Table 1_Identification of a prognostic signature based on ammonia metabolism-related genes

A prognostic model for clear cell renal cell carcinoma (ccRCC) based on six ammonia metabolism-related genes. The dataset likely contains results from an integrated analysis of bulk and single-cell transcriptomic data, including risk scores, gene expression, and associated analyses. It was authored by Yao Jiang and last updated on April 23, 2026.

Use Cases

• Validating the prognostic power of the six identified genes (RGS20, ADA, AICDA, SLC12A5, RUFY4, CDK5RAP3) in ccRCC.
• Investigating immune cell infiltration differences between high-risk and low-risk patient groups.
• Studying cell communication patterns, particularly between malignant cells and T cells, in ccRCC tumor microenvironments.
• Analyzing functional enrichment pathways, such as the 'olfactory transduction' signaling pathway, associated with patient risk groups.

Strengths

• The model is based on an integrated analysis of both bulk and single-cell transcriptomic data.
• The prognostic signature consists of six specific genes validated by RT-qPCR (p < 0.05).
• Analysis includes multiple dimensions: functional enrichment, immune infiltration, and somatic mutation profiling.
• The dataset is published under a permissive CC-BY-4.0 license.

Limitations

• The dataset is very small (14.9 KB), suggesting it contains summary results rather than raw expression data.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment for some analyses.

Provenance

Source

figshare

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 05:36:00; freshness should be verified.