Investigating the evolution process of lung adenocarcinoma via random walk and dynamic network analysis - Chen, Bolin; Zhang, Jinlei; Wang, Teng; Shao, Ci; Miao, Lijun; Zhang, Shengli; Shang, Xuequn (Frontiers in Genetics, 2022)
Lung adenocarcinoma (LUAD) is a typical disease whose progression is regarded as multi-stages. However, many existent methods often ignore the critical differences among these stages and thereby losing the efficiency for discovering key biological molecules and biological functions as signals via multi-stage disease. In this study, we proposed a method to discover the evolution between biological molecules and biological functions via multi-stage biological molecules of LUAD. The method is based on the Random Walk algorithm and the Monte Carlo method to generate clusters as the modules, which were concerned as subgraphs of the differentiated biological molecules network in one stage. The connection between modules of adjacent stages is based on the measurement of the Jaccard coefficient. The online gene set enrichment analysis tool (DAVID) was used to fetch biological functions corresponding to individual important modules. The core evolution network was constructed by combining the above two networks. Since the networks here are all dynamic, we also proposed a strategy to visualize the dynamic information together in one network. Eventually, 12 core modules and 11 core biological functions were found through such evolutionary analyses. And among the core biological functions we obtained, 6 of them are related to the disease, and the biological function of neutrophil chemotaxis is not straightly associated with LUAD but can serve as a prediction, 2 of them may serve as a predictive signal, and 2 of them need to be verified through more biological evidence. Compared with the two designing methods, the proposed method in this study performed more efficiently.