Cancer driver gene discovery in transcriptional regulatory networks using influence maximization approach

Abstract

Cancer driver genes (CDGs) are the genes whose mutations cause tumor growth. Several computational methods have been previously developed for finding CDGs. Most of these methods are sequence-based, that is, they rely on finding key mutations in genomic data to predict CDGs. In the present work, we propose iMaxDriver as a network-based tool for predicting driver genes by application of influence maximization algorithm on human transcriptional regulatory network (TRN). In the first step of this approach, the TRN is pruned and weighted by exploiting tumor-specific gene expression (GE) data. Then, influence maximization approach is used to find the influence of each gene. The top genes with the highest influence rate are selected as the potential driver genes. We compared the performance of our CDG prediction method with fifteen other computational tools, based on a benchmark of three different cancer types. Our results show that iMaxDriver outperforms most of the state-of-the-art algorithms for CDG prediction. Furthermore, iMaxDriver is able to correctly predict many CDGs that are overlooked by all previously published tools. Due to this relative orthogonality, iMaxDriver can be considered as a complementary approach to the sequence-based CDG prediction methods.