Abstract
Background: Genomic features including tumor mutation burden (TMB), microsatellite instability (MSI) and somatic copy number alteration (SCNA), had been demonstrated to be involved with the tumor microenvironment (TME) and outcome of gastric cancer (GC). Methods: We obtained profiles of TMB, MSI and SCNA by processing 405 GC data from The Cancer Genome Atlas (TCGA), then conducted a comprehensive analysis though “iClusterPlus”. Another independent Gene Expression Omnibus (GEO) contained specimens from 109 GC patients was designed as an external validation. Results: Two subgroups were generated, with distinguished prognosis, somatic mutation burden, copy number changes and immune landscape. We revealed that Cluster1 was marked by a better prognosis, accompanied by higher TMB, MSIsensor score, TMEscore, and lower SCNA burden. Based on these clusters, we screened 196 differentially expressed genes (DEGs), which were subsequently projected into univariate Cox survival analysis. Thus, we constructed a 9-gene immune risk score (IRS) model using lasso penalized logistic regression. Moreover, the prognostic prediction of IRS was verified by receiver operating characteristic (ROC) curve analysis and nomogram plot.Conclusions: Our works suggested that the 9‐gene‐signature prediction model, which derived from TMB, MSI, SCNA was a promising predictive tool for clinical outcome in GC patients. This novel methodology may help clinicians uncover the underlying mechanisms and guide future treatment strategies.
CNV Radar: an improved method for somatic copy number alteration characterization in oncology
