Multi-omics Integration for Berberine Mechanism in Triple-Negative Breast Cancer

A supplementary dataset from a 2026 study by Ying Zhang investigating the anti-tumor mechanism of berberine against triple-negative breast cancer. The data likely contains results from integrated network pharmacology, transcriptomic analysis, and multi-level experimental validation. It includes 182 common targets and 12 hub genes such as SRC, STAT3, and EGFR.

Use Cases

• Validate predicted drug-target interactions based on the 182 common targets identified.
• Analyze pathway activity changes based on the downregulation of p-PI3K/p-AKT and p-SRC/p-STAT3 pathways mentioned.
• Study tumor microenvironment associations based on immune infiltration and single-cell sequencing results described.

Strengths

• Identifies 182 common targets and 12 hub genes, including SRC, STAT3, and EGFR.
• Integrates results from multiple methodologies, including network pharmacology, transcriptomics, and molecular docking.
• Includes experimental validation data such as molecular docking scores (e.g., SRC affinity of -8.8 kcal/mol).

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The 15.4 KB file size suggests a limited scope of data.

Provenance

Source

Ying Zhang via figshare

Collection Method

Integrated analysis combining network pharmacology, transcriptomic analysis from GEO and TCGA data, molecular docking, dynamics simulation, and in vitro experiments.

Freshness

Last updated 2026-05-28 08:14:05