Document Type

Article

Source of Publication

Informatics in Medicine Unlocked

Publication Date

5-6-2025

Abstract

Background: Kawasaki disease (KD) is a complex acquired condition characterized by systemic blood vessel inflammation that primarily affects children under five years of age. It is clinically diagnosed as a syndrome, making it susceptible to misdiagnoses. Severe complications such as myocardial damage and coronary artery abnormalities can be fatal; thus, early diagnosis is critical for preventing disease progression. Currently, no specific diagnostic test can distinguish KD from viral or bacterial infections. Additionally, the molecular mechanisms underlying the disease remain unclear, hindering the development of targeted therapies. Objective: This study aimed to identify the genetic patterns and molecular mechanisms associated with KD using a comprehensive gene expression analysis. Methods: RNA sequencing and microarray genomic datasets were retrieved from the NCBI Gene Expression Omnibus (GEO). Four datasets (GSE68004, GSE63881, GSE73461, and GSE73463) were used for the final analysis. These datasets compared patients with KD to healthy controls, and patients with acute KD to convalescent patients. Differentially expressed genes (DEGs) were identified in the datasets. Enrichment analysis was conducted, followed by protein-protein interaction (PPI) network analysis to identify hub genes. Heatmaps were generated to visualize gene expression patterns. Results: Eighteen hub genes were identified in the KD versus control comparison, whereas 20 hub genes were identified in the acute versus convalescent analysis. These genes play key roles in inflammation, cytokine storm, innate immune modulation, and endothelial damage. Conclusion: This study provides valuable insights into the molecular mechanisms underlying KD, and identifies potential diagnostic biomarkers and therapeutic targets.

ISSN

2352-9148

Volume

56

Disciplines

Computer Sciences

Keywords

Cytokine regulation, Gene expression analysis, Inflammation biomarkers, Kawasaki disease, Protein-protein interaction

Scopus ID

05004929539

Indexed in Scopus

yes

Open Access

yes

Open Access Type

Gold: This publication is openly available in an open access journal/series

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