Abstract
Coronavirus disease 2019 (COVID-19) poses a serious threat to human health and life. The effective prevention and treatment of COVID-19 complications have become crucial to saving patients’ lives. During the phase of mass spread of the epidemic, a large number of patients with pulmonary fibrosis and lung cancers were inevitably infected with the SARS-CoV-2 virus. Lung cancers have the highest tumor morbidity and mortality rates worldwide, and pulmonary fibrosis itself is one of the complications of COVID-19. Idiopathic lung fibrosis (IPF) and various lung cancers (primary and metastatic) become risk factors for complications of COVID-19 and significantly increase mortality in patients. Therefore, we applied bioinformatics and systems biology approaches to identify molecular biomarkers and common pathways in COVID-19, IPF, colorectal cancer (CRC) lung metastasis, SCLC and NSCLC. We identified 79 DEGs between COVID-19, IPF, CRC lung metastasis, SCLC and NSCLC. Meanwhile, based on the transcriptome features of DSigDB and common DEGs, we identified 10 drug candidates. In this study, 79 DEGs are the common core genes of the 5 diseases. The 10 drugs were found to have positive effects in treating COVID-19 and lung cancer, potentially reducing the risk of pulmonary fibrosis.
【초록키워드】 COVID-19, Treatment, Transcriptome, Coronavirus disease 2019, Biomarker, Diseases, Mortality, Cancer, pulmonary fibrosis, bioinformatics, lung, risk, Systems biology, drug, lung cancer, risk factor, Spread, Colorectal cancer, Health, Patient, pathway, Lung fibrosis, morbidity and mortality, molecular, patients, IPF, Metastasis, drug candidates, DEGs, treating COVID-19, positive, complications of COVID-19, idiopathic, Effect, feature, effective, the epidemic, highest, identify, approach, significantly, applied, reducing, core gene, COVID-19 complication, the SARS-CoV-2 virus, 【제목키워드】 COVID-19, pulmonary fibrosis, lung, Patient, Bioinformatics analysis, identification, literature review, Effect,