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

Abstract

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.