Hepatocellular Carcinoma Multi-Omics Data for Senescence and Prognostic Modeling

A 12-gene random survival forest prognostic signature was developed from multi-cohort transcriptomic datasets using 101 machine-learning models. The data, authored by Talaiti Tuergan and last updated in May 2026, integrates transcriptomic, preliminary proteomic, exploratory metabolomic, and single-cell RNA sequencing analyses to explore a senescence-associated molecular axis in hepatocellular carcinoma (HCC). Validation was performed using RT–qPCR, Western blotting, immunohistochemistry, and multiplex immunofluorescence on a clinical HCC cohort.

Use Cases

• Developing prognostic survival models based on the described 12-gene signature.
• Studying the role of the MCM7 gene in cellular senescence and T-cell proliferation within the tumor microenvironment.
• Analyzing multi-omics correlations between transcriptomic, proteomic, and metabolomic alterations in HCC.
• Investigating immunotherapy response predictors based on TIDE analysis results mentioned in the description.

Strengths

• Integrates multiple data types, including transcriptomic, proteomic, metabolomic, and single-cell RNA sequencing analyses.
• Prognostic signature was validated across independent clinical cohorts using techniques like RT–qPCR and immunohistochemistry.
• The analysis employed 101 machine-learning models to develop a robust signature.

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 very small (6.1 KB), suggesting it may contain summary or metadata rather than the full underlying multi-omics data.

Provenance

Source

figshare, author Talaiti Tuergan.

Collection Method

Integrated multi-cohort transcriptomic datasets, with validation from a clinical HCC cohort and in vitro assays in HepG2 cells.

Freshness

Last updated 2026-05-08 05:56:46; freshness should be verified.