Identification of Hub Genes With Differential Correlations in Sepsis - Sheng, Lulu; Tong, Yiqing; Zhang, Yi; Feng, Qiming (Frontiers in Genetics, 2022)
As a multifaceted syndrome, sepsis leads to high risk of death worldwide. The lack of effective diagnosis biomarkers and therapeutic targets makes sepsis difficult to intervene. The fundamental reason is that we know very little about the regulatory mechanisms during sepsis. Here, we employed weighted gene co-expression network analysis (WGCNA) to build gene interaction network using expression profile of sepsis from GSE134347. R package DiffCorr was used to calculate differential correlations and identify significant differences between sepsis and healthy tissues. A total of 26 modules were detected in the network, among which blue and darkred modules showed the most significant associations with sepsis. Dozens of new genes with opposite correlations including ZNF366, ZMYND11, SVIP and UBE2H were identified. Further biological analysis yielded their valuable roles in the diagnosis and treatment of sepsis. Our study pioneered differential correlations-based algorithm in the application of sepsis etiology as well as found several promising regulators for sepsis detection and therapy.