Identification of Hub Genes With Differential Correlations in Sepsis

• DOI: 10.3389/fgene.2022.876514
• Publication Date: 2022-03-29T00:51:26Z
• Authors: Lulu Sheng; Yiqing Tong; Yi Zhang; Qiming Feng
• Keywords: Identification; Gene regulatory network

As a multifaceted syndrome, sepsis leads to high risk of death worldwide. It is difficult be intervened due insufficient biomarkers and potential targets. The reason that regulatory mechanisms during are poorly understood. In this study, expression profiles from GSE134347 were integrated construct gene interaction network through weighted co-expression analysis (WGCNA). R package DiffCorr was utilized evaluate differential correlations identify significant differences between healthy tissues. result, twenty-six modules detected in the network, among which blue darkred exhibited most associations with sepsis. Finally, we identified some novel genes opposite including ZNF366, ZMYND11, SVIP UBE2H. Further biological revealed their promising roles management. Hence, correlations-based algorithm firstly established for discovery appealing regulators