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Bioinformatics Approach Predicts Candidate Targets for SARS-CoV-2 Infections to COPD Patients
COVID-19 is still prevalent in more world regions and poses a severe threat to human health due to its high pathogenicity. The incidence of COPD patients is gradually increasing, especially in patients over 45 years old. COPD patients are susceptible to COVID-19 due to the specific lung receptor ACE...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9211381/ https://www.ncbi.nlm.nih.gov/pubmed/35747501 http://dx.doi.org/10.1155/2022/1806427 |
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author | Che, Li Chen, Guangshu Cai, Xingdong Xie, Zhefan Xia, Tingting Zhang, Wei Liu, Shengming |
author_facet | Che, Li Chen, Guangshu Cai, Xingdong Xie, Zhefan Xia, Tingting Zhang, Wei Liu, Shengming |
author_sort | Che, Li |
collection | PubMed |
description | COVID-19 is still prevalent in more world regions and poses a severe threat to human health due to its high pathogenicity. The incidence of COPD patients is gradually increasing, especially in patients over 45 years old. COPD patients are susceptible to COVID-19 due to the specific lung receptor ACE2 of SARS-CoV-2. We attempt to reveal the genetic basis by analyzing the expression of common DEGs of the two diseases through bioinformatics approaches and find potential therapeutic agents based on the target genes. Thus, we search the GEO database for COVID-19 and COPD transcriptomic gene expression. We also study the enrichment of signaling regulatory pathways and hub genes for potential therapeutic treatments. There are 34 common DEGs in the two datasets. The signaling pathways are mainly enriched in intercellular junctions between virus and cytokine regulation. In the PPI network of common DEGs, we extract 5 hub genes. We find that artesunate CTD 00001840, dexverapamil MCF7 UP, and STOCK1N-35696 PC3 DOWN could be therapeutic agents for both diseases. We also analyze the regulatory network of differential genes with transcription factors and miRNAs. Therefore, we conclude that artesunate CTD 00001840, dexverapamil MCF7 UP, and STOCK1N-35696 PC3 DOWN can be therapeutic candidates in COPD combined with COVID-19. |
format | Online Article Text |
id | pubmed-9211381 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-92113812022-06-22 Bioinformatics Approach Predicts Candidate Targets for SARS-CoV-2 Infections to COPD Patients Che, Li Chen, Guangshu Cai, Xingdong Xie, Zhefan Xia, Tingting Zhang, Wei Liu, Shengming Biomed Res Int Research Article COVID-19 is still prevalent in more world regions and poses a severe threat to human health due to its high pathogenicity. The incidence of COPD patients is gradually increasing, especially in patients over 45 years old. COPD patients are susceptible to COVID-19 due to the specific lung receptor ACE2 of SARS-CoV-2. We attempt to reveal the genetic basis by analyzing the expression of common DEGs of the two diseases through bioinformatics approaches and find potential therapeutic agents based on the target genes. Thus, we search the GEO database for COVID-19 and COPD transcriptomic gene expression. We also study the enrichment of signaling regulatory pathways and hub genes for potential therapeutic treatments. There are 34 common DEGs in the two datasets. The signaling pathways are mainly enriched in intercellular junctions between virus and cytokine regulation. In the PPI network of common DEGs, we extract 5 hub genes. We find that artesunate CTD 00001840, dexverapamil MCF7 UP, and STOCK1N-35696 PC3 DOWN could be therapeutic agents for both diseases. We also analyze the regulatory network of differential genes with transcription factors and miRNAs. Therefore, we conclude that artesunate CTD 00001840, dexverapamil MCF7 UP, and STOCK1N-35696 PC3 DOWN can be therapeutic candidates in COPD combined with COVID-19. Hindawi 2022-06-21 /pmc/articles/PMC9211381/ /pubmed/35747501 http://dx.doi.org/10.1155/2022/1806427 Text en Copyright © 2022 Li Che et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Che, Li Chen, Guangshu Cai, Xingdong Xie, Zhefan Xia, Tingting Zhang, Wei Liu, Shengming Bioinformatics Approach Predicts Candidate Targets for SARS-CoV-2 Infections to COPD Patients |
title | Bioinformatics Approach Predicts Candidate Targets for SARS-CoV-2 Infections to COPD Patients |
title_full | Bioinformatics Approach Predicts Candidate Targets for SARS-CoV-2 Infections to COPD Patients |
title_fullStr | Bioinformatics Approach Predicts Candidate Targets for SARS-CoV-2 Infections to COPD Patients |
title_full_unstemmed | Bioinformatics Approach Predicts Candidate Targets for SARS-CoV-2 Infections to COPD Patients |
title_short | Bioinformatics Approach Predicts Candidate Targets for SARS-CoV-2 Infections to COPD Patients |
title_sort | bioinformatics approach predicts candidate targets for sars-cov-2 infections to copd patients |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9211381/ https://www.ncbi.nlm.nih.gov/pubmed/35747501 http://dx.doi.org/10.1155/2022/1806427 |
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