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

A transcriptomic analysis of pediatric asthma, integrating bulk and single-cell RNA-seq data to identify candidate key genes. The dataset includes results from machine learning algorithms applied to public datasets GSE152004 and GSE65204, validated with in vitro and patient samples. It was authored by Yuchen Wen and last updated on June 3, 2026.

Use Cases

• Validate immune checkpoint gene signatures in pediatric asthma based on the identified HLA-DPA1 and HLA-DPB1 downregulation.
• Analyze macrophage subpopulation heterogeneity and pseudotime trajectories based on single-cell RNA-seq data mentioned in the description.
• Investigate cell-cell communication networks, such as the MIF signaling axis, between epithelial cells and immune cells.
• Build diagnostic models for pediatric asthma using the candidate key genes and nomogram approach described.

Strengths

• Integrates multiple data types, including bulk RNA-seq, single-cell RNA-seq (GSE254127), and experimental validation.
• Candidate genes were identified by consensus of four distinct machine learning algorithms (SVM-RFE, Boruta, LASSO, XGBoost).
• Released under a permissive CC-BY-4.0 license, allowing for broad reuse and adaptation.

Limitations

• The dataset is very small (13.3 KB), suggesting it contains summary results or a document rather than raw expression data.
• Column-level documentation and sample data are unavailable, making field semantics and structure unclear.
• Row count is unknown, which may limit suitability assessment for large-scale analysis.

Provenance

Source

figshare

Collection Method

Analysis of public RNA-sequencing datasets (GSE152004, GSE65204, GSE254127) combined with in vitro and patient sample validation.

Freshness

Last updated 2026-06-03 04:54:33; freshness should be verified.