Key genes and co-expression modules involved in asthma pathogenesis

Yuyi Huang; Hui Liu; Li Zuo; Ailin Tao

doi:10.7717/peerj.8456

Key genes and co-expression modules involved in asthma pathogenesis

PeerJ. 2020 Feb 3:8:e8456. doi: 10.7717/peerj.8456. eCollection 2020.

Authors

Yuyi Huang^#¹, Hui Liu^#^{1

2}, Li Zuo^{3

4}, Ailin Tao¹

Affiliations

¹ 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.
² School of Basic Medical Sciences, The Sixth Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
³ The Interdisciplinary Biophysics Graduate Program, The Ohio State University, Columbus, OH, USA.
⁴ College of Arts and Sciences, University of Maine Presque Isle Campus, Presque Isle, ME, USA.

^# Contributed equally.

Abstract

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.

Keywords: Asthma; Endocyte; Machine learning; Pathology; WGCNA.

Grants and funding

This work was supported by grants from the National Science and Technology Major Project of China (2016ZX08011-005), the Guangzhou Science and Technology Project (201604020008, 201804020042), the startup foundation of Guangzhou Medical University (B185006002003), and the Sixth Affiliated Hospital of Guangzhou Medical University grant (No. 1014155). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.