New insight into genes in association with asthma： literature-based mining and network centrality analysis

• Published in: Chinese medical journal 2013-07, Vol.126 (13), p.2472-2479
• Main Authors: Liang, Rui, Wang, Lei, Wang, Gang
• Format: Article
• Language: English
• Subjects: Asthma - genetics; Data Mining; Gene Ontology; Gene Regulatory Networks; Genetic Predisposition to Disease; Humans; JAK-STAT; PubMed; Signal Transduction; 信号转导通路; 哮喘; 挖掘; 文献; 易感基因; 细胞因子受体; 网络中心
• ISSN: 0366-6999; 2542-5641
• DOI: 10.3760/cma.j.issn.0366-6999.20122846

Background Asthma is a heterogeneous disease for which a strong genetic basis has been firmly established. Until now no studies have been undertaken to systemically explore the network of asthma-related genes using an internally developed literature-based discovery approach. This study was to explore asthma-related genes by using literature- based mining and network centrality analysis. Methods Literature involving asthma-related genes were searched in PubMed from 2001 to 2011. Integration of natural language processing with network centrality analysis was used to identify asthma susceptibility genes and their interaction network. Asthma susceptibility genes were classified into three functional groups by gene ontology （GO） analysis and the key genes were confirmed by establishing asthma-related networks and pathways. Results Three hundred and twenty-six genes related with asthma such as IGHE （IgE）, interleukin （IL）-4, 5, 6, 10, 13, 17A, and tumor necrosis factor （TNF）-alpha were identified. GO analysis indicated some biological processes （developmental processes, signal transduction, death, etc.）, cellular components （non-structural extracellular, plasma membrane and extracellular matrix）, and molecular functions （signal transduction activity） that were involved in asthma. Furthermore, 22 asthma-related pathways such as the Toll-like receptor signaling pathway, hematopoietic cell lineage, JAK-STAT signaling pathway, chemokine signaling pathway, and cytokine-cytokine receptor interaction, and 17 hub genes, such as JAK3, CCR1-3, CCR5-7, CCR8, were found. Conclusions Our study provides a remarkably detailed and comprehensive picture of asthma susceptibility genes and their interacting network. Further identification of these genes and molecular pathways may play a prominent role in establishing rational therapeutic approaches for asthma.