Hepatocellular Carcinoma Multi-Omics Data for a 12-Gene Prognostic Signature

A 12-gene random survival forest signature for hepatocellular carcinoma prognosis, derived from multi-cohort transcriptomic datasets using 101 machine-learning models. The dataset was created by Talaiti Tuergan and last updated on 2026-05-08. It integrates findings from preliminary proteomic, exploratory metabolomic, and single-cell RNA sequencing analyses.

Use Cases

• Validating the 12-gene prognostic signature for patient survival prediction based on transcriptomic data
• Investigating the role of the MCM7 gene in cellular senescence and immune evasion in HCC
• Analyzing the correlation between high-risk patient profiles and immunotherapy response potential based on TIDE analysis results

Strengths

• Signature was validated across independent patient cohorts.
• Analysis integrates multiple data types: transcriptomic, proteomic, metabolomic, and single-cell RNA sequencing.
• Functional validation was performed using in vitro assays in HepG2 cells.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset is small at 11.1 KB, indicating limited scope.

Provenance

Source

figshare

Collection Method

Integrating multi-cohort transcriptomic datasets and applying machine learning models, with validation from proteomic, metabolomic, scRNA-seq, and in vitro assays.

Freshness

Last updated 2026-05-08 05:56:47; freshness should be verified.