MDMNI-DGD: A novel graph neural network approach for druggable gene discovery based on the integration of multi-omics data and the multi-view network.

Abstract

Accurately predicting druggable genes is of paramount importance for enhancing the efficacy of targeted therapies, reducing drug-related toxicities and improving patients' survival rates. Nevertheless, accurately predicting candidate cancer-druggable genes remains a critical challenge in translational medicine due to the high heterogeneity and complexity of cancer data. In this study, we proposed a novel graph neural approach called Druggable Gene Discovery based on the Integration of Multi-omics Data and the Multi-view Network (MDMNI-DGD), aiming to predict and evaluate cancer-druggable genes. MDMNI-DGD integrated a comprehensive set of multi-omics data, including copy number variations, DNA methylation, somatic mutations, and gene expression profiles. Simultaneously, it constructed the multi-view gene association network based on protein-protein interactions (PPI), protein structural domains, gene co-expression, pathway co-occurrence, gene sequence and gene ontology. Compared to other state-of-the-art approaches, MDMNI-DGD exhibits excellent performance in key evaluation metrics such as AUROC and AUPR. Moreover, the case study has also demonstrated the efficacy of our approach in discovering potentially druggable genes. Among more than 20,000 protein-coding genes, MDMNI-DGD successfully identified 872 potentially druggable genes. The findings from this investigation may serve to bolster the assessment of pan-cancer druggable genes, potentially catalyzing the development of more personalized and efficacious therapeutic interventions.