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

Data Sheet 6 from a figshare study by Yuchen Wen, last updated in June 2026, contains results from an integrative multi-omics analysis of pediatric asthma. The dataset likely contains findings from bulk RNA-seq, single-cell RNA-seq, and experimental validation focusing on HLA-DPA1 and HLA-DPB1 genes. It was generated using four machine learning algorithms and validated across independent datasets.

Use Cases

• Validate immune checkpoint gene signatures in pediatric asthma based on the identified HLA-DPA1/DPB1 downregulation.
• Investigate macrophage subpopulation heterogeneity based on the described Macro2 subset characterized by metabolic and stress-related functions.
• Analyze cell-cell communication networks in asthma based on the identified MIF signaling axis between epithelial cells and macrophages/dendritic cells.
• Build diagnostic models for pediatric asthma based on the nomogram constructed using HLA-DPA1 and HLA-DPB1 expression levels.

Strengths

• Analysis integrates multiple data types, including bulk RNA-seq (GSE152004, GSE65204), single-cell RNA-seq (GSE254127), and in vitro experimental validation.
• Candidate genes were identified by consensus of four distinct machine learning algorithms (SVM-RFE, Boruta, LASSO, and XGBoost).
• Dataset is openly shared under a CC-BY-4.0 license, facilitating reuse and verification.

Limitations

• The dataset is very small (955.0 B), suggesting it may contain only summary-level or highly processed results, not raw omics data.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment for certain analyses.

Provenance

Source

figshare

Collection Method

Integrated analysis of public transcriptomic datasets (GSE152004, GSE65204, GSE254127) and in vitro validation experiments.

Freshness

Last updated 2026-06-03 04:54:42