Prognostic prediction of carcinoma by a differential-regulatory-network-embedded deep neural network

The accurate prognostic prediction is essential for precise diagnosis and treatment of carcinoma. In addition to clinical survival prediction method, many computational methods based on transcriptomic data have been proposed to build the prediction models and study the prognosis of cancer patients. We propose a differential-regulatory-network-embedded deep neural network (DRE-DNN) method by integrating differential regulatory analysis based on gene co-expression network and deep neural network (DNN) method. From three public hepatocellular carcinoma (HCC) datasets, we derive differential regulatory network and embed regulatory information into DNN. By employing 1869 differential regulatory genes and survival data, we apply DRE-DNN to build a prediction model. We compare our method with the one which has all gene features in normal DNN, and results show that our method has better generalization ability and accuracy. We modify the normal DNN and develop an efficient method to predict prognosis of HCC from gene expression data. Our method decreases the inconsistence caused by the overfitting problem when the training sample size is small. DRE-DNN is also extendable for prognostic prediction of other cancers.