ArticleFrontiers in genetics2022
Identification of Hub Genes With Differential Correlations in Sepsis.
Article in Frontiers in genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 7 citations in OpenAlex.
- Identification and Validation of programmed cell death-related biomarkers in Osteoarthritis and an Exploratory Pan-Cancer Analysis: SHARPIN as a Potential Biomarker.Clinical rheumatology · 2026Article
- Quantifying pathway perturbations based on gene network rewiring in sepsis progression toward death or survival.Frontiers in bioinformatics · 2026Article
- Excavation of gene markers associated with pancreatic ductal adenocarcinoma based on interrelationships of gene expression.IET systems biology · 2024Article
- Article
- Predicting patients with septic shock and sepsis through analyzing whole-blood expression of NK cell-related hub genes using an advanced machine learning framework.Frontiers in immunology · 2024Article
- Identification and experimental validation of cuproptosis regulatory program in a sepsis immune microenvironment through a combination of single-cell and bulk RNA sequencing.Frontiers in immunology · 2024Article
- High expression of LOC541471, GDAP1, SOD1, and STK25 is associated with poor overall survival of patients with acute myeloid leukemia.Cancer medicine · 2023Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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