Screening and identification of the core immune‐related genes and immune cell infiltration in severe burns and sepsis
Severe burns often have a high mortality rate due to sepsis, but the genetic and immune crosstalk between them remains unclear. In the present study, the GSE77791 and GSE95233 datasets were analysed to identify immune‐related differentially expressed genes (DEGs) involved in disease progression in b...
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Published in | Journal of cellular and molecular medicine Vol. 27; no. 11; pp. 1493 - 1508 |
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Main Authors | , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
England
John Wiley & Sons, Inc
01.06.2023
John Wiley and Sons Inc |
Subjects | |
Online Access | Get full text |
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Summary: | Severe burns often have a high mortality rate due to sepsis, but the genetic and immune crosstalk between them remains unclear. In the present study, the GSE77791 and GSE95233 datasets were analysed to identify immune‐related differentially expressed genes (DEGs) involved in disease progression in both burns and sepsis. Subsequently, weighted gene coexpression network analysis (WGCNA), gene enrichment analysis, protein–protein interaction (PPI) network construction, immune cell infiltration analysis, core gene identification, coexpression network analysis and clinical correlation analysis were performed. A total of 282 common DEGs associated with burns and sepsis were identified. Kyoto Encyclopedia of Genes and Genomes pathway analysis identified the following enriched pathways in burns and sepsis: metabolic pathways; complement and coagulation cascades; legionellosis; starch and sucrose metabolism; and ferroptosis. Finally, six core DEGs were identified, namely, IL10, RETN, THBS1, FGF13, LCN2 and MMP9. Correlation analysis showed that some core DEGs were significantly associated with simultaneous dysregulation of immune cells. Of these, RETN upregulation was associated with a worse prognosis. The immune‐related genes and dysregulated immune cells in severe burns and sepsis provide potential research directions for diagnosis and treatment. |
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Bibliography: | GSE77791 and GSE95233 represent gene expression datasets. Wenxing Su, Wei Li and Yuanyuan Zhang contributed equally to this work. ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 1582-1838 1582-4934 |
DOI: | 10.1111/jcmm.17749 |