Glioma SLC1A3 Expression and Tumor Microenvironment Interactions

Multi-omics analysis identifies SLC1A3 as a key regulator in glioblastoma immune evasion. The 10.0 KB Excel file, authored by Hao Lin and updated in April 2026, contains results from RNA-seq, scRNA-seq pseudotime analysis, and in silico perturbations. Findings link SLC1A3 expression to poor prognosis and a cascading tumor-endothelial-T cell immunosuppressive axis.

Use Cases

• Validate SLC1A3 as a prognostic biomarker based on its correlation with poor clinical outcomes mentioned in the description
• Model tumor-endothelial cell communication networks based on the identified ligand-receptor axes (e.g., TNC-ITGB1, PTN-SDC3)
• Investigate transcriptional reprogramming in glioma-initiating cells using the described RNA-seq and alternative splicing analysis
• Benchmark in silico genetic perturbation results against the Geneformer foundation model predictions described

Strengths

• Data is derived from patient-derived GIC cohorts and validated across clinical TCGA cohorts.
• Analysis integrates multiple methodologies: RNA-seq, scRNA-seq pseudotime, AI-driven in silico knockout (Geneformer), and in vitro functional assays.
• Released under a permissive CC-BY-4.0 license for broad reuse.

Limitations

• The dataset is very small at 10.0 KB, indicating limited scope, likely summary results rather than raw sequencing data.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment for statistical modeling.

Provenance

Source

figshare

Collection Method

Integrated RNA-seq and alternative splicing analysis of neural stem cells and patient-derived glioma-initiating cell cohorts, combined with scRNA-seq pseudotime analysis and AI-driven in silico perturbations.

Freshness

Last updated 2026-04-23 05:35:42; freshness should be verified.