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. 2022 Mar 24:13:876514.
doi: 10.3389/fgene.2022.876514. eCollection 2022.

Identification of Hub Genes With Differential Correlations in Sepsis

Lulu Sheng  1 , Yiqing Tong  1 , Yi Zhang  2 , Qiming Feng  1
Affiliations

Identification of Hub Genes With Differential Correlations in Sepsis

Lulu Sheng et al. Front Genet. .

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, ZMYND11, SVIP and UBE2H. Further biological analysis revealed their promising roles in sepsis management. Hence, differential correlations-based algorithm was firstly established for the discovery of appealing regulators in sepsis.

Keywords: WGCNA; biological analysis; differential correlation; regulatory network; sepsis.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Clustering dendrogram of sepsis and healthy tissues. (A) The workflow of this study. (B) Clustering dendrogram of 156 patients with sepsis and 82 healthy subjects and trait heatmap. (C) The relationship between soft threshold (power) and network properties. Left panel: The relationship between soft-threshold (power) and scale-free topology. Right panel: The relationship between soft threshold (power) and mean connectivity.
FIGURE 2
FIGURE 2
Identification of modules associated with the clinical traits of sepsis. Heatmap of the correlation between the module eigengenes and clinical traits of sepsis. All genes were clustered into twenty-six modules, of which each was labeled with one color.
FIGURE 3
FIGURE 3
Functional enrichment analysis of genes in the blue and darkred modules. (A) Left panel: GO analysis showed top ten enriched biological processes in blue module. Right panel: KEGG analysis showed top ten enriched pathways in blue module. (B) Left panel: GO analysis showed top ten enriched biological processes in darkred module. Right panel: KEGG analysis showed top ten enriched pathways in darkred module.
FIGURE 4
FIGURE 4
Module networks. The blue (A) and darkred (B) module networks from GSE134347 were shown. Each node represented one module. Each edge represented module correlation.
FIGURE 5
FIGURE 5
Differential co-expressed gene networks in the blue (A) and darkred (B) modules from GSE134347. Each node represented one gene. Each edge represented correlation between two genes, in which red meant positive correlation and green negative correlation. The thicker edge represented the stronger correlation coefficient.
FIGURE 6
FIGURE 6
The expression levels of ZNF366 (A), ZMYND11(B), SVIP (C) and UBE2H (D) in sepsis and healthy samples from GSE134347. These four hub genes were all differentially expressed. *** p < 0.001.
See this image and copyright information in PMC

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