IMRDriver: coding and non-coding cancer driver genes identification based on network propagation

In cancer genomics, the identification of Cancer Driver Genes (CDGs) is a major scientific interest. CDGs can be identified by numerous methods, however the false positive rate still remains high. In addition, non-coding genes, such as miRNAs, can also operate as CDGs due to their regulatory functions in the development of cancer. In this paper, we present IMRDriver, a novel method for identifying both protein-coding and non-coding CDGs based on network propagation. The method first employs gene expression data, copy number variation data, single nucleotide variation data, and gene interaction data to construct a node-weighted gene network. Then, the network topology is combined with the reverse network propagation to rank all genes, with the top ranked genes predicted to be CDG candidates. We compared the prediction results of IMRDriver to twelve other methods and found that IMRDriver outperforms in terms of accuracy, recall, and F1 score. In addition, IMRDriver identified a number of miRNAs as non-coding CDGs, the majority of which have been verified in the scientific literature. In summary, IMRDriver is an effective approach for predicting CDGs. Source code of our paper is available at https://github.com/cczxsong/IMRDriver.