Table 6_Integrate single-cell and transcriptome analyses to explore the prognostic genes r

A figshare dataset by Qi Zhao, last updated in April 2026, containing results from an integrated analysis of single-cell and whole-genome transcriptomic data for bladder cancer. The study identified 220 candidate genes and constructed a risk model based on six prognostic marker genes (UNC93B1, FAM193B, POGLUT3, FBN1, MAP1B, RUNX2). It includes findings on TRPM4 overexpression, immune infiltration of 12 cell types, and differential responses to 112 drugs.

Use Cases

• Validate prognostic risk models based on the six identified marker genes (UNC93B1, FAM193B, POGLUT3, FBN1, MAP1B, RUNX2).
• Analyze immune cell infiltration patterns based on the 12 distinct immune cell types mentioned.
• Investigate drug sensitivity correlations based on the differential responses to 112 drugs, including WZ3105.
• Explore gene set enrichment for key pathways like the melanoma pathway, noted as significant in the description.

Strengths

• Includes specific, statistically significant findings (p < 0.05) for TRPM4 overexpression and pathway disparities.
• Identifies a concrete set of 220 candidate genes and six prognostic marker genes for model construction.
• Results from integrated multi-omics analysis (single-cell and transcriptome data).

Limitations

• Row count and column-level documentation are absent; field semantics must be inferred after download.
• The dataset is very small (11.5 KB), indicating limited scope, likely containing summary results rather than raw data.
• Description metadata is limited; actual data quality and completeness require manual inspection.

Provenance

Source

Qi Zhao via figshare.

Collection Method

Integrated analysis of single-cell and whole-genome transcriptomic data.

Time Range

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Freshness

Last updated 2026-04-13 05:21:47; freshness should be verified.

Geography

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