Identification of Key Genes Associated with Changes in the Host Response to Severe Burn Shock: A Bioinformatics Analysis with Data from the Gene Expression Omnibus (GEO) Database
Xiao Fang,* Shu-Fang Duan,* Yu-Zhou Gong,* Fei Wang, Xu-Lin Chen Department of Burns, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xu-Lin ChenDepartment of Burns, The First...
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| Autores principales: | , , , , |
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| Formato: | article |
| Lenguaje: | EN |
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Dove Medical Press
2020
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| Acceso en línea: | https://doaj.org/article/85f62e7c3206437dbfd08ff465b8d1a6 |
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| Sumario: | Xiao Fang,* Shu-Fang Duan,* Yu-Zhou Gong,* Fei Wang, Xu-Lin Chen Department of Burns, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xu-Lin ChenDepartment of Burns, The First Affiliated Hospital of Anhui Medical University, 120 Wanshui Road, Hefei, Anhui 230088, People’s Republic of ChinaTel/Fax +86-551-65908495Email okcxl@126.comBackground: Patients with severe burns continue to display a high mortality rate during the initial shock period. The precise molecular mechanism underlying the change in host response during severe burn shock remains unknown. This study aimed to identify key genes leading to the change in host response during burn shock.Methods: The GSE77791 dataset, which was utilized in a previous study that compared hydrocortisone administration to placebo (NaCl 0.9%) in the inflammatory reaction of severe burn shock, was downloaded from the Gene Expression Omnibus (GEO) database and analyzed to identify the differentially expressed genes (DEGs). Functional enrichment analyses of Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were performed. The protein–protein interaction (PPI) network of DEGs was constructed using the Search Tool for the Retrieval of Interacting Genes (STRING) database and then visualized in Cytoscape. In addition, important modules in this network were selected using the Molecular Complex Detection (MCODE) algorithm, and hub genes were identified in cytoHubba, a Cytoscape plugin.Results: A total of 1059 DEGs (508 downregulated genes and 551 upregulated genes) were identified from the dataset. The DEGs enriched in GO terms and KEGG pathways were related to immune response. The PPI network contained 439 nodes and 2430 protein pairs. Finally, important modules and hub genes were identified using the different Cytoscape plugins. The key genes in burn shock were identified as arginase 1 (ARG1), cytoskeleton-associated protein (CKAP4), complement C3a receptor (C3AR1), neutrophil elastase (ELANE), gamma-glutamyl hydrolase (GGH), orosomucoid (ORM1), and quiescin sulfhydryl (QSOX1).Conclusion: The DEGs, functional terms and pathways, and hub genes identified in the present study can help shed light on the molecular mechanism underlying the changes in host response during burn shock and provide potential targets for early detection and treatment of burn shock.Keywords: burn shock, in silico study, host response, immune response |
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