Prediction of Gastric Cancer-Related Proteins Based on Graph Fusion Method - Zhang, Hao; Xu, Ruisi; Ding, Meng; Zhang, Ying (Frontiers in Cell and Developmental Biology, 2021)
Gastric cancer is a common malignant tumor of digestive system with no specific symptoms. Due to the limited knowledge of pathogenesis, patients are usually diagnosed in advanced stage and do not have effective treatment methods. Proteome has unique tissue and time specificity, and can reflect the influence of external factors, which has become a potential biomarker for early diagnosing. Therefore, discovering gastric cancer-related proteins could greatly help researchers design drugs and develop early diagnosis kit. However, identifying gastric cancer-related proteins by biological experiments is time and money-consuming. With the high speed increase of data, it has become a hot issue to mine the knowledge of proteomics data on a large scale through computational methods. Based on the hypothesis that the stronger the association between the two proteins, the more likely they are to be associated with the same disease, in this paper, we constructed both disease similarity network and protein interaction network. Then, Graph Convolutional Network (GCN) was applied to extract topological features of these networks. Finally, Xgboost was used to identify the relationship between proteins and gastric cancer. 10-cross validation experiments results show high AUC(0.85) and AUPR(0.76) of our method, which proves the effectiveness of our method.