![]() ![]() ![]() Production are important immunological features ( 10). That the inhibition of caspase-1 and defective interleukin 1β In Gram-negative bacteria-induced sepsis, it has been determined Sepsis by regulating cellular motility and proinflammatory gene Identified to be involved in Gram-positive endotoxin-mediated Hypoxia-inducible factor 1α and Kruppel-like factor 2 have been Gram-positive bacteria and Gram-negative bacteria. Study illustrated the different mechanisms of sepsis caused by Univariate F test according to the cut-off criteria of falseĭetermined that Gram-positive sepsis and Gram-negative sepsis had aĬommon host response at the transcriptome level in critically ill Of GSE6535 to identify the differentially expressed genes (DEGs)īetween patients with Gram-positive and Gram-negative sepsis with Tang et al ( 8) used the microarray expression profile Sepsis, the most common causative agents are Gram-positive and Systems may damage the host's own tissues and organs, leading to TheĮxcessive activation of inflammation, complement and coagulation Mortality globally in critically ill patients ( 3, 4). Worldwide annually therefore, it is a major cause of morbidity and Sepsis causes ~18 million new cases and millions of deaths Sepsis is a systemic and deleterious inflammatory These findings may promote the therapies of sepsis caused by Gram‑positive and Gram‑negative bacteria. NDUFB2, NDUFB8 and UQCRH may be biomarkers for Gram‑negative sepsis, whereas LATS2 may be a biomarker for Gram‑positive sepsis. Stochastic perturbation analysis revealed that NADH:ubiquinone oxidoreductase subunit B2 (NDUFB2), NDUFB8 and ubiquinol‑cytochrome c reductase hinge protein (UQCRH) were associated with cellular respiration in Gram‑negative samples, whereas large tumor suppressor kinase 2 (LATS2) was associated with G1/S transition of the mitotic cell cycle in Gram‑positive samples. Additionally, NADH:ubiquinone oxidoreductase subunit S4 was associated with mitochondrial respiratory chain complex I assembly. In Gram‑positive and Gram‑negative samples, myeloid cell leukemia sequence 1 was associated with apoptosis and programmed cell death. Hierarchical clustering revealed that there were significant differences between control and sepsis samples. A total of 340 and 485 DEGs were obtained in Gram‑positive and Gram‑negative samples, respectively. Finally, stochastic perturbation was used to determine the significantly differential functions between Gram‑positive and Gram‑negative samples. Functional and pathway enrichment analyses were conducted for the DEGs using DAVID. To analyze the correlation of samples at the gene level, a similarity network was constructed using Cytoscape software. Hierarchical clustering was conducted for the specific DEGs in Gram‑negative and Gram‑negative samples using cluster software and the TreeView software. Subsequently, the limma package in R was used to screen the differentially expressed genes (DEGs). GSE6535 was downloaded from Gene Expression Omnibus, containing 17 control samples, 18 Gram‑positive samples and 25 Gram‑negative samples. The present study aimed to identify the key genes in Gram‑positive and Gram‑negative sepsis. Sepsis is an inflammatory response to pathogens (such as Gram‑positive and Gram‑negative bacteria), which has high morbidity and mortality in critically ill patients. ![]()
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