ArticleFrontiers in immunology2026
Integrative multi-omics and radiomics reveal a TMSB10-driven cell state for non-invasive assessment and precision stratification in breast cancer.
Article in Frontiers in immunology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Integrative bulk and single-cell transcriptomic analysis identify an ac4C-related signature in lung adenocarcinoma.Journal of Cancer · 2026Article
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11 authors.
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Abstract
Background: Tumor cell heterogeneity is a fundamental driver of breast cancer aggressiveness, underlying recurrence, metastasis, and therapy resistance. Understanding the biological characteristics and functions of specific tumor cell clusters in the tumor microenvironment is crucial for advancing precision oncology. Methods: We delineated breast cancer tumor cell heterogeneity by integrating single-cell transcriptomics, spatial transcriptomics, bulk transcriptomics, genomic and radiomic data. The oncogenic functions of the candidate gene Results: We discovered a poor-prognosis tumor cell cluster (C1 cluster). C1 cluster exhibited a late evolutionary state, metabolic reprogramming (OXPHOS/glycolysis), and active crosstalk with cancer-associated fibroblasts and endothelial cells. High abundance of C1 cluster was associated with poor survival, specific somatic mutations, and predicted superior response to immune checkpoint blockade, but not to chemo/radiotherapy. The radiomic model based on MRI images was exploratively established for estimating the abundance of C1, and the prognostic model based on C1-derived genetic features significantly stratified the survival risk of breast cancer in multiple cohorts. Conclusions: This study revealed C1 cluster as a key driver of breast cancer progression and its application for predicting immunotherapy response. Additionally,
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