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Mining the key genes for ventilator-induced lung injury using co-expression network analysis

Mechanical ventilation is extensively adopted in general anesthesia and respiratory failure management, but it can also induce ventilator-induced lung injury (VILI). Therefore, it is of great urgency to explore the mechanisms involved in the VILI pathogenesis, which might contribute to its future pr...

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Autores principales: Li, Zhao, Xiao, Yajun, Xu, Li, Wang, Qingxiu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Portland Press Ltd. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7969703/
https://www.ncbi.nlm.nih.gov/pubmed/33687057
http://dx.doi.org/10.1042/BSR20203235
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author Li, Zhao
Xiao, Yajun
Xu, Li
Wang, Qingxiu
author_facet Li, Zhao
Xiao, Yajun
Xu, Li
Wang, Qingxiu
author_sort Li, Zhao
collection PubMed
description Mechanical ventilation is extensively adopted in general anesthesia and respiratory failure management, but it can also induce ventilator-induced lung injury (VILI). Therefore, it is of great urgency to explore the mechanisms involved in the VILI pathogenesis, which might contribute to its future prevention and treatment. Four microarray datasets from the GEO database were selected in our investigation, and were subjected to the Weighted Gene Co-Expression Network Analysis (WGCNA) to identify the VILI-correlated gene modules. The limma package in R software was used to identify the differentially expressed genes (DEGs) between the VILI and control groups. WGCNA was constructed by merging the GSE9314, GSE9368, GSE11434 and GSE11662 datasets. A total of 49 co-expression network modules were determined as associated with VILI. The intersected genes between hub genes screened from DEGs for VILI and those identified using WGCNA were as follows: Tlr2, Hmox1, Serpine1, Mmp9, Il6, Il1b, Ptgs2, Fos and Atf3, which were determined to be key genes for VILI. Those key genes were validated by GSE86229 and quantitative PCR (qPCR) experiment to have significantly statistical difference in their expression between the VILI and control groups. In a nutshell, nine key genes with expression differences in VILI were screened by WGCNA by integrating multiple datasets.
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spelling pubmed-79697032021-03-31 Mining the key genes for ventilator-induced lung injury using co-expression network analysis Li, Zhao Xiao, Yajun Xu, Li Wang, Qingxiu Biosci Rep Bioinformatics Mechanical ventilation is extensively adopted in general anesthesia and respiratory failure management, but it can also induce ventilator-induced lung injury (VILI). Therefore, it is of great urgency to explore the mechanisms involved in the VILI pathogenesis, which might contribute to its future prevention and treatment. Four microarray datasets from the GEO database were selected in our investigation, and were subjected to the Weighted Gene Co-Expression Network Analysis (WGCNA) to identify the VILI-correlated gene modules. The limma package in R software was used to identify the differentially expressed genes (DEGs) between the VILI and control groups. WGCNA was constructed by merging the GSE9314, GSE9368, GSE11434 and GSE11662 datasets. A total of 49 co-expression network modules were determined as associated with VILI. The intersected genes between hub genes screened from DEGs for VILI and those identified using WGCNA were as follows: Tlr2, Hmox1, Serpine1, Mmp9, Il6, Il1b, Ptgs2, Fos and Atf3, which were determined to be key genes for VILI. Those key genes were validated by GSE86229 and quantitative PCR (qPCR) experiment to have significantly statistical difference in their expression between the VILI and control groups. In a nutshell, nine key genes with expression differences in VILI were screened by WGCNA by integrating multiple datasets. Portland Press Ltd. 2021-03-17 /pmc/articles/PMC7969703/ /pubmed/33687057 http://dx.doi.org/10.1042/BSR20203235 Text en © 2021 The Author(s). https://creativecommons.org/licenses/by/4.0/ This is an open access article published by Portland Press Limited on behalf of the Biochemical Society and distributed under the Creative Commons Attribution License 4.0 (CC BY) (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Bioinformatics
Li, Zhao
Xiao, Yajun
Xu, Li
Wang, Qingxiu
Mining the key genes for ventilator-induced lung injury using co-expression network analysis
title Mining the key genes for ventilator-induced lung injury using co-expression network analysis
title_full Mining the key genes for ventilator-induced lung injury using co-expression network analysis
title_fullStr Mining the key genes for ventilator-induced lung injury using co-expression network analysis
title_full_unstemmed Mining the key genes for ventilator-induced lung injury using co-expression network analysis
title_short Mining the key genes for ventilator-induced lung injury using co-expression network analysis
title_sort mining the key genes for ventilator-induced lung injury using co-expression network analysis
topic Bioinformatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7969703/
https://www.ncbi.nlm.nih.gov/pubmed/33687057
http://dx.doi.org/10.1042/BSR20203235
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