Table 6_Identification of a prognostic signature based on ammonia metabolism-related genes

Six prognostic genes (RGS20, ADA, AICDA, SLC12A5, RUFY4, CDK5RAP3) were identified for clear cell renal cell carcinoma through integrated bulk and single-cell transcriptomic analysis. Yao Jiang published this 27.3 KB dataset on figshare in April 2026. The data underpins a risk model that segments patients into high- and low-risk groups and includes findings on immune cell infiltration and somatic mutations.

Use Cases

• Building a prognostic risk model for ccRCC based on the six identified ammonia metabolism-related genes.
• Analyzing differential immune cell infiltration between patient risk groups based on the 16 identified immune cell types.
• Investigating cell communication networks in ccRCC, particularly between malignant cells and T cells, as suggested by the single-cell analysis.
• Validating gene expression patterns for the six prognostic genes using the described RT-qPCR results.

Strengths

• Identifies six specific prognostic genes (RGS20, ADA, AICDA, SLC12A5, RUFY4, CDK5RAP3) with RT-qPCR validation.
• Integrates findings from both bulk and single-cell transcriptomic analysis, as described in the methodology.
• Includes analysis of 16 immune cell types with differential abundance between patient risk groups.
• Released under a permissive CC-BY-4.0 license for reuse.

Limitations

• The dataset is very small at 27.3 KB, suggesting limited raw data or a summary table.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment for large-scale modeling.

Provenance

Source

figshare, author Yao Jiang.

Collection Method

Integrated analysis of public transcriptomic data for ccRCC and ammonia death-related genes, using machine learning for prognostic gene filtering.

Freshness

Last updated 2026-04-23 05:35:57; freshness should be verified.