Assessing prospective molecular biomarkers and functional pathways in severe asthma based on a machine learning...

Area of Science:

• Pulmonary Medicine
• Molecular Biology
• Genetics

Background:

• Severe asthma presents distinct characteristics compared to typical asthma.
• Identifying specific molecular biomarkers is crucial for improved understanding and diagnosis.
• Understanding the underlying biological processes is essential for effective management.

Purpose of the Study:

• To investigate the biological processes associated with severe asthma.
• To identify key molecular biomarkers for severe asthma detection.
• To enhance diagnostic capabilities for severe asthma.

Main Methods:

• Weighted Gene Co-Expression Network Analysis (WGCNA) to identify hub genes.
• Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) analyses for functional annotation.
• Gene Set Enrichment Analysis (GSEA), Least Absolute Shrinkage and Selection Operator (LASSO) regression, and real-time quantitative PCR (RT-qPCR) for biomarker validation.

Main Results:

• WGCNA identified significant modules (purple and midnight blue) linked to clinical features.
• Hub genes were enriched in pathways related to mitochondrial function and oxidative phosphorylation.
• A LASSO model identified nine predictive genes, including TFCP2L1, KRT6A, FCER1A, and CCL5, validated by RT-qPCR.

Conclusions:

• Mitochondrial abnormalities and altered oxidative phosphorylation are critical in severe asthma.
• TFCP2L1, KRT6A, FCER1A, and CCL5 serve as essential molecular biomarkers for severe asthma.
• This study advances the understanding and diagnostic approaches for severe asthma.