DGRanker: Cancer Driver Gene Detection in Human Transcriptional Regulatory Network

Background: Cancer is a group of diseases that have received much attention in biological research because of its high mortality rate and the lack of accurate identification of its root causes. In such studies, researchers usually try to identify cancer driver genes (CDGs) that start cancer in a cell. The majority of the methods that have ever been proposed for the identification of CDGs are based on gene expression data and the concept of mutation in genomic data. Recently, using networking techniques and the concept of influence maximization, some models have been proposed to identify these genes. Objectives: We aimed to construct the cancer transcriptional regulatory network and identify cancer driver genes using a network science approach without the use of mutation and genomic data. Materials and Methods: In this study, we will employ the social influence network theory to identify CDGs in the human gene regulatory network (GRN) that is based on the concept of influence and power of webpages. First, we will create GRN Networks using gene expression data and Existing nodes and edges. Next, we will implement the modified algorithm on GRN networks being studied by weighting the regulatory interaction edges using the influence spread concept. Nodes with the highest ratings will be selected as the CDGs. Results: The results show our proposed method outperforms most of the other computational and network-based methods and show its superiority in identifying CDGs compared to many other methods. In addition, the proposed method can identify many CDGs that are overlooked by all previously published methods. Conclusions: Our study demonstrated that the Google’s PageRank algorithm can be utilized and modified as a network based method for identifying cancer driver gene in transcriptional regulatory network. Furthermore, the proposed method can be considered as a complementary method to the computational-based cancer driver gene identification tools.