TMSB10-Driven Cell State: Multi-Omics and Radiomics for Breast Cancer Assessment

A research dataset integrating single-cell transcriptomics, spatial transcriptomics, bulk transcriptomics, genomic, and MRI radiomic data to delineate tumor cell heterogeneity in breast cancer. The study, authored by Gui-Xin Wang and last updated in April 2026, identifies a poor-prognosis tumor cell cluster (C1) and develops machine learning models for non-invasive assessment and risk stratification.

Use Cases

• Developing non-invasive MRI radiomic models to estimate tumor cluster abundance based on the described methodology.
• Building prognostic signatures for breast cancer risk stratification using genetic features derived from the C1 cluster.
• Studying tumor cell heterogeneity and metabolic reprogramming (OXPHOS/glycolysis) through integrated multi-omics data.
• Investigating the role of the TMSB10 gene in promoting cancer cell proliferation, migration, and invasion as validated in vitro.

Strengths

• Integrates five distinct data modalities: single-cell transcriptomics, spatial transcriptomics, bulk transcriptomics, genomic, and MRI radiomics.
• Includes in vitro experimental validation of the functional role of the core gene TMSB10.
• Machine learning models for prognosis and assessment were validated across multiple cohorts.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The primary file is a 5.7 MB DOCX document, which may not be a standard data format and requires extraction.

Provenance

Source

figshare

Collection Method

Data integration from multiple omics and imaging sources, with in vitro validation.

Time Range

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Freshness

Last updated 2026-04-20 05:22:51; freshness should be verified.

Geography

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