OBJECTIVE: To screen the genes related with leukocyte responses in mice early after burn injury by bioinformatic analysis of the gene expression profiling data. METHODS: Gene expression profiles were obtained from GEO (GSE7404, Mouse musculus, 25% TBSA, full-thickness) database. After screening of the differentially expressed genes (DEGs) through paired-sample t-test and fold-change, DAVID online tools were used to select the DEGs related to leukocyte responses to burns by GO functional enrichment analysis; the interacting genes identified through KEGG pathway enrichment analysis were transferred to STRING to construct the protein-protein interaction (PPI) network. Biological annotation of the sub-networks was executed using the software Cytoscape. Real-time PCR was used to verify the DEGs identified in mice. RESULTS: Of the 259 leukocyte response-related DEGs screened at 1 day post-burn, 118 were up-regulated and 141 were down-regulated. KEGG pathway enrichment analysis showed that the pathways were associated with the immune function, cell growth and cell death. PPI network and module analysis suggested that some of genes (such as Lck, Stat1, Myd88, Stat3, and Jun) play critical roles in the PPI network post-burn. RT-PCR results were consistent with those of bioinformatic analysis. CONCLUSIONS: Lck, Stat1, Myd88, Stat3, and Jun might be critical players in the development of leukocyte response in mice early after burn injury. Our finding provides new insights into the pathogenesis of leukocyte response to burn injury and identifies several potential biomarkers for burn treatment.
OBJECTIVE: To screen the genes related with leukocyte responses in mice early after burn injury by bioinformatic analysis of the gene expression profiling data. METHODS: Gene expression profiles were obtained from GEO (GSE7404, Mouse musculus, 25% TBSA, full-thickness) database. After screening of the differentially expressed genes (DEGs) through paired-sample t-test and fold-change, DAVID online tools were used to select the DEGs related to leukocyte responses to burns by GO functional enrichment analysis; the interacting genes identified through KEGG pathway enrichment analysis were transferred to STRING to construct the protein-protein interaction (PPI) network. Biological annotation of the sub-networks was executed using the software Cytoscape. Real-time PCR was used to verify the DEGs identified in mice. RESULTS: Of the 259 leukocyte response-related DEGs screened at 1 day post-burn, 118 were up-regulated and 141 were down-regulated. KEGG pathway enrichment analysis showed that the pathways were associated with the immune function, cell growth and cell death. PPI network and module analysis suggested that some of genes (such as Lck, Stat1, Myd88, Stat3, and Jun) play critical roles in the PPI network post-burn. RT-PCR results were consistent with those of bioinformatic analysis. CONCLUSIONS:Lck, Stat1, Myd88, Stat3, and Jun might be critical players in the development of leukocyte response in mice early after burn injury. Our finding provides new insights into the pathogenesis of leukocyte response to burn injury and identifies several potential biomarkers for burn treatment.