Table 2_Heterogeneity-based stratification identifies CKMT2 as a prognostic marker in oste

A bioinformatics analysis integrating single-cell and multi-cohort transcriptomic data to map intratumoral heterogeneity in osteosarcoma. The dataset likely contains results from a machine learning model identifying ten critical genes and validates CKMT2 as a prognostic biomarker. Authored by Xuesong Li and last updated on 2026-05-26, this 8.6 KB Excel file is shared under a CC-BY-4.0 license.

Use Cases

• Validate prognostic risk models for osteosarcoma based on the ten-gene signature identified through machine learning.
• Analyze the correlation between molecular subtypes and clinical prognosis or immune infiltration as described in the study.
• Investigate the functional role of CKMT2 in osteosarcoma cell proliferation using the in vitro assay results referenced.
• Compare intratumoral heterogeneity stratification methods using the malignant subtype classifications derived from single-cell data.

Strengths

• Integrates single-cell transcriptomic data with multi-cohort datasets from public repositories, suggesting a multi-source validation approach.
• Identifies a specific prognostic marker, CKMT2, validated through immunohistochemistry and in vitro functional assays.
• The dataset is small (8.6 KB), facilitating quick download and initial inspection.

Limitations

• Row count and column-level documentation are unknown, which limits suitability assessment for specific analyses.
• The description is detailed but does not specify the exact variables or sample size contained in the Excel file.
• Data may reflect bias inherent to the specific public repositories and cohorts used in the original study.

Provenance

Source

figshare, authored by Xuesong Li.

Collection Method

Bioinformatics pipeline analyzing integrated single-cell and bulk transcriptomic data, followed by machine learning modeling and experimental validation.

Freshness

Last updated 2026-05-26 09:03:35; freshness should be verified.