A network-based method for identifying cancer driver genes based on node control centrality

Cancer is one of the major contributors to human mortality and has a serious influence on human survival and health. In biomedical research, the identification of cancer driver genes (cancer drivers for short) is an important task; cancer drivers can promote the progression and generation of cancer. To identify cancer drivers, many methods have been developed. These computational models only identify coding cancer drivers; however, non-coding drivers likewise play significant roles in the progression of cancer. Hence, we propose a Network-based Method for identifying cancer Driver Genes based on node Control Centrality (NMDGCC), which can identify coding and non-coding cancer driver genes. The process of NMDGCC for identifying driver genes mainly includes the following two steps. In the first step, we construct a gene interaction network by using mRNAs and miRNAs expression data in the cancer state. In the second step, the control centrality of the node is used to identify cancer drivers in the constructed network. We use the breast cancer dataset from The Cancer Genome Atlas (TCGA) to verify the effectiveness of NMDGCC. Compared with the existing methods of cancer driver genes identification, NMDGCC has a better performance. NMDGCC also identifies 295 miRNAs as non-coding cancer drivers, of which 158 are related to tumorigenesis of BRCA. We also apply NMDGCC to identify driver genes related to the different breast cancer subtypes. The result shows that NMDGCC detects many cancer drivers of specific cancer subtypes.

Automated summary

Cancer is a major cause of human mortality with a significant impact on survival and health. Many computational methods have been developed to identify cancer driver genes, but they mainly focus on coding genes, disregarding the role of non-coding genes. In this study, we propose a network-based method called NMDGCC that can identify both coding and non-coding cancer driver genes. The method involves constructing a gene interaction network using mRNA and miRNA expression data, and then using node control centrality to identify cancer drivers. Testing on breast cancer datasets shows that NMDGCC outperforms existing methods and identifies several non-coding miRNA cancer drivers, particularly those related to the tumorigenesis of BRCA. Furthermore, NMDGCC successfully detects cancer drivers specific to different breast cancer subtypes.