Data Sheet 1_Integrated multi-omics and single-cell transcriptomic analysis reveals shared

261 common differentially expressed genes were identified between gout and metabolic syndrome from GEO transcriptomic datasets. The dataset, created by Zichen Shao and last updated in April 2026, includes results from protein-protein interaction, drug-gene, and ceRNA network analyses, validated with in vitro models. Single-cell RNA sequencing data reveals cell-cell communication signatures and hub gene expression in specific cell types.

Use Cases

• Identify shared hub genes like JAK1 and CSF1R based on integrated multi-omics analysis.
• Analyze cell-cell communication networks based on single-cell transcriptomic data highlighting monocytes and neutrophils.
• Validate bioinformatic predictions using qPCR and Western blot results from in vitro models.
• Investigate the VISFATIN/NAMPT signaling axis based on ligand-receptor interaction data.

Strengths

• Includes experimental validation of key predictions via qPCR and Western blot analysis.
• Integrates multiple analysis layers: PPI networks, drug-gene interactions, ceRNA networks, and single-cell data.
• Identifies 261 common differentially expressed genes and 19 hub genes from transcriptomic datasets.

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 GEO datasets and in vitro models used.

Provenance

Source

Transcriptomic data retrieved from the Gene Expression Omnibus (GEO) database.

Collection Method

Integrative bioinformatic analysis followed by experimental validation in cell models.

Time Range

Temporal coverage of the source GEO datasets is not specified.

Freshness

Last updated 2026-04-15 04:37:01; freshness should be verified.

Geography

Spatial coverage is not specified.