Evidence map›Paper›PMID 33188570›Full record

ArticleImmunity, inflammation and disease2021

Five immune-gene-signatures participate in the development and pathogenesis of Kawasaki disease.

Han Nie, Shizhi Wang, Quanli Wu, Danni Xue, Weimin Zhou

Open access · goldAbstract read
In one paragraph

Article in Immunity, inflammation and disease, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
4.5field-weighted citation impact, top 4% of its field
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

10 citing papers in PubMed, 24 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 1 institution in 1 country.

Han NieDepartment of Vascular Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China.
Shizhi WangDepartment of Vascular Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China.
Quanli WuMedical College of Nanchang University, Nanchang, Jiangxi, China.
Danni XueMedical College of Nanchang University, Nanchang, Jiangxi, China.
Weimin ZhouDepartment of Vascular Surgery, The Second Affiliated Hospital of Nanchang University, Nanchang, Jiangxi, China.ORCID 0000-0003-4081-1919
Nanchang University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo screen for immune genes that play a major role in Kawasaki disease and to investigate the pathogenesis of Kawasaki disease through bioinformatics analysis.

methodsKawasaki disease-related datasets GSE18606, GSE68004, and GSE73461 were downloaded from the Gene Expression Omnibus database. Three microarrays were integrated and standardized to include 173 Kawasaki disease samples and 101 normal samples. The samples were analyzed using CIBERSORT to obtain the infiltration of 22 immune cells and analyze the differential immune cells in the samples and correlations. The distribution of the samples was analyzed using principal component analysis (PCA). Immune-related genes were downloaded, extracted from the screened samples and analyzed for differential analysis (different expression genes [DEG]) and weighted gene co-expression network analysis (WGCNA). We constructed coexpression networks, and used the cytohobbe tool in Cytoscape to analyze the coexpression networks and select the immune genes that played a key role in them.

resultsImmune cell infiltration analysis showed that B cells naive, T cells CD8, natural killer (NK) cells activated, and so forth were highly expressed in normal samples. T cells CD4 memory activated, monocytes, neutrophils, and so forth were highly expressed in Kawasaki disease samples. PCA results showed a significant difference in the distribution of normal and Kawasaki disease samples. From the screened samples, 97 upregulated and 103 downregulated immune-related genes were extracted. WGCNA analysis of DEG yielded 10 gene modules, of which the three most relevant to Kawasaki disease were red, yellow, and gray modules. They were associated with cytokine regulation, T-cell activation, presentation of T-cell receptor signaling pathways, and NK cell-mediated cytotoxicity. CXCL8, CCL5, CCR7, CXCR3, and CCR1 were identified as key genes by constructing a coexpression network.

conclusionOur study shows that we can distinguish normal samples from Kawasaki disease samples based on the infiltration of immune cells, and that CXCL8, CCL5, CCR7, CXCR3, and CCR1 may play important roles in the development of Kawasaki disease.

Indexed as

Mucocutaneous Lymph Node SyndromeComputational BiologyGene ExpressionGene Expression ProfilingGene Regulatory NetworksHumansimmune-related genesKawasaki diseaseWGCNA

Identifiers

PMID33188570
PMCPMC7860604
OpenAlexW3101142174

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