An Ovarian Cancer Susceptible Gene Prediction Method Based on Deep Learning Methods

Frontiers in Cell and Developmental Biology

卷:9

Xinying Yang¹  Lu Ye¹  Yi Zhang¹  Bo Xu²  Fei Shen² 

[1] Department of Gynecology, Guangdong Second Provincial General Hospital, Guangzhou, China;

[2] Department of Thyroid Surgery, Guangzhou First People’s Hospital, School of Medicine, South China University of Technology, Guangzhou, China;

关键词: ovarian cancer;    gene prediction;    omics data;    deep learning method;    pathway analysis;   

DOI  :  10.3389/fcell.2021.730475

来源: DOAJ

【 摘 要 】

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 may reduce the survival rate greatly. Even though most of the patients are treated timely and effectively, the survival rate is still low due to the high recurrence rate of OC. With a large number of genome-wide association analysis (GWAS)-discovered risk regions of OC, expression quantitative trait locus (eQTL) analyses can explore candidate susceptible genes based on these risk loci. However, a large number of OC-related genes remain 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 a compact gene feature representation, 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 of our proposed method. 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.

【 授权许可】

Unknown