Identification of Hub Genes With Differential Correlations in Sepsis.

ABSTRACT: As a multifaceted syndrome, sepsis leads to high risk of death worldwide. It is difficult to be intervened due to insufficient biomarkers and potential targets. The reason is that regulatory mechanisms during sepsis are poorly understood. In this study, expression profiles of sepsis from GSE134347 were integrated to construct gene interaction network through weighted gene co-expression network analysis (WGCNA). R package DiffCorr was utilized to evaluate differential correlations and identify significant differences between sepsis and healthy tissues. As a result, twenty-six modules were detected in the network, among which blue and darkred modules exhibited the most significant associations with sepsis. Finally, we identified some novel genes with opposite correlations including ZNF366, Z

SUBMITTER: Sheng L 

PROVIDER: S-EPMC8987114 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature