Identification of recurrent risk-related genes and establishment of support vector machine prediction model for gastric cancer

摘要

This study sought to investigate genes related to recurrent risk and establish a support vector machine (SVM) classifier for prediction of recurrent risk in gastric cancer (GC). Based on the gene expression profiling dataset GSE26253, feature genes that were significantly associated with survival time and status were screened out. Subsequently, protein-protein interaction (PPI) network was constructed for these feature genes, and genes in this network were optimized using betweenness centrality algorithm in order to identify genes potentially correlated with GC (named as GCGs).