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Identification of Key Genes in Severe Burns by Using Weighted Gene Coexpression Network Analysis
The aims of this work were to explore the use of weighted gene coexpression network analysis (WGCNA) for identifying the key genes in severe burns and to provide a reference for finding therapeutic targets for burn wounds. The GSE8056 dataset was selected from the gene expression database of the US...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9256319/ https://www.ncbi.nlm.nih.gov/pubmed/35799661 http://dx.doi.org/10.1155/2022/5220403 |
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author | Guo, ZhiHui Zhang, YuJiao Ming, ZhiGuo Hao, ZhenMing Duan, Peng |
author_facet | Guo, ZhiHui Zhang, YuJiao Ming, ZhiGuo Hao, ZhenMing Duan, Peng |
author_sort | Guo, ZhiHui |
collection | PubMed |
description | The aims of this work were to explore the use of weighted gene coexpression network analysis (WGCNA) for identifying the key genes in severe burns and to provide a reference for finding therapeutic targets for burn wounds. The GSE8056 dataset was selected from the gene expression database of the US National Center for Biotechnology Information for analysis, and a WGCNA network was constructed to screen differentially expressed genes (DEGs). Gene Ontology and pathway enrichment of DGEs were analyzed, and protein interaction network was constructed. A burn mouse model was constructed, and the burn tissue was taken to identify the expression levels of differentially expressed genes. The results showed that the optimal soft threshold for constructing the WGCNA network was 9. 10 coexpressed gene modules were identified, among which the green, brown, and gray modules had the largest number of burn-related genes. The DEGs were mainly related to immune cell activation, inflammatory response, and immune response, and they were enriched in PD-1/PD-L1, Toll-like receptor, p53, and nuclear factor-kappa B (NF-κB) signaling pathways. 5 DEGs were screened and identified, namely, Jun protooncogene (JUN), signal transducer and activator of transcription 1 (STAT1), BCL2 apoptosis regulator (Bcl2), matrix metallopeptidase 9 (MMP9), and Toll-like receptor 2 (TLR2). Compared with skin tissue of normal mouse, the messenger ribose nucleic acid (mRNA) and protein expression levels (PEL) of STAT1 and Bcl2 in burn tissue were greatly decreased, while those of JUN, MMP9, and TLR2 were increased obviously (p < 0.05). In conclusion, STAT1, Bcl2, JUN, MMP9, and TLR2 can be potential biological targets for the treatment of severe burn wounds. |
format | Online Article Text |
id | pubmed-9256319 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-92563192022-07-06 Identification of Key Genes in Severe Burns by Using Weighted Gene Coexpression Network Analysis Guo, ZhiHui Zhang, YuJiao Ming, ZhiGuo Hao, ZhenMing Duan, Peng Comput Math Methods Med Research Article The aims of this work were to explore the use of weighted gene coexpression network analysis (WGCNA) for identifying the key genes in severe burns and to provide a reference for finding therapeutic targets for burn wounds. The GSE8056 dataset was selected from the gene expression database of the US National Center for Biotechnology Information for analysis, and a WGCNA network was constructed to screen differentially expressed genes (DEGs). Gene Ontology and pathway enrichment of DGEs were analyzed, and protein interaction network was constructed. A burn mouse model was constructed, and the burn tissue was taken to identify the expression levels of differentially expressed genes. The results showed that the optimal soft threshold for constructing the WGCNA network was 9. 10 coexpressed gene modules were identified, among which the green, brown, and gray modules had the largest number of burn-related genes. The DEGs were mainly related to immune cell activation, inflammatory response, and immune response, and they were enriched in PD-1/PD-L1, Toll-like receptor, p53, and nuclear factor-kappa B (NF-κB) signaling pathways. 5 DEGs were screened and identified, namely, Jun protooncogene (JUN), signal transducer and activator of transcription 1 (STAT1), BCL2 apoptosis regulator (Bcl2), matrix metallopeptidase 9 (MMP9), and Toll-like receptor 2 (TLR2). Compared with skin tissue of normal mouse, the messenger ribose nucleic acid (mRNA) and protein expression levels (PEL) of STAT1 and Bcl2 in burn tissue were greatly decreased, while those of JUN, MMP9, and TLR2 were increased obviously (p < 0.05). In conclusion, STAT1, Bcl2, JUN, MMP9, and TLR2 can be potential biological targets for the treatment of severe burn wounds. Hindawi 2022-06-28 /pmc/articles/PMC9256319/ /pubmed/35799661 http://dx.doi.org/10.1155/2022/5220403 Text en Copyright © 2022 ZhiHui Guo 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 Guo, ZhiHui Zhang, YuJiao Ming, ZhiGuo Hao, ZhenMing Duan, Peng Identification of Key Genes in Severe Burns by Using Weighted Gene Coexpression Network Analysis |
title | Identification of Key Genes in Severe Burns by Using Weighted Gene Coexpression Network Analysis |
title_full | Identification of Key Genes in Severe Burns by Using Weighted Gene Coexpression Network Analysis |
title_fullStr | Identification of Key Genes in Severe Burns by Using Weighted Gene Coexpression Network Analysis |
title_full_unstemmed | Identification of Key Genes in Severe Burns by Using Weighted Gene Coexpression Network Analysis |
title_short | Identification of Key Genes in Severe Burns by Using Weighted Gene Coexpression Network Analysis |
title_sort | identification of key genes in severe burns by using weighted gene coexpression network analysis |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9256319/ https://www.ncbi.nlm.nih.gov/pubmed/35799661 http://dx.doi.org/10.1155/2022/5220403 |
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