Evidence map›Paper›PMID 34290221›Full record

ArticleMedical science monitor : international medical journal of experimental and clinical research2021

Identification of Key Genes and Underlying Mechanisms in Acute Kawasaki Disease Based on Bioinformatics Analysis.

Side Gao, Wenjian Ma, Xuze Lin, Sizhuang Huang, Mengyue Yu

Open access · hybridAbstract read
In one paragraph

Article in Medical science monitor : international medical journal of experimental and clinical research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
2.1field-weighted citation impact, top 13% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Review
  4. Article
  5. Article
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.

Side GaoDepartment of Cardiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (mainland).
Wenjian MaDepartment of Cardiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (mainland).
Xuze LinDepartment of Cardiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (mainland).
Sizhuang HuangDepartment of Cardiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (mainland).
Mengyue YuDepartment of Cardiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China (mainland).
Chinese Academy of Medical Sciences & Peking Union Medical College · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND Kawasaki disease (KD) is a systemic vasculitis that predominantly occurs in children, but the pathogenesis of KD remains unclear. Here, we explored key genes and underlying mechanisms potentially involved in KD using bioinformatic analyses. MATERIAL AND METHODS The shared differentially expressed genes (DEGs) in KD compared to control samples were identified using the microarray data from the Gene Expression Omnibus Series (GSE) 18606, GSE68004, and GSE73461. Analyses of the functional annotation, protein-protein interaction (PPI) network, microRNA-target DEGs regulatory network, and immune cell infiltration were performed. The expression of hub genes before and after intravenous immunoglobulin (IVIG) treatment in KD was further verified using GSE16797. RESULTS A total of 195 shared DEGs (164 upregulated and 31 downregulated genes) were identified between KD and healthy controls. These shared DEGs were mainly enriched in immune and inflammatory responses. Ten upregulated hub genes (ITGAX, SPI1, LILRB2, MMP9, S100A12, C3AR1, RETN, MAPK14, TLR5, MYD88) and the most significant module were identified in the PPI network. There were 309 regulatory relationships detected within 70 predicted microRNAs and 193 target DEGs. The immune cell infiltration analysis showed that monocytes, neutrophils, activated mast cells, and activated natural killer cells had relatively high proportions and were significantly more infiltrated in KD samples. Six hub genes of ITGAX, LILRB2, C3AR1, MAPK14, TLR5, and MYD88 were markedly downregulated after IVIG treatment for KD. CONCLUSIONS Our study identified the candidate genes and associated molecules that may be related to the KD process, and provided new insights into potential mechanisms and therapeutic targets for KD.

Indexed as

Acute DiseaseComputational BiologyHumansImmunoglobulinsMicroarray AnalysisMicroRNAsMucocutaneous Lymph Node SyndromeProtein Interaction MapsImmunoglobulinsMicroRNAs

Identifiers

PMID34290221
PMCPMC8314960
OpenAlexW3153123251

What OpenQuestion holds

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LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.