Abstract
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.
Highlights
Cellular signaling pathways are composed of a number of proteins between which information is transmitted via chemical reactions
The methods employed by the packages DriverNet, VarWalker and DawnRank to predict cancer driver genes involve the use of well-established algorithms that are very different
VarWalker Random Walk with Restart Mutation only The Cancer Genome Atlas (TCGA) Cancer Genome Census (CGC)
Summary
Cellular signaling pathways are composed of a number of proteins between which information is transmitted via chemical reactions. This flow of signals between cells and within cells allows them to respond appropriately to biological needs. Such processes form extremely complex and carefully regulated pathways that branch out to reach a number of effector proteins. As a consequence of this, a single protein is able to influence multiple cellular processes such as cell division, protein synthesis, and cell death. Interactions include protein–protein binding, protein degradation, phosphorylation, and protein–DNA binding. Intracellular pathways do not operate in isolation, but are cross-linked to other pathways that together form a huge web
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