Multi-Omics Analysis of Inflammation in Acute Mountain Sickness

323 differentially expressed genes were identified from integrated RNA-seq datasets (GSE75665, GSE90500) to explore inflammation in acute mountain sickness. The dataset, created by Zhicheng Xiang and last updated in May 2026, includes results from gene ontology, pathway, and network analyses, with experimental validation in a mouse model. It highlights five inflammation-related genes and the potential therapeutic target HBEGF.

Use Cases

• Identify key inflammation-related genes and pathways based on the 323 differentially expressed genes (DEGs) mentioned in the description.
• Construct regulatory networks based on the described protein-protein, miRNA-mRNA, and transcription factor interactions.
• Analyze cell-type-specific expression patterns based on the single-cell and deconvolution analyses of immune composition.
• Evaluate potential drug targets based on the drug-gene network analysis highlighting HBEGF.

Strengths

• Integrates multiple omics data types (bulk RNA-seq, non-coding RNA-seq, single-cell RNA-seq) from specific public datasets (GSE75665, GSE90500).
• Includes experimental validation from a hypobaric hypoxia-induced mouse model, as described.
• Analysis results are structured, including 323 DEGs and constructed networks for protein, miRNA, and drug interactions.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Data may reflect bias inherent to the specific experimental and bioinformatics pipeline described.

Provenance

Source

figshare, author Zhicheng Xiang

Collection Method

Computational integration of public RNA-seq datasets and experimental validation with a mouse model.

Freshness

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