Abstract
Purpose
Cellular senescence participates in the occurrence and development of chronic obstructive pulmonary disease (COPD). This study aimed to identify senescence-related hub genes and explore effective diagnostic markers and therapeutic targets for COPD.
Methods
The microarray data from the GSE38974 dataset was downloaded from the Gene Expression Omnibus (GEO) database. The overlapping genes between genes from the GSE38974 dataset and CellAge database were considered differentially expressed senescence-related genes (DESRGs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using R software. Protein-protein interaction (PPI), miRNA-mRNA network, and competitive endogenous RNA (ceRNA) network were constructed and visualized by Cytoscape software. GSE100281 and GSE103174 datasets were employed to validate the expression and diagnostic value of hub genes. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was used to measure the mRNA levels of hub genes in peripheral blood mononuclear cells (PBMCs) from COPD and control samples.
Results
A total of 23 DESRGs were identified between COPD samples and healthy controls. Enrichment analysis revealed that DESRGs were mainly related to apoptosis and senescence. Moreover, four hub genes and two key clusters were acquired by Cytohubba and MCODE plugin, respectively. CDKN1A and HDAC1 were verified as final hub genes based on GSE100281 and GSE103174 datasets validation. The mRNA expression level of CDKN1A was negatively related to forced expiratory volume in 1 second/forced vital capacity (FEV1/FVC), and HDAC1 expression had the opposite correlation. Finally, an HDAC1-based ceRNA network, including 6 miRNAs and 11 lncRNAs, was constructed.
Conclusion
We identified two senescence-related hub genes, CDKN1A and HDAC1, which may be effective biomarkers for COPD diagnosis and treatment. An HDAC1-related ceRNA network was constructed to clarify the role of senescence in COPD pathogenesis.
Abbreviations
BMSC, bone marrow mesenchymal stem cells; COPD, chronic obstructive pulmonary disease; ceRNA, competitive endogenous RNA; GOLD, global initiative for chronic obstructive lung disease; CSE, cigarette smoke extract; DESRGs, differentially expressed senescence-related genes; ENCORI, encyclopedia of RNA interactomes; EPCs, endothelial progenitor cells; FEV1, forced expiratory volume in 1 second; FVC, forced vital capacity; GEO, gene expression omnibus; GO, gene ontology; HBECs, human bronchial epithelial cells; HDACs, histone deacetylases; HBECs, human bronchial epithelial cells; HPH, hypoxia-induced pulmonary hypertension; ICSs, inhaled corticosteroids; ILD, interstitial lung disease; KEGG, Kyoto encyclopedia of genes and genomes; MMPs, matrix metalloproteinases; PBMCs, peripheral blood mononuclear cells; PCA, principal component analysis; PPI, protein-protein interaction; RT-qPCR, reverse transcription-quantitative polymerase chain reaction; SASP, senescence-associated secretory phenotype; SRGs, senescence-related genes.
Data Sharing Statement
The datasets analyzed in this paper can be found in the GEO database. Senescence-related genes are downloaded from the CellAge database.
Ethics Approval
The trial was conducted according to the Declaration of Helsinki (as revised in 2013). The experimental procedure was approved by the Medical Ethics Committee of Qilu Hospital of Shandong University. All participants signed informed consent forms prior to study.
Author Contributions
All authors made a significant contribution to the work reported, whether it was in the conception, study design, execution, acquisition of data, analysis and interpretation, or all of these; took part in drafting, revising, or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.
Disclosure
The authors report no conflicts of interest in this work.