Multi-Omics Analysis of Inflammation in Acute Mountain Sickness

Table 2_Integrative multi-omics analysis reveals inflammation-related molecular networks in acute mountain sickness.xlsx contains results from a multi-omics study of Acute Mountain Sickness (AMS). The dataset was authored by Zhicheng Xiang and last updated on 2026-05-22. It integrates bulk RNA-seq, non-coding RNA-seq, and single-cell RNA-seq data to explore inflammation-related mechanisms.

Use Cases

• Identify differentially expressed genes (DEGs) based on the 323 DEGs and five inflammation-related genes mentioned in the description.
• Analyze protein-protein interaction, miRNA-mRNA, and drug-gene networks based on the constructed networks described.
• Investigate cell-type-specific expression based on the single-cell and deconvolution analyses that identified monocytes as a key source.
• Validate potential therapeutic targets based on the highlighted role of HBEGF as a target from the drug-gene analysis.

Strengths

• Integrates multiple omics data types (bulk RNA-seq, non-coding RNA-seq, single-cell RNA-seq) as described in the methods.
• Includes experimental validation from a hypobaric hypoxia-induced mouse model, as mentioned in the results.
• Results are structured around specific findings, including 323 DEGs and the identification of hsa-miR-375 and HBEGF.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The dataset is small (62.3 KB), indicating it likely contains summary results rather than raw sequencing data.

Provenance

Source

Zhicheng Xiang via figshare.

Collection Method

Integrative analysis of public RNA-seq datasets (GSE75665, GSE90500) and generation of new single-cell RNA-seq data, combined with experimental validation in a mouse model.

Freshness

Last updated 2026-05-22 11:13:42; freshness should be verified.