Table 1_Multi-omics analysis identifies stemness-driven molecular subtypes, prognostic sig

A 2026 study by Huifang Du analyzes stemness features in Wilms tumor using multi-omics data and computational biology. The dataset likely contains results from single-cell RNA sequencing, consensus clustering, prognostic risk modeling, and drug repositioning analysis. It identifies two molecular subtypes, a prognostic signature, an epigenetic regulator APCDD1, and a drug candidate Leflunomide.

Use Cases

• Stratifying Wilms tumor patients into molecular subtypes based on stemness-related genes.
• Developing prognostic risk models for Wilms tumor using Lasso-Cox regression.
• Investigating epigenetic regulation of tumor-suppressive genes like APCDD1.
• Identifying drug candidates for high-risk patients via integrative repositioning analysis.
• Validating drug effects on cell proliferation, migration, and apoptosis through in vitro assay results.

Strengths

• Analysis integrates multi-omics data and computational biology approaches.
• Prognostic signature and drug candidate are validated through in vitro functional assays.
• Dataset is openly licensed under CC-BY-4.0.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The 10.2 KB file size suggests a limited scope, likely containing summary results rather than raw omics data.

Provenance

Source

figshare

Collection Method

Likely contains computational analysis results from a research study.

Freshness

Last updated 2026-05-25 14:31:28; freshness should be verified.