Consensus analysis via weighted gene co-expression network analysis (WGCNA) reveals genes participating in early phase of acute respiratory distress syndrome (ARDS) induced by sepsis

Fang, Qing; Wang, Qilai; Zhou, Zhiming; Xie, An

doi:10.1080/21655979.2021.1909961

Cited by 14 publications

(9 citation statements)

References 29 publications

(30 reference statements)

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“…A study on transcriptome analysis predicts drug candidates for sepsis-induced ARDS; doxorubicin could be a potential therapeutic for sepsis-induced ARDS by targeting TOP2A. However, this is only a statistical analysis and requires further investigation and validation [ 24 ]. Therefore, a safe and effective treatment method based on an in-depth discussion of the pathogenesis of ARDS is urgently needed.…”

Section: Discussionmentioning

confidence: 99%

Special electromagnetic field-treated water and far-infrared radiation alleviates lipopolysaccharide-induced acute respiratory distress syndrome in rats by regulating haptoglobin

Luo

et al. 2021

Bioengineered

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Section: Discussionmentioning

confidence: 99%

Special electromagnetic field-treated water and far-infrared radiation alleviates lipopolysaccharide-induced acute respiratory distress syndrome in rats by regulating haptoglobin

Luo

et al. 2021

Bioengineered

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“…It has been performed to measure the similarity of gene expression patterns in the livers of patients with different gender, or in neurodegenerative diseases, such as Alzheimer's disease, Parkinson's disease, etc. ( 14 , 15 ).…”

Section: Introductionmentioning

confidence: 99%

The Application of Consensus Weighted Gene Co-expression Network Analysis to Comparative Transcriptome Meta-Datasets of Multiple Sclerosis in Gray and White Matter

et al. 2022

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Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system characterized by demyelination, which leads to the formation of white matter lesions (WMLs) and gray matter lesions (GMLs). Recently, a large amount of transcriptomics or proteomics research works explored MS, but few studies focused on the differences and similarities between GMLs and WMLs in transcriptomics. Furthermore, there are astonishing pathological differences between WMLs and GMLs, for example, there are differences in the type and abundance of infiltrating immune cells between WMLs and GMLs. Here, we used consensus weighted gene co-expression network analysis (WGCNA), single-sample gene set enrichment analysis (ssGSEA), and machine learning methods to identify the transcriptomic differences and similarities of the MS between GMLs and WMLs, and to find the co-expression modules with significant differences or similarities between them. Through weighted co-expression network analysis and ssGSEA analysis, CD56 bright natural killer cell was identified as the key immune infiltration factor in MS, whether in GM or WM. We also found that the co-expression networks between the two groups are quite similar (density = 0.79), and 28 differentially expressed genes (DEGs) are distributed in the midnightblue module, which is most related to CD56 bright natural killer cell in GM. Simultaneously, we also found that there are huge disparities between the modules, such as divergences between darkred module and lightyellow module, and these divergences may be relevant to the functions of the genes in the modules.

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“…Accumulating studies have used transcriptome data comprised of cellular components contents between disease and healthy tissues to decipher potential molecular mechanisms of sepsis ( Zhang et al, 2020a ; Fang et al, 2021 ; Yu et al, 2021 ). Meanwhile, most of these studies incorporated differentially expressed genes and gene correlation data to explore gene interaction networks, followed by enrichment analysis to clarify function of unknown genes ( Balamuth et al, 2020 ; Zhai et al, 2020 ; Zhang et al, 2020b ).…”

Section: Introductionmentioning

confidence: 99%

Identification of Hub Genes With Differential Correlations in Sepsis

et al. 2022

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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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Consensus analysis via weighted gene co-expression network analysis (WGCNA) reveals genes participating in early phase of acute respiratory distress syndrome (ARDS) induced by sepsis

Abstract: To cite this article: Qing Fang, Qilai Wang, Zhiming Zhou & An Xie (2021) Consensus analysis via weighted gene co-expression network analysis (WGCNA) reveals genes participating in early phase of acute respiratory distress syndrome (ARDS) induced by sepsis,

Cited by 14 publications

References 29 publications

Special electromagnetic field-treated water and far-infrared radiation alleviates lipopolysaccharide-induced acute respiratory distress syndrome in rats by regulating haptoglobin

Special electromagnetic field-treated water and far-infrared radiation alleviates lipopolysaccharide-induced acute respiratory distress syndrome in rats by regulating haptoglobin

The Application of Consensus Weighted Gene Co-expression Network Analysis to Comparative Transcriptome Meta-Datasets of Multiple Sclerosis in Gray and White Matter

Identification of Hub Genes With Differential Correlations in Sepsis

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