HLA-DPA1/DPB1 Downregulation in Pediatric Asthma: Multi-Omics Analysis

HLA-DPA1 and HLA-DPB1 were identified as candidate key genes significantly downregulated in pediatric asthma through consensus of four machine learning algorithms. The dataset includes bulk RNA-seq from airway epithelium (GSE152004, GSE65204), single-cell RNA-seq (GSE254127), and experimental validation from IL-13-stimulated cells and patient samples. Yuchen Wen published this 13.8 KB document under CC-BY-4.0 on figshare in June 2026.

Use Cases

• Validate diagnostic biomarkers for pediatric asthma based on the identified downregulation of HLA-DPA1/DPB1.
• Analyze immune-metabolic dysregulation in macrophages based on the described single-cell subclustering and pseudotime trajectory.
• Study intercellular communication networks in asthma based on the described MIF signaling axis between epithelial cells and macrophages.
• Benchmark machine learning feature selection algorithms (SVM-RFE, Boruta, LASSO, XGBoost) on transcriptomic data for gene discovery.

Strengths

• Integrates multiple data sources: bulk RNA-seq, single-cell RNA-seq, and experimental validation.
• Candidate genes were identified by consensus of four distinct machine learning algorithms.
• Published under a permissive CC-BY-4.0 license for reuse.

Limitations

• The primary data file is a 13.8 KB DOCX document, suggesting it contains analysis results or a summary rather than the raw underlying datasets.
• Row and column counts for the underlying genomic data are unknown, limiting assessment of scale.
• Direct functional validation of the identified genes is noted as required to establish causality.

Provenance

Source

figshare, author Yuchen Wen.

Collection Method

Analysis of public RNA-sequencing datasets (GSE152004, GSE65204, GSE254127) and in vitro experimental validation.

Freshness

Last updated 2026-06-03 04:54:40.