Prognostic Gene Signature for Clear Cell Renal Cell Carcinoma Based on Ammonia Metabolism

An integrated analysis of bulk and single-cell transcriptomics data for clear cell renal cell carcinoma (ccRCC). The dataset, authored by Yao Jiang and last updated in April 2026, contains a risk model constructed from 6 prognostic genes (RGS20, ADA, AICDA, SLC12A5, RUFY4, CDK5RAP3). It includes results from functional enrichment, immune infiltration, somatic mutation, and RT-qPCR analyses.

Use Cases

• Validating a 6-gene prognostic risk score for ccRCC patients based on the described model.
• Analyzing differential immune cell infiltration between high-risk and low-risk patient groups.
• Investigating the role of ammonia metabolism-related genes in cancer cell death pathways.
• Studying cell communication and pseudo-temporal trajectories of malignant cells identified in single-cell data.

Strengths

• Model is based on an integrated analysis of both bulk and single-cell transcriptomic data.
• Identifies 6 specific prognostic genes (RGS20, ADA, AICDA, SLC12A5, RUFY4, CDK5RAP3) with RT-qPCR validation.
• Includes analyses of 16 immune cell types with differential abundance between risk groups.
• Released under a permissive CC-BY-4.0 license for reuse.

Limitations

• Row count and column-level documentation are absent; field semantics must be inferred after download.
• The dataset is small (124.3 KB), indicating limited scope, likely containing summary results rather than raw sequencing data.
• Geographic and demographic context for the patient samples is not provided.

Provenance

Source

figshare, author Yao Jiang.

Collection Method

Transcriptomic data retrieved from public resources, with analysis involving differential expression, machine learning, and single-cell techniques.

Freshness

Last updated 2026-04-23 05:36:02.