cMaxDriver: A Centrality Maximization Intersection Approach for Prediction of Cancer-Causing Genes in the Transcriptional Regulatory Network

Cancer-causing genes are genes in which mutations cause the onset and spread of cancer. These genes are called driver genes or cancer-causal genes. Several computational methods have been proposed so far to find them. Most of these methods are based on the genome sequencing of cancer tissues. They look for key mutations in genome data to predict cancer genes. This study proposes a new approach called centrality maximization intersection, cMaxDriver, as a network-based tool for predicting cancer-causing genes in the human transcriptional regulatory network. In this approach, we used degree, closeness, and betweenness centralities, without using genome data. We first constructed three cancer transcriptional regulatory networks using gene expression data and regulatory interactions as benchmarks. We then calculated the three mentioned centralities for the genes in the network and considered the nodes with the highest values in each of the centralities as important genes in the network. Finally, we identified the nodes with the highest value between at least two centralities as cancer causal genes. We compared the results with eighteen previous computational and network-based methods. The results show that the proposed approach has improved the efficiency and F-measure, significantly. In addition, the cMaxDriver approach has identified unique cancer driver genes, which other methods cannot identify.