DGMP: Identifying Cancer Driver Genes by Jointing DGCN and MLP from Multi-omics Genomic Data

Abstract

Identification of cancer driver genes plays an important role in precision oncology research, which is helpful to understand the cancer initiation and progression. However, most of existing computational methods mainly used the protein-protein interaction networks (PPIs), or treated the directed gene regulatory networks (GRNs) as the undirected gene-gene association networks to identify the cancer driver genes, which will lose the unique structure regulatory information in the directed GRNs, and then affect the outcome of the cancer driver genes identification. Here, based on the multi-omics pan-cancer data (i.e., gene expression, mutation, copy number variation and DNA methylation), we proposed a novel method (called DGMP) to identify cancer driver genes by jointing Directed Graph Convolution Network (DGCN) and Multilayer Perceptron (MLP). DGMP learns the multi-omics features of genes as well as the topological structure features in GRN with DGCN model, and uses MLP to weight more on gene features for mitigating the bias toward the graph topological features in DGCN learning process. The results on three gene regulation networks show that DGMP outperforms other existing state-of-the-art methods. It can not only identify highly mutated cancer driver genes but also the driver genes harboring other kinds of alterations (e.g., differential expression, aberrant DNA methylation) or genes involved in GRNs with other cancer genes. The source code of DGMP can be freely downloaded from https://github.com/NWPU-903PR/DGMP.