Table 1_Machine learning-driven multi-omics integration uncovers a senescence associated m

Table 1_Machine learning-driven multi-omics integration uncovers a senescence associated molecular axis in HCC.xlsx is a dataset from a study integrating multi-cohort transcriptomic data to develop a prognostic signature for hepatocellular carcinoma (HCC). The work, authored by Talaiti Tuergan and last updated on 2026-05-08, employed 101 machine-learning models and validated findings with proteomic, metabolomic, and single-cell RNA sequencing analyses.

Use Cases

• Validate the 12-gene random survival forest prognostic signature for HCC patient survival prediction based on the described multi-cohort transcriptomic integration.
• Investigate the role of the MCM7 gene as a senescence-associated driver in HCC progression based on the study's functional validation.
• Analyze the relationship between high-risk patient profiles and immune evasion potential using the TIDE analysis framework mentioned in the results.
• Explore multi-omics alterations linking transcriptomic, proteomic, and metabolomic data as described in the study's methodological integration.

Strengths

• The underlying analysis integrated data from multiple independent cohorts, which likely improves the robustness of the findings.
• The study employed a substantial number of machine-learning models (101) to develop its prognostic signature.
• Validation used multiple experimental techniques, including RT–qPCR, Western blotting, and single-cell RNA sequencing.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment for specific analytical tasks.
• The dataset is small (876.4 KB), indicating limited scope, likely containing summary results rather than raw multi-omics data.

Provenance

Source

Talaiti Tuergan via figshare

Collection Method

Integrated analysis of multi-cohort transcriptomic datasets, with validation from proteomic, metabolomic, and single-cell RNA sequencing analyses.

Freshness

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