MHN-GCN: A Cancer Driver Gene Prediction Method for Multi-Omics Data

Identifying cancer driver genes is crucial for studying targeted therapies. Due to the complexity of cancer, single-omics data often fall short in elucidating cancer genes. Therefore, this paper proposes a novel cancer gene prediction method, MHN-GCN, which integrates data from gene expression, DNA methylation, mutations, and copy number variations to predict cancer genes accurately. By comparing with currently popular single-omics data prediction methods and multi-omics data prediction methods, it was found that MHN-GCN performed well in most metrics. Subsequently, bioinformatics analysis was conducted on the predicted genes to explore these suspicious genes' functions and potential mechanisms, providing new research directions. The MHN-GCN proposed in this paper provides valuable insights for predicting cancer driver genes.