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Identification of key molecules in COVID-19 patients significantly correlated with clinical outcomes by analyzing transcriptomic data

BACKGROUND: Although several key molecules have been identified to modulate SARS-CoV-2 invasion of human host cells, the molecules correlated with outcomes in COVID-19 caused by SARS-CoV-2 infection remain insufficiently explored. METHODS: This study analyzed three RNA-Seq gene expression profiling...

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Autores principales: Dong, Zehua, Yan, Qiyu, Cao, Wenxiu, Liu, Zhixian, Wang, Xiaosheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9441550/
https://www.ncbi.nlm.nih.gov/pubmed/36072597
http://dx.doi.org/10.3389/fimmu.2022.930866
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author Dong, Zehua
Yan, Qiyu
Cao, Wenxiu
Liu, Zhixian
Wang, Xiaosheng
author_facet Dong, Zehua
Yan, Qiyu
Cao, Wenxiu
Liu, Zhixian
Wang, Xiaosheng
author_sort Dong, Zehua
collection PubMed
description BACKGROUND: Although several key molecules have been identified to modulate SARS-CoV-2 invasion of human host cells, the molecules correlated with outcomes in COVID-19 caused by SARS-CoV-2 infection remain insufficiently explored. METHODS: This study analyzed three RNA-Seq gene expression profiling datasets for COVID-19 and identified differentially expressed genes (DEGs) between COVID-19 patients and normal people, commonly in the three datasets. Furthermore, this study explored the correlation between the expression of these genes and clinical features in COVID-19 patients. RESULTS: This analysis identified 13 genes significantly upregulated in COVID-19 patients’ leukocyte and SARS-CoV-2-infected nasopharyngeal tissue compared to normal tissue. These genes included OAS1, OAS2, OAS3, OASL, HERC6, SERPING1, IFI6, IFI44, IFI44L, CMPK2, RSAD2, EPSTI1, and CXCL10, all of which are involved in antiviral immune regulation. We found that these genes’ downregulation was associated with worse clinical outcomes in COVID-19 patients, such as intensive care unit (ICU) admission, mechanical ventilatory support (MVS) requirement, elevated D-dimer levels, and increased viral loads. Furthermore, this analysis identified two COVID-19 clusters based on the expression profiles of the 13 genes, termed COV-C1 and COV-C2. Compared with COV-C1, COV-C2 more highly expressed the 13 genes, had stronger antiviral immune responses, were younger, and displayed more favorable clinical outcomes. CONCLUSIONS: A strong antiviral immune response is essential in reducing severity of COVID-19.
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spelling pubmed-94415502022-09-06 Identification of key molecules in COVID-19 patients significantly correlated with clinical outcomes by analyzing transcriptomic data Dong, Zehua Yan, Qiyu Cao, Wenxiu Liu, Zhixian Wang, Xiaosheng Front Immunol Immunology BACKGROUND: Although several key molecules have been identified to modulate SARS-CoV-2 invasion of human host cells, the molecules correlated with outcomes in COVID-19 caused by SARS-CoV-2 infection remain insufficiently explored. METHODS: This study analyzed three RNA-Seq gene expression profiling datasets for COVID-19 and identified differentially expressed genes (DEGs) between COVID-19 patients and normal people, commonly in the three datasets. Furthermore, this study explored the correlation between the expression of these genes and clinical features in COVID-19 patients. RESULTS: This analysis identified 13 genes significantly upregulated in COVID-19 patients’ leukocyte and SARS-CoV-2-infected nasopharyngeal tissue compared to normal tissue. These genes included OAS1, OAS2, OAS3, OASL, HERC6, SERPING1, IFI6, IFI44, IFI44L, CMPK2, RSAD2, EPSTI1, and CXCL10, all of which are involved in antiviral immune regulation. We found that these genes’ downregulation was associated with worse clinical outcomes in COVID-19 patients, such as intensive care unit (ICU) admission, mechanical ventilatory support (MVS) requirement, elevated D-dimer levels, and increased viral loads. Furthermore, this analysis identified two COVID-19 clusters based on the expression profiles of the 13 genes, termed COV-C1 and COV-C2. Compared with COV-C1, COV-C2 more highly expressed the 13 genes, had stronger antiviral immune responses, were younger, and displayed more favorable clinical outcomes. CONCLUSIONS: A strong antiviral immune response is essential in reducing severity of COVID-19. Frontiers Media S.A. 2022-08-22 /pmc/articles/PMC9441550/ /pubmed/36072597 http://dx.doi.org/10.3389/fimmu.2022.930866 Text en Copyright © 2022 Dong, Yan, Cao, Liu and Wang https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Immunology
Dong, Zehua
Yan, Qiyu
Cao, Wenxiu
Liu, Zhixian
Wang, Xiaosheng
Identification of key molecules in COVID-19 patients significantly correlated with clinical outcomes by analyzing transcriptomic data
title Identification of key molecules in COVID-19 patients significantly correlated with clinical outcomes by analyzing transcriptomic data
title_full Identification of key molecules in COVID-19 patients significantly correlated with clinical outcomes by analyzing transcriptomic data
title_fullStr Identification of key molecules in COVID-19 patients significantly correlated with clinical outcomes by analyzing transcriptomic data
title_full_unstemmed Identification of key molecules in COVID-19 patients significantly correlated with clinical outcomes by analyzing transcriptomic data
title_short Identification of key molecules in COVID-19 patients significantly correlated with clinical outcomes by analyzing transcriptomic data
title_sort identification of key molecules in covid-19 patients significantly correlated with clinical outcomes by analyzing transcriptomic data
topic Immunology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9441550/
https://www.ncbi.nlm.nih.gov/pubmed/36072597
http://dx.doi.org/10.3389/fimmu.2022.930866
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