Integrative Bioinformatics Analysis of Pyroptosis-Related Genes and Immune Infiltration Patterns in Childhood Asthma

Di Lian¹Chenye Lin¹Meiling Xie²Jianxing Wei¹Xueling Huang¹Ke Lian¹Qiuyu Tang^3*

• ¹Pulmonology Department, Fujian Children's Hospital（Fujian Branch of Shanghai Children's Medical Center），College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University, Fuzhou Fujian, China
• ²Department of Nephrology, Blood Purification Research Center, The First Affiliated Hospital, Fujian Medical University, Fuzhou Fujian, Fuzhou Fujian, China
• ³School of Gynecology and Pediatrics, Fujian Medical University, Fuzhou, Fujian Province, China

Introduction: Childhood 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. Methods: Differential 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. Results: Immune 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). Discussion: Our 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.