Multi-Network Graph Contrastive Learning for Cancer Driver Gene Identification

Identifying driver genes contributing to the occurrence and development of cancers plays a critical role in cancer research and treatment. Some recent computational approaches identify cancer-driver genes based on gene networks, assuming that cancer-driver genes perform essential functions in gene networks. Due to the noise in gene function networks, many works focus on integrating gene networks derived from multi-omics datasets to improve the accuracy of cancer driver gene detection. However, most of them ignore the information interactions between these multi-omics datasets. In this work, we propose MNGCL, a Multi-Network Graph Contrastive Learning method to identify cancer driver genes. It first constructs three gene networks as different views based on protein interactions, gene semantic similarities, and gene co-occurrence in signaling pathways. Then, we perform data augmentation of these gene networks and input them into a graph contrastive learning (GCL) encoder with shared parameters to learn consistent gene feature representation in different networks from a holistic perspective. After that, the gene features from the GCL encoder are passed through three different graph convolutional networks to generate the unique gene feature representations in the three networks. Finally, we used a logistic regression model to fuse the gene feature representations generated in each network to predict cancer driver genes. The experimental results show that MNGCL improves the area under the ROC curve (AUC) and the area under the precision-recall curve (AUPRC) to a greater extent than the existing methods in identifying driver genes for both pan-cancer and single-type cancers. Furthermore, the ablation studies show that our model capturing dependencies and interactions between gene networks provided a more comprehensive perspective on the molecular mechanisms underlying cancer and improved the accuracy of cancer driver identification.