He Xin, Li Wei-Song, Qiu Zhen-Gang, Zhang Lei, Long He-Ming, Zhang Gui-Sheng, Huang Yang-Wen, Zhan Yun-Mei, Meng Fan
Department of Respiratory and Critical Care, The First Affiliated Hospital of Gannan Medical University, Ganzhou, China.
Department of pathology, The First Affiliated Hospital of Gannan Medical University, Ganzhou, China.
Front Oncol. 2022 Aug 16;12:982641. doi: 10.3389/fonc.2022.982641. eCollection 2022.
The incidence of esophageal cancer has obvious genetic susceptibility. Identifying esophageal cancer-related genes plays a huge role in the prevention and treatment of esophageal cancer. Through various sequencing methods, researchers have found only a small number of genes associated with esophageal cancer. In order to improve the efficiency of esophageal cancer genetic susceptibility research, this paper proposes a method for large-scale identification of esophageal cancer-related genes by computational methods. In order to improve the efficiency of esophageal cancer genetic susceptibility research, this paper proposes a method for large-scale identification of esophageal cancer-related genes by computational methods. This method fuses graph convolutional network and logical matrix factorization to effectively identify esophageal cancer-related genes through the association between genes. We call this method GCNLMF which achieved AUC as 0.927 and AUPR as 0.86. Compared with other five methods, GCNLMF performed best. We conducted a case study of the top three predicted genes. Although the association of these three genes with esophageal cancer has not been reported in the database, studies by other reseachers have shown that these three genes are significantly associated with esophageal cancer, which illustrates the accuracy of the prediction results of GCNLMF.
食管癌的发病率具有明显的遗传易感性。识别与食管癌相关的基因在食管癌的防治中发挥着巨大作用。通过各种测序方法，研究人员仅发现了少数与食管癌相关的基因。为了提高食管癌遗传易感性研究的效率，本文提出了一种通过计算方法大规模识别食管癌相关基因的方法。为了提高食管癌遗传易感性研究的效率，本文提出了一种通过计算方法大规模识别食管癌相关基因的方法。该方法融合了图卷积网络和逻辑矩阵分解，通过基因之间的关联有效地识别食管癌相关基因。我们将这种方法称为GCNLMF，其AUC为0.927，AUPR为0.86。与其他五种方法相比，GCNLMF表现最佳。我们对预测排名前三的基因进行了案例研究。虽然数据库中尚未报道这三个基因与食管癌的关联，但其他研究人员的研究表明这三个基因与食管癌显著相关，这说明了GCNLMF预测结果的准确性。
