Identification of Dysregulated Mechanisms and Candidate Gene Markers in Chronic Obstructive Pulmonary Disease

• Published in: International journal of chronic obstructive pulmonary disease Vol. 17; pp. 475 - 487
• Main Authors: Lin, Jie, Xue, Yanlong, Su, Wenyan, Zhang, Zan, Wei, Qiu, Huang, Tianxia
• Format: Journal Article
• Language: English
• Published: New Zealand Dove Medical Press Limited 01.01.2022; Dove Medical Press Ltd; Dove; Dove Medical Press
• Subjects: Analysis; Asthma; bioinformatics analysis; Cardiovascular disease; Chronic obstructive pulmonary disease; Computational Biology; Datasets; DNA methylation; Dyspnea; feature genes; Gene expression; Gene Expression Profiling; Gene Regulatory Networks; Genes; Genetic markers; Genomes; Humans; immune response; Inflammation; Lung diseases, Obstructive; Medical research; Medicine, Experimental; Methylation; MicroRNA; MicroRNAs; MicroRNAs - genetics; mirnas; Ontology; Original Research; Pulmonary Disease, Chronic Obstructive - diagnosis; Pulmonary Disease, Chronic Obstructive - genetics; China; miRNAs; immune response; bioinformatics analysis; feature genes; chronic obstructive pulmonary disease
• ISSN: 1178-2005; 1176-9106; 1178-2005
• DOI: 10.2147/COPD.S349694

This study aimed to identify candidate gene markers that may facilitate chronic obstructive pulmonary disease (COPD) diagnosis and treatment. The GSE47460 and GSE151052 datasets were analyzed to identify differentially expressed mRNAs (DEmRs) between COPD patients and controls. DEmRs that were differentially expressed in the same direction in both datasets were analyzed for functional enrichment and for coexpression. Genes from the largest three modules were tested for their ability to diagnose COPD based on the area under the receiver operating characteristic curve (AUC). Genes with AUC > 0.7 in both datasets were used to perform regression based on the "least absolute shrinkage and selection operator" in order to identify feature genes. We also identified differentially expressed miRNAs (DEmiRs) between COPD patients and controls using the GSE38974 dataset, then constructed a regulatory network. We also examined associations between feature genes and immune cell infiltration in COPD, and we identified methylation markers of COPD using the GSE63704 dataset. A total of 1350 genes differentially regulated in the same direction in the GSE47460 and GSE151052 datasets were found. The genes were significantly enriched in immune-related biological functions. Of 186 modules identified using MEGENA, the largest were C1_ 6, C1_ 3, and C1_ 2. Of the 22 candidate genes screened based on AUC, 11 feature genes emerged from analysis of a subset of GSE47460 data, which we validated using another subset of GSE47460 data as well as the independent GSE151052 dataset. Feature genes correlated significantly with infiltration by immune cells. The feature genes GPC4 and RS1 were predicted to be regulated by miR-374a-3p. We identified 117 candidate methylation markers of COPD, including PRRG4. The feature genes we identified may be potential diagnostic markers and therapeutic targets in COPD. These findings provide new leads for exploring disease mechanisms and targeted treatments.