ArticleTranslational pediatrics2026
Combining bioinformatics and machine learning to identify common mechanisms and biomarkers of childhood asthma and obesity.
Article in Translational pediatrics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Childhood asthma (CA), a chronic inflammatory disorder of the airways, and childhood obesity (CO), characterized by low-grade systemic inflammation, frequently coexist. This study seeks to elucidate shared biological mechanisms underlying CA and CO and to identify potential biomarkers via comprehensive bioinformatics analyses of public datasets. Methods: CA and CO gene expression datasets were retrieved from the Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) common to both conditions were identified, with hub genes (HGs) screened via four machine learning (ML) algorithms. The diagnostic performance of candidate HGs was evaluated utilizing receiver operating characteristic (ROC) curve analysis. Functional characterization was conducted utilizing Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and single-gene gene set enrichment analysis (GSEA). In addition, competitive endogenous RNA (ceRNA) networks were constructed to further explore regulatory relationships and shared pathogenic mechanisms. Results: There were 25 key genes closely linked to CA and CO identified. Enrichment analyses indicated the main involvement of these genes in immune and inflammatory responses, as well as extracellular matrix organization and tissue remodeling. ML analyses ultimately identified Conclusions: This study identified
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