XGBG: A Novel Method for Identifying Ovarian Carcinoma Susceptible Genes Based on Deep Learning - Sun, Ke Feng; Sun, Li Min; Zhou, Dong; Chen, Ying Ying; Hao, Xi Wen; Liu, Hong Ruo; Liu, Xin; Chen, Jing Jing (Frontiers in Oncology, 2022)
Ovarian carcinomas (OC) represent a heterogeneous group of neoplasms consisting of several entities with pathogenesis, molecular profiles, multiple risk factors and outcomes. It has been regarded as the most lethal cancers among women all around the world. There are at least five main types of ovarian carcinomas 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 analysis (GWAS) and expression quantitative trait locus (eQTL) analyses, the knowledge of genomic landscape of complex diseases has been uncovered in large measure. Besides, pathway analyses also play an important role in exploring the underlying mechanism of complex diseases with providing curated pathway models and information about molecular dynamics and cellular processes. To investigate deeper about ovarian carcinomas, 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 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 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 a strong support to the results, which may help in understanding the pathogenesis and mechanisms of the disease.