Key genes and co-expression modules involved in asthma pathogenesis
Yuyi Huang,
Hui Liu,
Li Zuo,
Ailin Tao
Affiliations
Yuyi Huang
The State Key Laboratory of Respiratory Disease, Guangdong Provincial Key Laboratory of Allergy & Clinical Immunology, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
Hui Liu
The State Key Laboratory of Respiratory Disease, Guangdong Provincial Key Laboratory of Allergy & Clinical Immunology, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
Li Zuo
The Interdisciplinary Biophysics Graduate Program, The Ohio State University, Columbus, OH, USA
Ailin Tao
The State Key Laboratory of Respiratory Disease, Guangdong Provincial Key Laboratory of Allergy & Clinical Immunology, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
Machine learning and weighted gene co-expression network analysis (WGCNA) have been widely used due to its well-known accuracy in the biological field. However, due to the nature of a gene’s multiple functions, it is challenging to locate the exact genes involved in complex diseases such as asthma. In this study, we combined machine learning and WGCNA in order to analyze the gene expression data of asthma for better understanding of associated pathogenesis. Specifically, the role of machine learning is assigned to screen out the key genes in the asthma development, while the role of WGCNA is to set up gene co-expression network. Our results indicated that hormone secretion regulation, airway remodeling, and negative immune regulation, were all regulated by critical gene modules associated with pathogenesis of asthma progression. Overall, the method employed in this study helped identify key genes in asthma and their roles in the asthma pathogenesis.
