已发表论文

通过加权基因共表达网络分析识别慢性阻塞性肺疾病相关肺气肿的 miRNA 模块和相关通路

 

Authors An J, Yang T, Dong J, Liao Z, Wan C, Shen Y, Chen L 

Received 24 June 2021

Accepted for publication 25 October 2021

Published 15 November 2021 Volume 2021:16 Pages 3119—3130

DOI https://doi.org/10.2147/COPD.S325300

Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 2

Editor who approved publication: Professor Zhang

Background: Chronic obstructive pulmonary disease (COPD) is a heterogeneous chronic inflammatory disease characterized by progressive airflow limitation that causes high morbidity and mortality. MicroRNA, a short-chain noncoding RNA, regulates gene expression at the transcriptional level. microRNA modules with a role in the pathogenesis of COPD may serve as COPD biomarkers.
Methods: We downloaded the GSE33336 microarray data set from the Gene Expression Omnibus (GEO) database, the data are derived from 29 lung samples of patients with emphysema undergoing curative resection for lung cancer. We used weighted gene co-expression network analysis (WGCNA) to construct co-expression modules and detect trait-related microRNA modules. We used the Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis to predict the biological function of the interest modules, and we screened out candidate hub microRNAs based on their module membership (MM) value and top proteins on the results of the protein–protein interaction (PPI) network.
Results: Three microRNA modules (royal blue, light yellow and grey60) were highly associated with COPD. Axon guidance, proteoglycans in cancer and mitogen-activated protein kinases (MAPK) signaling pathway were common pathways in these three modules. Keratin18 (KRT18) was the top protein in our study. miR-452, miR-149, miR-133a, miR-181a and miR-421 in hub microRNAs may play a role in COPD.
Conclusion: These findings provide evidence for the role of miRNAs in COPD and identify biomarker candidates.
Keywords: chronic obstructive pulmonary disease, weighted gene co-expression network analysis, microRNA