An Ovarian Cancer Susceptible Gene Prediction Method Based on Deep Learning Methods - Ye, Lu; Zhang, Yi; Yang, Xinying; Shen, Fei; Xu, Bo (Frontiers in Cell and Developmental Biology, 2021)
Ovarian Cancer (OC) is one of the most fatal diseases among women all around the world. It is highly lethal because it is usually diagnosed at an advanced stage which reduced the survival rate greatly. Even though most of patients have timely and effectively initial therapy, the survival rate is still low since the high recurrence rate of OC. With a large number of genome-wide association analysis (GWAS) have discovered risk regions of OC, expression quantitative trait locus (eQTL) analyses can explore candidate susceptible genes at these risk loci. However, there are still a large number of OC related genes are still unknown. In this study, we proposed a novel gene prediction method based on different omics data and deep learning methods to identify OC causal genes. We first employed graph attention network (GAT) to obtain compact gene feature representation from gene-gene interaction network, then a deep neural network (DNN) is utilized to predict OC related genes. As a result, our model achieved a high AUC of 0.761 and AUPR of 0.788, which proved the accuracy and effectiveness. At last, we conducted a gene set enrichment analysis to further explore the mechanism of OC. Finally, we predicted 245 novel OC causal genes and 10 top related KEGG pathways.