A Four−Gene-Based Risk Score With High Prognostic Value in Gastric Cancer - Zhang, Bingdong; Li, Yuerui; Yang, Liu; Chen, Yongbing (Frontiers in Oncology, 2021)
Background: Gastric adenocarcinoma (GC) is an important contributor to cancer mortality and morbidity. This study aimed to explore the prognostic value of mutation pattern in GC. Material and methods: We extracted somatic mutation data for 437 GC samples from The Cancer Genome Atlas project (TCGA) Stomach adenocarcinoma (STAD) cohort. Kaplan-Meier survival in the R package maftools was used to analyze associations between mutations and survival. Multiply Cox proportional model was used to establish risk formula. A four-gene based risk score was developed to predict the overall survival of patients with GC. We used Tianjin cohort dataset with survival information to further evaluate the clinical value of this mutation signature. Results: 45 survival-related mutated genes were identified and verified, most of which were co-occurring in their mutation pattern and co-occurring with MLH3 and POLE mutations. GC samples with the 45-gene mutations had a significantly higher mutation count. Four-gene (UTRN, MUC16, CCDC178 and HYDIN) mutation status was used to build a prognostic risk score which could be translated into the clinical setting. The association between the four-gene based signature and overall survival remained statistically significant after controlling forage, sex, TNM stage and POLE mutation status in the multivariate model (HR, 1.88; 95% CI, 1.33-2.7; P<0.001). The prognostic significance of four-gene based risk score identified in the TCGA cohort was validated in the Tianjin cohort. Conclusion: A four-mutated genes risk formula was developed that correlated with the overall survival of patients with GC using a multivariable cox regression model. In 2 independent genomic data sets from TCGA and Tianjin cohorts, low risk scores were associated with higher tumor mutation loads and improved outcome in patients with GC. This finding may have implications for prognostic prediction and therapeutic guidance for GC.