ArticleScientific reports2020
Crucial transcripts predict response to initial immunoglobulin treatment in acute Kawasaki disease.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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Who cites it
8 citing papers in PubMed, 18 citations in OpenAlex.
- Behind the scenes: how the EMILIN/Multimerin family shapes the cancer landscape.The FEBS journal · 2026Review
- Exploring the role of exosomal and non-exosomal non-coding RNAs in Kawasaki disease: Implications for diagnosis and therapeutic strategies against coronary artery aneurysms.Biochemistry and biophysics reports · 2025Review
- Article
- Distinctive serum lipidomic profile of IVIG-resistant Kawasaki disease children before and after treatment.PloS one · 2023Article
- Identification of key signaling pathways and hub genes related to immune infiltration in Kawasaki disease with resistance to intravenous immunoglobulin based on weighted gene co-expression network analysis.Frontiers in molecular biosciences · 2023Article
- Negative Regulation of the IL-1 System by IL-1R2 and IL-1R8: Relevance in Pathophysiology and Disease.Frontiers in immunology · 2022Review
- Single-Cell Analysis Reveals the Role of the Neuropeptide Receptor FPR2 in Monocytes in Kawasaki Disease: A Bioinformatic Study.Disease markers · 2022Article
- The Identification of Key Genes and Biological Pathways in Heart Failure by Integrated Bioinformatics Analysis.Computational and mathematical methods in medicine · 2021Article
Corrections and comments
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Authors and funding
12 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Although intravenous immunoglobulin (IVIG) can effectively treat Kawasaki disease (KD), 10-20% of KD patients show no beneficial clinical response. Developing reliable criteria to discriminate non-responders is important for early planning of appropriate regimens. To predict the non-responders before IVIG treatment, gene expression dataset of 110 responders and 61 non-responders was obtained from Gene Expression Omnibus. After weighted gene co-expression network analysis, we found that modules positively correlated with the non-responders were mainly associated with myeloid cell activation. Transcripts up-regulated in the non-responders, IL1R2, GK, HK3, C5orf32, CXCL16, NAMPT and EMILIN2, were proven to play key roles via interaction with other transcripts in co-expression network. The crucial transcripts may affect the clinical response to IVIG treatment in acute KD. And these transcripts may serve as biomarkers and therapeutic targets for precise diagnosis and treatment of the non-responders.
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Registered trials
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