Bioinformatics-based identification of potential microRNA biomarkers in frequent and non-frequent exacerbators of COPD

Abstract

Objectives: MicroRNAs (miRNAs) play essential roles in the development of COPD. In this study, we aimed to identify and validate potential miRNA biomarkers in frequent and non-frequent exacerbators of COPD patients using bioinformatic analysis.

Materials and methods: The candidate miRNA biomarkers in COPD were screened from Gene Expression Omnibus (GEO) dataset and identified using GEO2R online tool. Then, we performed bioinformatic analyses including target prediction, gene ontology (GO), pathway enrichment analysis and construction of protein-protein interaction (PPI) network. Furthermore, the expression of the identified miRNAs in peripheral blood monocular cells (PBMCs) of COPD patients was validated using quantitative real-time polymerase chain reaction (qRT-PCR).

Results: MiR-23a, miR-25, miR-145 and miR-224 were identified to be significantly downregulated in COPD patients compared with healthy controls. GO analysis showed the four miRNAs involved in apoptotic, cell differentiation, cell proliferation and innate immune response. Pathway analysis showed that the targets of these miRNAs were associated with p53, TGF-β, Wnt, VEGF and MAPK signal pathway. In healthy controls, the miR-25 and miR-224 levels were significantly decreased in smokers compared with nonsmokers (P<0.001 and P<0.05, respectively). In COPD patients, the levels of miR-23a, miR-25, miR-145 and miR-224 were associated with Global Initiative for Chronic Obstructive Lung Disease (GOLD) stages. Notably, miR-23a and miR-145 were significantly elevated in non-frequent exacerbators compared with frequent exacerbators (P<0.05), and miR-23a showed higher area under the receiver-operator characteristic curve (AUROC) than miR-145 (0.707 vs 0.665, P<0.05).

Conclusion: MiR-23a, miR-25, miR-145 and miR-224 were associated with the development of COPD, and miR-23a might be a potential biomarker for discriminating the frequent exacerbators from non-frequent exacerbators.

Keywords: COPD; Gene Expression Omnibus dataset; Global Initiative for Chronic Obstructive Lung Disease stages; bioinformatic analysis; biomarkers; microRNAs.

Figure 1

The general overview of the study design. Notes: 1, Selection of candidate miRNAs through GEO dataset. 2, Bioinformatic analyses of candidate miRNAs involving target prediction, GO and pathway analyses and PPI network construction. 3, qRT-PCR validation of miRNAs in PBMCs from COPD patients and healthy control subjects. Abbreviations: GEO, Gene Expression Omnibus; GO, gene ontology; miRNAs, microRNAs; PBMCs, peripheral blood monocular cells; PPI, protein–protein interaction; qRT-PCR, quantitative real-time polymerase chain reaction.

Figure 2

Bioinformatic analysis of predicted target genes of candidate miRNAs. Notes: (A) GO BP of targets. (B) KEGG pathways of target genes. (C) Panther pathways of target genes. (D) Venn diagram of 3114 target genes. Abbreviations: BP, biological process; GO, gene ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; miRNAs, microRNAs.

Figure 2

Bioinformatic analysis of predicted target genes of candidate miRNAs. Notes: (A) GO BP of targets. (B) KEGG pathways of target genes. (C) Panther pathways of target genes. (D) Venn diagram of 3114 target genes. Abbreviations: BP, biological process; GO, gene ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; miRNAs, microRNAs.

Figure 3

Interaction of miRNAs and targets and interaction of protein–protein network. Notes: The orange edges represent the interaction of miRNAs and target genes, while the green edges represent the interaction of those proteins. MYC, MDM2, CDH1, ACTB, CTNNB1 and CCND1 are shown as hub target genes according to the counts of interacting protein. Abbreviation: miRNAs, microRNAs.

Figure 4

Smoking status of COPD patients. Notes: FEV₁%pre was negatively correlated with pack-years in COPD patients (P<0.05, r=−0.298). Abbreviation: FEV₁%pre, forced expiratory volume in the first second of expiration for predicted values.

Figure 5

qRT-PCR validation of four candidate miRNAs in PBMCs of participants. Notes: (A) Relative levels of miR-23a, miR-25, miR-145 and miR-224 among different groups (NS-C, healthy nonsmokers; S-C, healthy smokers; NF-AE, non-frequent acute exacerbators; F-AE, frequent acute exacerbators). (B) Relative levels of these four miRNAs in patients with different GOLD stages (I, II: GOLD I and GOLD II stages; III: GOLD III stage; IV: GOLD IV stage). *P<0.05; **P<0.01; ***P<0.001. Abbreviations: GOLD, Global Initiative for Chronic Obstructive Lung Disease; miRNAs, microRNAs; PBMCs, peripheral blood monocular cells; qRT-PCR, quantitative real-time polymerase chain reaction.

Figure 6

ROC curve of miRNA-23a and miRNA-145 in COPD patients. Notes: ROC curve of miRNA-23a and miRNA-145 between non-frequent and frequent exacerbators of COPD patients. miR-23a: AUC 0.707 (95% CI: 0.543–0.880), P=0.016; miR-145: 0.665 (95% CI: 0.5123–0.8175), P=0.066. Abbreviations: ROC, receiver–operator characteristic; miRNAs, microRNAs; AUC, area under the curve.