Background: The key genes of pediatric asthma have not yet been identified and there is a lack of serological diagnostic markers. This may be related to the lack of comprehensive exploration of g The study sought to screen the key genes of childhood asthma using a machine-learning algorithm based on transcriptome sequencing results and explore potential diagnostic markers.
Methods: The transcriptome sequencing results (GSE188424) of pediatric asthmatic plasma samples were downloaded from the Gene Expression Omnibus database, including 43 controlled pediatric asthma serum samples and 46 uncontrolled pediatric asthma samples. R software (AT&T Bell Laboratories) was used to construct the weighted gene co-expression network and screen the hub genes. The penalty model was established by least absolute shrinkage and selection operator (LASSO) regression analysis to further screen the genes in the hub genes. The receiver operating characteristic curve (ROC) was used to confirm the diagnostic value of key genes.
Results: A total of 171 differentially expressed genes were screened from the controlled and uncontrolled samples. Chemokine (C-X-C motif) ligand 12 (CXCL12), matrix metallopeptidase 9 (MMP9), and wingless-type MMTV integration site family member 2 (WNT2) were the key genes, which were upregulated in the uncontrolled samples. The areas under the ROC curve of CXCL12, MMP9, and WNT2 were 0.895, 0.936, and 0.928, respectively.
Conclusions: The key genes CXCL12, MMP9, and WNT2 in pediatric asthma were identified by a bioinformatics analysis and machine-learning algorithm, which may be potential diagnostic biomarkers.
Keywords: Pediatric asthma; key gene; machine learning.
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