Prediction of Cancer Driver Genes through Integrated Analysis of Differentially Expressed Genes at the Individual Level

Introduction It is expected that certain driver mutations may alter the gene expression of their associated or interacting partners, including cognate proteins.Methods We introduced DEGdriver, a novel method that can discriminate between mutations in drivers and passengers by utilizing gene differential expression at the individual level.Results After being tested on eleven TCGA cancer datasets, DEGdriver substantially outperformed cutting-edge approaches in distinguishing driver genes from passengers and exhibited robustness to varying parameters and protein-protein interaction networks.Conclusion Through enrichment analysis, we prove that DEGdriver can identify functional modules or pathways in addition to novel driver genes.