The use of gene interaction networks to improve the identification of cancer driver genes

• Published in: PeerJ (San Francisco, CA) Vol. 5; p. e2568
• Main Authors: Ramsahai, Emilie, Walkins, Kheston, Tripathi, Vrijesh, John, Melford
• Format: Journal Article
• Language: English
• Published: United States PeerJ. Ltd 26-01-2017; PeerJ, Inc; PeerJ Inc
• Subjects: Acids; Algorithm; Algorithms; Analysis; Bioinformatics; Biology; Cancer; Cancer genetics; Cell division; Cell growth; Computational Biology; Computational Science; Data analysis; Driver genes; Gene expression; Genes; Genetic aspects; Genomes; Identification; Interaction network; Mutation; Proteins; Statistics; Weighted network; Driver genes; Graph; Interaction network; Mutation; Gene expression; Algorithm; Cancer; Weighted network
• ISSN: 2167-8359; 2167-8359
• DOI: 10.7717/peerj.2568

Bioinformaticians have implemented different strategies to distinguish cancer driver genes from passenger genes. One of the more recent advances uses a pathway-oriented approach. Methods that employ this strategy are highly dependent on the quality and size of the pathway interaction network employed, and require a powerful statistical environment for analyses. A number of genomic libraries are available in R. DriverNet and DawnRank employ pathway-based methods that use gene interaction graphs in matrix form. We investigated the benefit of combining data from 3 different sources on the prediction outcome of cancer driver genes by DriverNet and DawnRank. An enriched dataset was derived comprising 13,862 genes with 372,250 interactions, which increased its accuracy by 17% and 28%, respectively, compared to their original networks. The study identified 33 new candidate driver genes. Our study highlights the potential of combining networks and weighting edges to provide greater accuracy in the identification of cancer driver genes.