Bladder Cancer Transcriptomic Data with Amino Acid Metabolism Genes and Clinical Outcomes

Transcriptomic and clinical data from TCGA-BLCA, GSE13507, and GSE32894 cohorts were integrated with 32 MSigDB amino acid metabolism gene sets. The dataset contains 144 dysregulated amino acid metabolism-related genes used to define two metabolic subtypes and a 16-gene prognostic signature, created by Junrui He and last updated on 2026-03-18.

Use Cases

• Build prognostic models for bladder cancer based on the 16-gene metabolic signature.
• Identify molecular subtypes of bladder cancer based on amino acid metabolism gene expression patterns.
• Analyze associations between metabolic gene dysregulation and immune infiltration features.
• Validate drug sensitivity predictions for PI3K/mTOR inhibitors or DNA-damaging agents using the high-risk group classification.
• Investigate the role of specific genes like PSPH in bladder cancer cell proliferation and invasion.

Strengths

• Integrates data from three established cohorts: TCGA-BLCA, GSE13507, and GSE32894.
• Identifies 144 dysregulated amino acid metabolism-related genes and a validated 16-gene prognostic signature.
• Validated across multiple independent cohorts, showing consistent prognostic performance.
• Includes single-cell data (GSE222315) for tracing cellular origin of signature genes.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Data is contained in a 4.2 MB DOCX file, which may require extraction and formatting for analysis.

Provenance

Source

Transcriptomic and clinical data sourced from TCGA-BLCA, GSE13507, and GSE32894 cohorts.

Collection Method

Data integration, differential analysis, consensus clustering, and model construction using WGCNA and the MIME platform.

Freshness

Last updated 2026-03-18 07:35:00; freshness should be verified.