Bidirectional deep neural networks to integrate RNA and DNA data for predicting outcome forpatients withhepatocellular carcinoma.

Abstract

Aims: Individualized patient profiling is instrumental for personalized management in hepatocellular carcinoma (HCC). This study built a model based on bidirectional deep neural networks (BiDNNs), an unsupervised machine-learning approach, to integrate multi-omics data and predict survival in HCC. Methods: DNA methylation and mRNA expression data for HCC samples from the The Cancer Genome Atlas database were integrated using BiDNNs. With optimal clusters as labels, a support vector machinemodel was developed to predict survival. Results: Using the BiDNN-based model, samples were clustered into two survival subgroups. The survival subgroup classification was an independent prognostic factor. BiDNNs were superior to multimodal autoencoders. Conclusion:Thisstudy constructed and validated a BiDNN-based model for predicting prognosis in HCC, withimplications for individualized therapies in HCC.