Abstract
Pediatric asthma, a growing chronic condition, faces diagnostic and treatment challenges in the post-COVID era. This study uses bioinformatics to explore lncRNAs' roles in pediatric asthma, aiming to improve diagnosis and treatment. To identify differentially expressed lncRNAs, the study will utilize a 2-pronged approach: obtaining gene expression datasets from the gene expression omnibus and recruiting patients for gene sequencing. The intersection of these 2 datasets will be analyzed to pinpoint lncRNAs that exhibit differential expression. The CIBERSORT approach was employed to estimate the composition of immune-infiltrating cells within the microenvironment of each patient in the gene expression omnibus dataset. Ultimately, real-time quantitative PCR was employed to confirm the variation in lncRNA in peripheral blood. In pediatric asthma patients, MIR22HG is significantly underexpressed compared to healthy subjects. Concurrently, there is a notable increase in naive CD4 T cells in the microenvironment during asthma, along with a negative correlation between MIR22HG and naive CD4 T cells.. In summary, MIR22HG might influence the onset and progression of pediatric asthma, which could serve as a diagnostic marker for pediatric asthma and offer valuable insights for asthma diagnosis.