PURPOSE

Chronic obstructive pulmonary disease (COPD) is the third leading cause of death worldwide, characterized by intense lung infiltrations of immune cells (macrophages and monocytes). While existing studies have highlighted the crucial role of the competitive endogenous RNA (ceRNA) regulatory network in COPD development, the complexity and characteristics of the ceRNA network in monocytes remain unexplored.

METHODS

We downloaded messenger RNA (mRNA), microRNA (miRNA), and long noncoding RNA (lncRNA) microarray data from GSE146560, GSE102915, and GSE71220 in the Gene Expression Omnibus (GEO) database. This data was used to identify differentially expressed mRNAs (DEmRNAs), miRNAs (DEmiRNAs), and lncRNAs (DElncRNAs). Predicted miRNAs that bind to DElncRNAs were intersected with DEmiRNAs, forming a set of intersecting miRNAs. This set was then used to predict potential binding mRNAs, intersected with DEmRNAs, and underwent functional enrichment analysis using R software and the STRING database. The resulting triple regulatory network and hub genes were constructed using Cytoscape. Comparative Toxicomics Database (CTD) was utilized for disease correlation predictions, and ROC curve analysis assessed diagnostic accuracy.

RESULTS

Our study identified 5 lncRNAs, 4 miRNAs, and 149 mRNAs as differentially expressed. A lncRNA-miRNA-mRNA regulatory network was constructed, and hub genes were selected through hub analysis. Enrichment analysis highlighted terms related to cell movement and gene expression regulation. We established a LINC00482-has-miR-6088-PRRC2B ceRNA network with diagnostic relevance for COPD. ROC analysis demonstrated the diagnostic value of these genes. Moreover, a positive correlation between LINC00482 and PRRC2B expression was observed in COPD PBMCs. The CTD database indicated their involvement in inflammatory responses.

CONCLUSION

In summary, our study not only identified pivotal hub genes in peripheral blood mononuclear cells (PBMCs) of COPD but also constructed a ceRNA regulatory network. This contributes to understanding the pathophysiological processes of COPD through bioinformatics analysis, expanding our knowledge of COPD, and providing a foundation for potential diagnostic and therapeutic targets for COPD.