GD-Net: An Integrated Multimodal Information Model Based on Deep Learning for Cancer Outcome Prediction and Informative Feature Selection.

Multimodal information provides valuable resources for cancer prognosis and survival prediction. However, the computational integration of this heterogeneous data information poses significant challenges due to the complex interactions between molecules from different biological modalities and the limited sample size. Here, we introduce GD-Net, a Graph Deep learning algorithm to enhance the accuracy of survival prediction with an average accuracy of 72% by early fusing of multimodal information, which includes an interpretable and lightweight XGBoost module to efficiently extract informative features. First, we applied GD-Net to eight cancer datasets and achieved superior performance compared to benchmarking methods, with an average 7.9% higher C-index value. The ablation experiments strongly supported that multi-modal integration could significantly improve accuracy over the single-modality model. In the deep case study of liver cancer, 319 differential genes, 15 differential miRNAs and 155 methylated differential genes based on the predicted risk subgroups are identified as the informative features, and then we have statistically and biologically validated the efficacy of these key molecules in internal and external test datasets. The comprehensive independent validations demonstrated that GD-Net is accurate and competitive in predicting different cancer outcomes in real-time, and it is an effective tool for identifying new multimodal prognosis biomarkers.