Glioma Prognostic Biomarker GLA Expression Data from TCGA and CGGA

Data from TCGA and CGGA datasets analyzes the expression of the sphingolipid metabolic enzyme GLA in gliomas. The analysis identified ten prognosis-related genes, with GLA showing the strongest association with survival, therapy resistance, and higher WHO grade. Time-dependent ROC analysis evaluated its predictive performance for 1-, 3-, and 5-year survival.

Use Cases

• Assess the association between GLA expression levels and WHO tumor grade across glioma samples.
• Validate the prognostic performance of GLA for 1-, 3-, and 5-year survival using time-dependent ROC analysis.
• Investigate correlations between GLA expression and pathways like sphingolipid metabolism or hypoxia-related signaling.
• Examine the relationship between GLA expression and EDEM2 expression based on the associated ERAD-related pathways.

Strengths

• Analysis is cross-validated using two major glioma cohorts: TCGA and CGGA.
• Identifies ten specific prognosis-related genes, with GLA highlighted as the strongest survival association.
• Includes functional validation from glioma cell line experiments, such as GLA knockdown effects.

Limitations

• The specific row count and column features of the underlying data are unknown.
• Data is derived from secondary analysis of public cohorts; original sample collection biases may be inherited.
• The 3.4 MB file size suggests a relatively small, aggregated analysis dataset, not the raw genomic data.

Provenance

Source

TCGA (The Cancer Genome Atlas) and CGGA (Chinese Glioma Genome Atlas) datasets.

Collection Method

Bioinformatic analysis of gene expression data, single-cell RNA sequencing, and functional validation in cell lines.

Freshness

Last updated March 24, 2026.