AUTHOR=Lian Di , Lin Chenye , Xie Meiling , Wei JianXing , Huang Xueling , Lian Ke , Tang Qiuyu 

TITLE=Integrative bioinformatics analysis of pyroptosis-related genes and immune infiltration patterns in childhood asthma

JOURNAL=Frontiers in Genetics

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2025.1557709

DOI=10.3389/fgene.2025.1557709

ISSN=1664-8021

ABSTRACT=IntroductionChildhood asthma (CA) is a common chronic respiratory condition that significantly impacts the respiratory function and quality of life of affected children. With a rising global incidence, CA poses substantial physical, psychological, and economic burdens. This study aimed to elucidate the role of pyroptosis-related differentially expressed genes (PRDEGs) in CA by conducting a comprehensive bioinformatics analysis using an integrated dataset from the Gene Expression Omnibus.MethodsDifferential expression analysis was performed using the R package limma, identifying 2,069 differentially expressed genes (DEGs), with 1,158 upregulated and 911 downregulated genes in CA compared with the control group. Among these DEGs, 45 PRDEGs were identified, suggesting the potential involvement of pyroptosis in the pathological processes of CA. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses showed that PRDEGs were primarily enriched in biological processes related to the immune response, cell disassembly, and inflammatory pathways.ResultsImmune cell infiltration analysis using the CIBERSORT algorithm revealed significant differences between the CA and control groups, with increased macrophages M0, activated mast cells, and γδ T cells and decreased resting natural killer cells in the CA group. Among the six hub genes identified, BAX, BECN1, MAVS, and BCL2 exhibited statistically significant expression differences between the groups (p < 0.05 in GEO data; p < 0.0001 or p < 0.001 in quantitative real-time polymerase chain reaction validation), while NOD2 and NFKBIA showed no significant differences. Receiver operating characteristic analysis of BAX, BECN1, MAVS, and BCL2 supported their potential as diagnostic biomarkers for CA, with area under the curve values ranging from 0.602 to 0.621 (95% confidence interval: 0.510–0.712).DiscussionOur findings provide novel insights into the molecular mechanisms underlying CA and highlight the diagnostic potential of BAX, BECN1, MAVS, and BCL2 as biomarkers. Targeting PRDEGs may offer new therapeutic avenues, and further research is warranted to validate these findings and explore the clinical applicability of suggested biomarkers in precision medicine for managing CA.