Multi-omics Analysis of ST3GAL4 in TNBC Revealing Immune Evasion and Prognosis

An integrative multi-omics analysis of 85 KEGG metabolic pathways across TNBC and non-TNBC samples from TCGA, GEO, and 10X Genomics. Machine learning identified ST3GAL4 as a core enzyme linked to poor prognosis, with validation performed on 100 clinical samples. The dataset was authored by Yu Zhang and last updated on 2026-04-22.

Use Cases

• Identify prognostic biomarkers in TNBC based on multi-omics pathway analysis.
• Investigate correlations between metabolic gene expression and tumor immune infiltration profiles.
• Validate machine learning-prioritized gene targets using clinical immunohistochemistry data.

Strengths

• Integrates bulk RNA-seq, single-cell RNA-seq, and spatial transcriptomics data.
• Validates findings with immunohistochemistry on 100 clinical samples.
• Profiles 85 KEGG metabolic pathways for specificity.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The primary data file is a DOCX document, which may require extraction of underlying data.

Provenance

Source

TCGA, GEO, 10X Genomics, and The First Affiliated Hospital of Anhui University Chinese Medicine.

Collection Method

Integrative multi-omics analysis combining transcriptomics data and validated with clinical immunohistochemistry.

Freshness

Last updated 2026-04-22 05:46:48; freshness should be verified.