The Identification of Childhood Asthma Progression-Related lncRNAs and mRNAs Suitable as Biomarkers Using Weighted Gene Coexpression Network Analysis

Background. Asthma is a common chronic respiratory disease in children, seriously affecting children's health and growth. This bioinformatics study aimed to identify potential RNA candidates closely associated with childhood asthma development within current gene databases. Methods. GSE65204 and GSE19187 datasets were screened and downloaded from the NCBI GEO database. Differentially expressed long noncoding RNAs (DE-lncRNAs) and mRNAs (DE-mRNAs) were identified using the Bioconductor limma package in R, and these DE-mRNAs were used to perform biological process (BP) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Thereafter, weighted gene coexpression network analysis (WGCNA) was utilized to screen the modules directly related to childhood asthma, and a coexpression network of DE-lncRNAs and DE-mRNAs was built. Finally, principal component analysis (PCA) was performed. Results. In total, 7 DE-lncRNAs and 1060 DE-mRNAs, as well as 7 DE-lncRNAs and 1027 DE-mRNAs, were identified in GSE65204 and GSE19187, respectively. After comparison, 336 overlapping genes had the same trend of expression, including 2 overlapped DE-lncRNAs and 334 overlapped DE-mRNAs. These overlapped DE-mRNAs were enriched in 28 BP and 12 KEGG pathways. Eleven modules were obtained in GSE65204, and it was found that the purple, black, and yellow modules were significantly positively correlated with asthma development. Subsequently, a coexpression network including 63 DE-mRNAs and 2 DE-lncRNAs was built, and five KEGG pathways, containing 8 genes, were found to be directly associated with childhood asthma. The PCA further verified these results. Conclusion. LncRNAs LINC01559 and SNHG8 and mRNAs VWF, LAMB3, LAMA4, CAV1, ALDH1A3, SMOX, GNG4, and PPARG were identified as biomarkers associated with the progression of childhood asthma.

Automated summary

Asthma is a common chronic respiratory disease in children, and this bioinformatics study aimed to identify potential RNA candidates associated with childhood asthma development. Through differential expression analysis and coexpression network screening, specific biomarkers were found to be directly related to childhood asthma progression.