Data Sheet 2_Integrated bulk and single-cell transcriptomics reveals TREH as a novel prote

Transcriptomic data from a study identifying Trehalase (TREH) as a protective biomarker in clear cell renal cell carcinoma (ccRCC). The dataset includes results from integrated Weighted Gene Co-expression Network Analysis (WGCNA) and machine learning algorithms (LASSO and GBM) applied to the TCGA-KIRC cohort. It was authored by Kuo Ma and last updated on May 11, 2026.

Use Cases

• Validate prognostic biomarkers for ccRCC based on integrated transcriptomic analysis.
• Study the relationship between gene expression (e.g., TREH) and clinical outcomes like survival and metastasis.
• Analyze cell-type-specific gene expression patterns in ccRCC using single-cell RNA-seq data referenced in the study.
• Investigate signaling pathways like VEGF, PLG, and APP in the context of epithelial cell crosstalk within the tumor microenvironment.

Strengths

• Data is derived from the TCGA-KIRC cohort, a well-established cancer genomics resource.
• Analysis integrates multiple methods: WGCNA, LASSO, GBM, and single-cell RNA-seq validation.
• Results are validated through in vitro and in vivo assays as described in the study.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The dataset is small (278.0 KB), indicating limited scope, likely containing summary results rather than raw sequencing data.

Provenance

Source

TCGA-KIRC cohort and associated single-cell RNA-seq analysis.

Collection Method

Integrated transcriptomic data analysis combining WGCNA with machine learning algorithms (LASSO and GBM).

Freshness

Last updated 2026-05-11 04:26:43; freshness should be verified.