Ranking cancer drivers via betweenness-based outlier detection and random walks

Abstract

Background Recent cancer genomic studies have generated detailed molecular data on a large number of cancer patients. A key remaining problem in cancer genomics is the identification of driver genes. Results We propose BetweenNet, a computational approach that integrates genomic data with a protein-protein interaction network to identify cancer driver genes. BetweenNet utilizes a measure based on betweenness centrality on patient specific networks to identify the so-called outlier genes that correspond to dysregulated genes for each patient. Setting up the relationship between the mutated genes and the outliers through a bipartite graph, it employs a random-walk process on the graph, which provides the final prioritization of the mutated genes. We compare BetweenNet against state-of-the art cancer gene prioritization methods on lung, breast, and pan-cancer datasets. Conclusions Our evaluations show that BetweenNet is better at recovering known cancer genes based on multiple reference databases. Additionally, we show that the GO terms and the reference pathways enriched in BetweenNet ranked genes and those that are enriched in known cancer genes overlap significantly when compared to the overlaps achieved by the rankings of the alternative methods.

Exploring gene-patient association to identify personalized cancer driver genes by linear neighborhood propagation

“…Subsequently, it ranks the the potential driver genes based on their influence scores evaluated by the edge weights in the bipartite graph. Similarly, BetweenNET [ 22 ] combines patient genomic data with protein–protein interaction network to build customized gene interaction network and identifies personalized cancer driver genes in the customized network. Meanwhile, based on the structural controllability theory.…”