Data Sheet 1_Multi-omics analysis of ST3GAL4-mediated lacto/neolacto glycosphingolipid met

A multi-omics dataset integrates bulk RNA-seq, single-cell RNA-seq, and spatial transcriptomics data from TCGA, GEO, and 10X Genomics sources to investigate metabolic-immune mechanisms in triple-negative breast cancer (TNBC). The analysis, authored by Yu Zhang and last updated in April 2026, identifies ST3GAL4 as a core enzyme in lacto/neolacto glycosphingolipid metabolism linked to immune evasion and poor prognosis, validated with immunohistochemistry on 100 clinical samples.

Use Cases

• Identify prognostic biomarkers based on ST3GAL4 expression levels in TNBC.
• Analyze immune cell infiltration patterns based on CIBERSORT, ssGSEA, and ESTIMATE algorithm outputs.
• Investigate metabolic pathway activation based on KEGG pathway profiling across TNBC and non-TNBC samples.
• Validate spatial co-localization of immunosuppressive cells based on spatial transcriptomics data.
• Train machine learning models for gene prioritization based on Random Forest and XGBoost results.

Strengths

• Integrates data from multiple omics layers: bulk RNA-seq, single-cell RNA-seq, and spatial transcriptomics.
• Includes validation on 100 clinical TNBC samples via immunohistochemistry.
• Applies multiple computational methods for immune infiltration evaluation (CIBERSORT, ssGSEA, ESTIMATE).

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 geographic or source bias inherent to the specific repositories (TCGA, GEO) used.

Provenance

Source

Data aggregated from TCGA, GEO, and 10X Genomics, with clinical validation from The First Affiliated Hospital of Anhui University Chinese Medicine.

Collection Method

Integrative multi-omics analysis combining transcriptomic data and machine learning.

Freshness

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