Combining Gene Expression Signature With Clinical Features for Survival Stratification of Gastric Cancer

Abstract Background: The AJCC staging system is considered as the golden standard in clinical practice. However, it remains some pitfalls in assessing the prognosis of gastric cancer (GC) patients with similar clinicopathological characteristics. We aim to develop a new clinic and genetic risk score (CGRS) to improve the prognosis prediction of GC patients.Methods: The gene expression profiles of the training set from the Asian Cancer Research Group (ACRG) cohort were used for developing genetic risk score (GRS) by LASSO-Cox regression algorithms. CGRS was established by integrating GRS with clinical risk score (CRS) derived from Surveillance, Epidemiology, and End Results (SEER) database. GRS and CGRS were validated in ACRG validation set and other four independent GC cohorts with different data types, such as microarray, RNA sequencing, and qRT-PCR. Multivariable Cox regression was adopted to evaluate the independence of GRS and CGRS in prognosis evaluation.Results: We established GRS based on a nine-gene signature including APOD, CCDC92, CYS1, GSDME, ST8SIA5, STARD3NL, TIMEM245, TSPYL5, and VAT1. GRS and CGRS dichotomized GC patients into high and low risk groups with significantly different prognosis in four independent cohorts, including our Zhejiang cohort (all HR > 1, all P < 0.001). Both GRS and CGRS were prognostic signatures independent of the AJCC staging system. Receiver operating characteristic (ROC) analysis showed that area under ROC curve of CGRS was larger than that of the AJCC staging system in most cohorts we studied. Nomogram and web tool (http://39.100.117.92/CGRS/) based on CGRS were developed for clinicians to conveniently assess GC prognosis in clinical practice.Conclusions: CGRS integrating genetic signature with clinical features shows strong robustness in predicting GC prognosis, and can be easily applied in clinical practice through the web application.