Abstract
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), better known as COVID-19, has become a current threat to humanity. The second wave of the SARS-CoV-2 virus has hit many countries, and the confirmed COVID-19 cases are quickly spreading. Therefore, the epidemic is still passing the terrible stage. Having idiopathic pulmonary fibrosis (IPF) and chronic obstructive pulmonary disease (COPD) are the risk factors of the COVID-19, but the molecular mechanisms that underlie IPF, COPD, and CVOID-19 are not well understood. Therefore, we implemented transcriptomic analysis to detect common pathways and molecular biomarkers in IPF, COPD, and COVID-19 that help understand the linkage of SARS-CoV-2 to the IPF and COPD patients. Here, three RNA-seq datasets (GSE147507, GSE52463, and GSE57148) from Gene Expression Omnibus (GEO) is employed to detect mutual differentially expressed genes (DEGs) for IPF, and COPD patients with the COVID-19 infection for finding shared pathways and candidate drugs. A total of 65 common DEGs among these three datasets were identified. Various combinatorial statistical methods and bioinformatics tools were used to build the protein-protein interaction (PPI) and then identified Hub genes and essential modules from this PPI network. Moreover, we performed functional analysis under ontologies terms and pathway analysis and found that IPF and COPD have some shared links to the progression of COVID-19 infection. Transcription factors-genes interaction, protein-drug interactions, and DEGs-miRNAs coregulatory network with common DEGs also identified on the datasets. We think that the candidate drugs obtained by this study might be helpful for effective therapeutic in COVID-19.
Keywords: SARS-CoV-2; chronic obstructive pulmonary disease; differentially expressed genes; drug molecule; gene ontology; hub gene; idiopathic pulmonary fibrosis; protein–protein interaction (PPI).
【저자키워드】 SARS-CoV-2, Gene ontology, Idiopathic pulmonary fibrosis, Differentially expressed genes, drug molecule, hub gene, chronic obstructive pulmonary disease, protein–protein interaction (PPI)., 【초록키워드】 COVID-19, coronavirus, Biomarker, Risk factors, pulmonary fibrosis, bioinformatics, hub genes, fibrosis, SARS-CoV-2 virus, Gene ontology, progression, molecular mechanism, risk factor, severe acute respiratory syndrome Coronavirus, Pathway analysis, Idiopathic pulmonary fibrosis, COPD, COVID-19 infection, therapeutic, Patient, Ontology, pathway, second wave, dataset, transcriptomic analysis, molecular, Differentially expressed genes, patients, protein-protein interaction, interactions, differentially expressed gene, pulmonary disease, Protein–protein interaction, IPF, Interaction, linkage, Analysis, PPI, chronic obstructive pulmonary disease, obstructive pulmonary disease, transcription factors, acute respiratory syndrome, acute respiratory syndrome coronavirus, acute respiratory syndrome coronavirus 2, molecular mechanisms, help, candidate drugs, Gene Expression Omnibus, DEGs, PPI network, datasets, shared pathways, confirmed COVID-19 case, GSE147507, effective, statistical, the epidemic, RNA-seq dataset, performed, detect, functional, were used, differentially expressed, underlie, chronic obstructive, build, candidate drug, GEO, shared pathway, the SARS-CoV-2 virus, 【제목키워드】 SARS-COV-2 infection, pulmonary fibrosis, Idiopathic pulmonary fibrosis, Patient, pulmonary disease, System biology, approach, identify, influence, chronic obstructive,