Multi-Omics Analysis of Inflammation Networks in Acute Mountain Sickness

323 differentially expressed genes were identified from integrated bulk RNA-seq, non-coding RNA-seq, and single-cell RNA-seq datasets. The analysis, authored by Zhicheng Xiang and uploaded on figshare in May 2026, explores inflammation-related mechanisms in acute mountain sickness and high-altitude cerebral edema. It includes constructed protein-protein interaction, miRNA-mRNA, transcription factor, and drug-gene networks.

Use Cases

• Identifying potential therapeutic targets like HBEGF based on drug-gene network analysis
• Analyzing cell-type-specific expression patterns in monocytes using single-cell RNA-seq data
• Exploring regulatory networks through miRNA-mRNA and transcription factor interactions
• Investigating pathway enrichment in epithelial cell growth and ErbB signaling via GO and KEGG analysis

Strengths

• Integrates three distinct RNA-seq datasets (bulk, non-coding, and single-cell) for a multi-omics perspective
• Identifies 323 differentially expressed genes and five specific inflammation-related genes
• Includes experimental validation from a hypobaric hypoxia-induced mouse model

Limitations

• Row count is unknown, which may limit suitability assessment
• Column-level documentation is absent; field semantics must be inferred after download
• The dataset is 8.4 KB, indicating a very limited scope likely containing summary results rather than raw data

Provenance

Source

figshare

Collection Method

Integrative analysis of public RNA-seq datasets (GSE75665, GSE90500) and single-cell data, combined with experimental validation.

Freshness

Last updated 2026-05-22 11:13:43; freshness should be verified