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Screening and identification of key gene in sepsis development: Evidence from bioinformatics analysis
Sepsis is one of the leading causes of mortality in intensive care units (ICU). The growing incidence rate of sepsis and its high mortality rate result are very important sociosanitary problems. Sepsis is a result of infection which can cause systemic inflammatory and organ failure. But the pathogen...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Wolters Kluwer Health
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7337576/ https://www.ncbi.nlm.nih.gov/pubmed/32629654 http://dx.doi.org/10.1097/MD.0000000000020759 |
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author | Fu, Qinghui Yu, Wenqiao Fu, Shuiqiao Chen, Enjiang Zhang, Shaoyang Liang, Ting-bo |
author_facet | Fu, Qinghui Yu, Wenqiao Fu, Shuiqiao Chen, Enjiang Zhang, Shaoyang Liang, Ting-bo |
author_sort | Fu, Qinghui |
collection | PubMed |
description | Sepsis is one of the leading causes of mortality in intensive care units (ICU). The growing incidence rate of sepsis and its high mortality rate result are very important sociosanitary problems. Sepsis is a result of infection which can cause systemic inflammatory and organ failure. But the pathogenesis and the molecular mechanisms of sepsis is still not well understood. The aim of the present study was to identify the candidate key genes in the progression of sepsis. Microarray datasets GSE28750, GSE64457, and GSE95233 were downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified, and function enrichment analyses were performed. The protein–protein interaction network (PPI) was constructed and the module analysis was performed using STRING and Cytoscape. Furthermore, to verify the results of the bioinformatics analyses, the expression levels of selected DEGs were quantified by Reverse Transcription-Polymerase Chain Reaction (RT-PCR) in libobolysaccharide (LPS)-induced Human Umbilical Vein Endothelial Cells (HUVECs) to support the result of bioinformatics analysis. Thirteen hub genes were identified and biological process analysis revealed that these genes were mainly enriched in apoptotic process, inflammatory response, innate immune response. Hub genes with high degrees, including MAPK14, SLC2A3, STOM, and MMP8, were demonstrated to have an association with sepsis. Furthermore, RT-PCR results showed that SLC2A3 and MAPK14 were significantly upregulated in the HUVECs induced by LPS compared with controls. In conclusion, DEGs and hub genes identified in the present study help us understand the molecular mechanisms of sepsis, and provide candidate targets for diagnosis and treatment of sepsis. |
format | Online Article Text |
id | pubmed-7337576 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Wolters Kluwer Health |
record_format | MEDLINE/PubMed |
spelling | pubmed-73375762020-07-14 Screening and identification of key gene in sepsis development: Evidence from bioinformatics analysis Fu, Qinghui Yu, Wenqiao Fu, Shuiqiao Chen, Enjiang Zhang, Shaoyang Liang, Ting-bo Medicine (Baltimore) 3900 Sepsis is one of the leading causes of mortality in intensive care units (ICU). The growing incidence rate of sepsis and its high mortality rate result are very important sociosanitary problems. Sepsis is a result of infection which can cause systemic inflammatory and organ failure. But the pathogenesis and the molecular mechanisms of sepsis is still not well understood. The aim of the present study was to identify the candidate key genes in the progression of sepsis. Microarray datasets GSE28750, GSE64457, and GSE95233 were downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified, and function enrichment analyses were performed. The protein–protein interaction network (PPI) was constructed and the module analysis was performed using STRING and Cytoscape. Furthermore, to verify the results of the bioinformatics analyses, the expression levels of selected DEGs were quantified by Reverse Transcription-Polymerase Chain Reaction (RT-PCR) in libobolysaccharide (LPS)-induced Human Umbilical Vein Endothelial Cells (HUVECs) to support the result of bioinformatics analysis. Thirteen hub genes were identified and biological process analysis revealed that these genes were mainly enriched in apoptotic process, inflammatory response, innate immune response. Hub genes with high degrees, including MAPK14, SLC2A3, STOM, and MMP8, were demonstrated to have an association with sepsis. Furthermore, RT-PCR results showed that SLC2A3 and MAPK14 were significantly upregulated in the HUVECs induced by LPS compared with controls. In conclusion, DEGs and hub genes identified in the present study help us understand the molecular mechanisms of sepsis, and provide candidate targets for diagnosis and treatment of sepsis. Wolters Kluwer Health 2020-07-02 /pmc/articles/PMC7337576/ /pubmed/32629654 http://dx.doi.org/10.1097/MD.0000000000020759 Text en Copyright © 2020 the Author(s). Published by Wolters Kluwer Health, Inc. http://creativecommons.org/licenses/by-nc/4.0 This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC), where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc/4.0 |
spellingShingle | 3900 Fu, Qinghui Yu, Wenqiao Fu, Shuiqiao Chen, Enjiang Zhang, Shaoyang Liang, Ting-bo Screening and identification of key gene in sepsis development: Evidence from bioinformatics analysis |
title | Screening and identification of key gene in sepsis development: Evidence from bioinformatics analysis |
title_full | Screening and identification of key gene in sepsis development: Evidence from bioinformatics analysis |
title_fullStr | Screening and identification of key gene in sepsis development: Evidence from bioinformatics analysis |
title_full_unstemmed | Screening and identification of key gene in sepsis development: Evidence from bioinformatics analysis |
title_short | Screening and identification of key gene in sepsis development: Evidence from bioinformatics analysis |
title_sort | screening and identification of key gene in sepsis development: evidence from bioinformatics analysis |
topic | 3900 |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7337576/ https://www.ncbi.nlm.nih.gov/pubmed/32629654 http://dx.doi.org/10.1097/MD.0000000000020759 |
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