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Published on: October 3, 2025
The Blood Gene Expression Signature for Kawasaki Disease in Children Identified with Advanced Feature Selection
Bing Hu1, Yun Li2, Guilian Wang1
1Department of Pediatrics, Yichun People's Hospital, Yichun 336000, China.
Insights
Kawasaki disease (KD) is an acute vasculitis. This study identified key genes and molecular mechanisms involved in KD and pathogen infections, aiding in detection and treatment.
Area of Science:
- Cardiovascular Research
- Molecular Biology
- Infectious Diseases
Background:
- Kawasaki disease (KD) is an acute vasculitis with severe cardiovascular complications.
- The etiology and molecular mechanisms of KD remain largely unknown.
- Understanding KD pathogenesis is crucial for effective diagnosis and treatment.
Purpose of the Study:
- To investigate the molecular mechanisms underlying Kawasaki disease (KD).
- To identify key genes associated with KD and concurrent pathogen infections.
- To provide insights for improved KD detection and therapeutic strategies.
Main Methods:
- Analysis of gene expression profiles from bacterial (DB), viral (DV), healthy control (HC), and KD patient samples.
- Application of advanced feature selection methods including Boruta, Monte-Carlo Feature Selection (MCFS), and Incremental Feature Selection (IFS).
- Functional enrichment analyses using KEGG and Gene Ontology (GO) to understand gene functions.
Main Results:
- Identification of 332 key genes significantly related to KD and pathogen infections.
- Stepwise reduction of signature genes to pinpoint critical molecular players.
- KEGG and GO analyses revealed potential molecular pathways involved in KD pathogenesis.
Conclusions:
- The study provides valuable clues into the molecular mechanisms of Kawasaki disease.
- Identified genes and pathways may serve as biomarkers for KD detection.
- Findings contribute to a better understanding of KD, potentially guiding future treatments.
Abstract:
Kawasaki disease (KD) is an acute vasculitis, accompanied by coronary artery aneurysm, coronary artery dilatation, arrhythmia, and other serious cardiovascular diseases. So far, the etiology of KD is unclear; it is necessary to study the molecular mechanism and related factors of KD. In this study, we analyzed the expression profiles of 75 DB (identifying bacteria), 122 DV (identifying virus), 71 HC (healthy control), and 311 KD (Kawasaki disease) samples. 332 key genes related to KD and pathogen infections were identified using a combination of advanced feature selection methods: (1) Boruta, (2) Monte-Carlo Feature Selection (MCFS), and (3) Incremental Feature Selection (IFS). The number of signature genes was narrowed down step by step. Subsequently, their functions were revealed by KEGG and GO enrichment analyses. Our results provided clues of potential molecular mechanisms of KD and were helpful for KD detection and treatment.
