Sun Ke Feng, Sun Li Min, Zhou Dong, Chen Ying Ying, Hao Xi Wen, Liu Hong Ruo, Liu Xin, Chen Jing Jing
Department of Obstetrics and Gynecology, First Affiliated Hospital, Heilongjiang University of Chinese Medicine, Harbin, China.
Department of Oncology, The Second Affiliated Hospital of Dalian Medical University, Dalian, China.
Front Oncol. 2022 May 12;12:897503. doi: 10.3389/fonc.2022.897503. eCollection 2022.
Ovarian carcinomas (OCs) represent a heterogeneous group of neoplasms consisting of several entities with pathogenesis, molecular profiles, multiple risk factors, and outcomes. OC has been regarded as the most lethal cancer among women all around the world. There are at least five main types of OCs classified by the fifth edition of the World Health Organization of tumors: high-/low-grade serous carcinoma, mucinous carcinoma, clear cell carcinoma, and endometrioid carcinoma. With the improved knowledge of genome-wide association study (GWAS) and expression quantitative trait locus (eQTL) analyses, the knowledge of genomic landscape of complex diseases has been uncovered in large measure. Moreover, pathway analyses also play an important role in exploring the underlying mechanism of complex diseases by providing curated pathway models and information about molecular dynamics and cellular processes. To investigate OCs deeper, we introduced a novel disease susceptible gene prediction method, XGBG, which could be used in identifying OC-related genes based on different omics data and deep learning methods. We first employed the graph convolutional network (GCN) to reconstruct the gene features based on both gene feature and network topological structure. Then, a boosting method is utilized to predict OC susceptible genes. As a result, our model achieved a high AUC of 0.7541 and an AUPR of 0.8051, which indicates the effectiveness of the XGPG. Based on the newly predicted OC susceptible genes, we gathered and researched related literatures to provide strong support to the results, which may help in understanding the pathogenesis and mechanisms of the disease.
卵巢癌（OCs）是一组异质性肿瘤，由几种具有不同发病机制、分子特征、多种风险因素和预后的实体组成。OC被认为是全球女性中最致命的癌症。根据世界卫生组织肿瘤学第五版，OC至少有五种主要类型：高/低级别浆液性癌、黏液性癌、透明细胞癌和子宫内膜样癌。随着全基因组关联研究（GWAS）和表达定量性状位点（eQTL）分析知识的不断完善，复杂疾病的基因组图谱在很大程度上已被揭示。此外，通路分析通过提供经过整理的通路模型以及有关分子动力学和细胞过程的信息，在探索复杂疾病的潜在机制方面也发挥着重要作用。为了更深入地研究OC，我们引入了一种新的疾病易感基因预测方法XGBG，该方法可用于基于不同组学数据和深度学习方法识别与OC相关的基因。我们首先利用图卷积网络（GCN）基于基因特征和网络拓扑结构重建基因特征。然后，使用一种提升方法来预测OC易感基因。结果，我们的模型实现了0.7541的高AUC和0.8051的AUPR，这表明XGPG的有效性。基于新预测的OC易感基因，我们收集并研究了相关文献，为结果提供有力支持，这可能有助于理解该疾病的发病机制。
