Development and verification of a microsatellite instability-related risk signature for predicting survival and therapy effectiveness in gastric cancer.

ABSTRACT

Background: Gastric cancer (GC) is one of is one of the most common malignancy among digestive system cancers worldwide. Increasing evidence has revealed that microsatellite instability (MSI) status can affect the survival in various cancers. However, the role of MSI status in GC remains uncertain. Methods: The RNA-seq and clinicopathological features and mutation data of GC was obtained from The Cancer Genome Atlas (TCGA). Different bioinformatic and statistical methods were combined to construct a robust MSI-related gene signature for prognosis. Gene set enrichment analysis was conducted to explore Kyoto Encyclopedia of Genes and Genomes pathways associated with the MSI-related risk signature. Moreover, Kaplan-Meier (K-M) survival and receiver operating characteristic (ROC) analyses evaluate that the MSI-related risk signature. Immune-associated miRNAs were identified using immune scores calculated by the ssGSEA. In addition, 'pRRophetic' R package was used to assess the chemotherapeutic response by the GDSC website. Results: We firstly analyzed the influence of MSI status to GC survival based on the data from the TCGA database. GC patients in the TCGA database were divided into MSI-H and MSI-L/MSS groups. We counted the survival conditions of GC patients in these two groups. In addition, we also calculated the difference of TMB between these two groups and found that MSI-H group had a relatively high survival rate. Next, we identified 99 highly mutated genes in MSI-H group and constructed a MSI-related risk signature based on 10 robust genes for predicting the overall survival (OS) of GC patients. Moreover, analyses indicated that the MSI-related risk signature can accurately predict 1-, 3- and 5-year OS of GC patients. Furthermore, enrichment analysis suggested that genes between the high- and low-risk groups mainly involved in mutation and DNA repair related pathways. Finally, we also found that the MSI-related risk signature can affect the TME immune cell infiltration in GC and can be used to predict the clinical response to immunotherapy. Conclusions: In the present study, we develop a MSI-related risk signature for predicting the survival and therapy of GC, which may contribute to the clinical treatment of GC.