Development and validation of an RNA-seq-based transcriptomic risk score for asthma.

Area of Science:

• Genomics
• Transcriptomics
• Biostatistics

Background:

• RNA sequencing (RNA-seq) enables whole-genome gene expression analysis for disease risk prediction.
• Developing accurate predictive models for complex diseases like asthma is crucial.

Purpose of the Study:

• To develop and validate an RNA-seq-based transcriptomic risk score (RSRS) for asthma risk prediction.
• To integrate demographic information with gene expression data for enhanced predictive power.

Main Methods:

• Analyzed RNA-seq data from asthmatic and non-asthmatic individuals.
• Utilized logistic least absolute shrinkage and selection operator (Lasso) regression to identify differentially expressed genes (DEGs).
• Validated the 73-gene RSRS in three independent datasets.

Main Results:

• A 73-gene RSRS was developed, achieving an area under the curve (AUC) of 0.80 for discriminating asthmatics from controls after adjusting for age and gender.
• The RSRS demonstrated consistent performance in independent validation datasets with AUCs of 0.70, 0.77, and 0.60.
• Enrichment pathway analysis revealed significant enrichment of the 73 genes in DNA replication, cell-to-cell signaling, and developmental pathways.
• In-silico analysis identified potential drug repurposing candidates and genetic perturbagens for asthma treatment.

Conclusions:

• RNA-seq-based risk scores can effectively stratify and predict disease risk, offering a promising tool for personalized medicine.
• The identified 73 genes and pathways provide insights into asthma pathogenesis.
• Potential therapeutic targets and drug repurposing opportunities for asthma were identified.