Prediction of cancer driver genes through integrated analysis of differentially expressed genes at the individual level

Abstract Driver mutations are anticipated to change the gene expression of their related or interacting partners, or cognate proteins. We introduce DEGdriver, a novel method that can discriminate between mutations in drivers and passengers by utilizing gene differential expression at the individual level. Tested on eleven TCGA cancer datasets, DEGdriver substantially outperforms cutting-edge approaches in distinguishing driver genes from passengers and exhibits robustness to varying parameters and protein-protein interaction networks. We further show, through enrichment analysis, that DEGdriver is capable of identifying functional modules or pathways in addition to novel driver genes.