Anti-ITGAM monoclonal antibody, clone 5C6

Sepsis is a critical illness with a high mortality rate in intensive care units. Septic shock is a severe, dangerous type of sepsis; however, the molecular mechanisms involved in septic shock are largely unclear. The present study integrated three datasets with 210 septic shock and 68 control samples to identify the hub genes involved in septic shock progression. Datasets GSE9692, GSE26378, and GSE26440 were downloaded from the Gene Expression Omnibus (GEO). Using bioinformatics tools such as GEO2R, Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes, and Genomes (KEGG) pathway analysis, and a protein-protein interaction (PPI) network analysis, five clusters and ten hub genes were successfully identified. The hub genes, including ITGAM, TLR4, TLR8, TLR2, MMP9, C3AR1, CCL5, FPR2, MPO, and LCK, were mainly enriched in the immune signaling pathway and in the inflammatory response. In summary, the present study identified ten septic shock-related genes using a bioinformatics analysis. The results indicate that the candidate genes may be involved in the regulation of immunity and the inflammatory response in septic shock and may act as predictors or therapeutic targets for septic shock.

Background: This study aimed to identify hub genes and pathways in a rat model of renal ischemia-reperfusion injury (IRI) using bioinformatics analysis of the Gene Expression Omnibus (GEO) microarray dataset and integration of gene expression profiles. Material/Methods: GEO software and the GEO2R calculation method were used to analyze two mRNA profiles, including GSE 39548 and GSE 108195. The co-expression of differentially expressed genes (DEGs) were identified and searched in the DAVID and STRING databases for pathway and protein-protein interaction (PPI) analysis. Cytoscape was used to draw the PPI network. DEGs were also analyzed using the Molecular Complex Detection (MCODE) algorithm. Cytoscape and cytoHubba were used to analyze the hub genes and visualize the molecular interaction networks. Rats (n=20) included the IRI model group (n=10) and a control group (n=10). Quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was used to measure and compare the expression of the identified genes in rat renal tissue in the IRI model and the control group. Results: Ten hub genes were identified, STAT3, CD44, ITGAM, CCL2, TIMP1, MYC, THBS1, IGF1, SOCS3, and CD14. Apart from IGF1, qRT-PCR showed that expression of these genes was significantly increased in renal tissue in the rat model of IRI. The HIF-la signaling pathway was involved in IRI in the rat model, which was supported by MCODE analysis. Conclusions: In a rat model of renal IRI, bioinformatics analysis of the GEO dataset and integration of gene expression pro- files identified involvement of HIF-1 alpha signaling and the STAT3 hub gene.