KatzDriver: A network based method to cancer causal genes discovery in gene regulatory network. (March 2021) - Akhavan-Safar, Mostafa; Teimourpour, Babak
Abstract: One of the important problems in oncology is finding the genes that perturb the cell functionality and cause cancer. These genes, namely cancer driver genes (CDGs), when mutated, lead to the activation of the abnormal proteins. This abnormality is passed on to other genes by protein-protein interactions, which can cause cells to uncontrollably multiply and become cancerous. So, many methods have been introduced to predict this group of genes. Most of these methods are computational-based, which identify the CDGs based on mutations and genomic data. In this study, we proposed KatzDriver, as a network-based approach, in order to detect CDGs. This method is able to calculate the relative impact of each gene in the spread of abnormality in the gene regulatory network. In this approach, we firstly create the studied networks using gene expression and regulatory interaction data. Then by combining the topological and biological data, the weights of edges (regulatory interactions) and nodes (genes) are calculated. Afterward, based on the KATZ approach, the receiving and broadcasting powers of each gene were calculated to find the relative impact of each gene. At the end, the top genes with the highest relative impact ranks were selected as potential cancer drivers. The result of the proposed approach was compared with 18 existing computational and network-based methods in terms of F-measure, and the number of the predicted cancer driver genes. The result shows that our proposed algorithm is better than most of the other methods. KatzDriver is also able to detect a significant number of unique driver genes compared to other computational and network-based methods. Highlights: Cancer driver gene prediction is one of the most important problem in oncology studies. Majority of the methods, which have been proposed for the identification of CDGs, are based on the gene expression data as well as the concept of mutation in genomic data. This consequently results in a high false positive rate and a low F-measure. In this study, we proposed KatzDriver, as a network-based approach, to detect CDGs in human regulatory network. This method is able to calculate the relative impact of each gene in the broadcasting and receiving of abnormality in the transcriptional regulatory network. The result of the KatzDriver was compared with 18 previous computational and network-based methods based on F-measure as well as the number of the predicted cancer driver genes. The proposed approach and algorithm in F-measure and number of CDGs detected is better than the other methods.