Table 2_Identification of a prognostic signature based on ammonia metabolism-related genes

A 2026 study by Yao Jiang presents a prognostic model for clear cell renal cell carcinoma (ccRCC) based on six ammonia metabolism-related genes. The model stratifies patients into high- and low-risk groups and includes analyses of immune infiltration, somatic mutations, and single-cell interactions. The underlying data, shared under a CC-BY-4.0 license, is a 9.9 KB Excel file containing the results of this integrated transcriptomic analysis.

Use Cases

• Validating the prognostic six-gene signature (RGS20, ADA, AICDA, SLC12A5, RUFY4, CDK5RAP3) in independent ccRCC cohorts.
• Investigating immune cell infiltration differences between high- and low-risk patient groups as described in the analysis.
• Studying cell communication patterns, particularly between malignant cells and T cells, using the single-cell transcriptomics results.
• Exploring the association between the 'olfactory transduction' signaling pathway and ccRCC prognosis as suggested by enrichment analysis.

Strengths

• Model is based on an integrated analysis of both bulk and single-cell transcriptomic data.
• Identifies six specific prognostic genes (RGS20, ADA, AICDA, SLC12A5, RUFY4, CDK5RAP3) with RT-qPCR validation mentioned.
• Includes multiple analytical layers: functional enrichment, immune infiltration, and somatic mutation analysis.

Limitations

• The dataset is very small (9.9 KB), suggesting it contains summary results rather than raw expression data.
• Row count and column-level documentation are unknown, limiting assessment of data structure and granularity.
• Data freshness should be verified, as the last update timestamp is set in the future (2026-04-23).

Provenance

Source

figshare, author Yao Jiang.

Collection Method

Integrated analysis of public transcriptomic data using differential expression, machine learning, and single-cell techniques.

Freshness

Last updated 2026-04-23 05:36:01.