Subtyping children with asthma by clustering analysis of mRNA expression data

Area of Science:

• Pediatric Allergy and Immunology
• Computational Biology
• Genomics

Background:

• Asthma is a complex respiratory condition with varied presentations in children.
• Current phenotypic classifications for childhood asthma require refinement for personalized treatment.

Purpose of the Study:

• To classify childhood asthma subtypes using unsupervised consensus clustering.
• To identify differentially expressed genes (DEGs) and key biological pathways distinguishing these subtypes.
• To analyze the immune microenvironment differences between identified asthma subtypes.

Main Methods:

• Applied unsupervised consensus cluster analysis to a dataset of 36 children with persistent asthma (GSE65204).
• Utilized weighted gene co-expression network analysis (WGCNA) and Gene Ontology/Kyoto Encyclopedia of Genes and Genomes enrichment analysis.
• Constructed protein-protein interaction (PPI) networks to identify hub genes and analyzed immune microenvironment variations.

Main Results:

• Identified two distinct asthma subtypes (C1 and C2) based on gene expression profiles.
• DEGs and WGCNA modules were significantly enriched in immune regulation and inflammatory mediator pathways.
• Discovered 10 hub genes with high accuracy in distinguishing between C1 and C2, alongside notable immune microenvironment differences.

Conclusions:

• Two novel childhood asthma subtypes with distinct gene expression patterns and biological characteristics were identified from nasal epithelium gene expression data.
• These subtypes differ in their immune microenvironments, offering a basis for targeted therapeutic strategies.
• This classification provides a foundation for future advancements in childhood asthma typing and personalized medicine.