Ye Lu, Zhang Yi, Yang Xinying, Shen Fei, Xu Bo
Department of Gynecology, Guangdong Second Provincial General Hospital, Guangzhou, China.
Department of Thyroid Surgery, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, China.
Front Cell Dev Biol. 2021 Aug 13;9:730475. doi: 10.3389/fcell.2021.730475. eCollection 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 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.
卵巢癌（OC）是全球女性中最致命的疾病之一。它具有高度致死性，因为通常在晚期才被诊断出来，这可能会大大降低生存率。尽管大多数患者得到了及时有效的治疗，但由于OC的高复发率，生存率仍然很低。随着大量通过全基因组关联分析（GWAS）发现的OC风险区域，表达定量性状位点（eQTL）分析可以基于这些风险位点探索候选易感基因。然而，大量与OC相关的基因仍然未知。在本研究中，我们提出了一种基于不同组学数据和深度学习方法的新型基因预测方法，以识别OC致病基因。我们首先采用图注意力网络（GAT）来获得紧凑的基因特征表示，然后利用深度神经网络（DNN）来预测与OC相关的基因。结果，我们的模型实现了0.761的高AUC和0.788的AUPR，证明了我们提出的方法的准确性和有效性。最后，我们进行了基因集富集分析，以进一步探索OC的机制。最后，我们预测了245个新的OC致病基因和10条顶级相关KEGG通路。
