibus database, was used as a training set, and the mRNA‑sequencing data of GC (including 384 GC tissues) downloaded from the Cancer Genome Atlas database served as a validation set. Based on the t‑test and Wilcoxon test, the significantly differentially expressed genes (DEGs) were obtained by screening the intersecting DEGs. The prognosis-associated genes and clinical factors were identified using Cox regression analysis in the R survival package. The optimal prognosis‑associated pathways were examined using the Cox‑proportional hazards (Cox‑PH) model in the R penalized package. Finally, risk prediction models were constructed and validated using the Cox‑PH model and the Kaplan‑Meier method, respectively. There were a total of 382 significant DEGs, including 268 upregulated genes and 114 downregulated genes. A total of 50 prognosis‑associated genes were identified, 16 optimal prognosis‑associated pathways (including mitochondrial pathway and the tyrosine‑protein kinase JAK‑signal transducer and activator of transcription signaling pathway, which involve caspase 7, phosphoinositide‑3‑kinase regulatory subunit 3, peroxisome proliferator‑activated receptor γ and collagen triple helix repeat containing 1) and four prognosis‑associated clinical factors [including Pathologic_N, Pathologic_stage, mutL homolog 1 (MLH1) mutation and recurrence]. The pathway‑ and clinical‑factor‑based risk prediction model exhibited marked prognostic accuracy. The clinical‑factor‑based risk prediction model with improved P‑values for prognosis prediction may be superior to the pathway‑based risk prediction model in predicting the prognosis of GC patients.