ArticleDiscover oncology2025
Characterization of stem cell landscape and identification of stemness-relevant prognostic gene signature to aid immunotherapy in breast cancer.
Article in Discover oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Construction of a novel signature based on CRISPR-Cas9 screening for prognostic prediction in breast cancer.BMC cancer · 2026Article
- Novel Perspective for Prognostic Stratification and Personalized Therapy in Breast Cancer Patients: Development of Cancer Stem Cells and Metabolism-Associated Prognostic Model.International journal of women's health · 2026Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A common digestive system cancer with a dismal prognosis and a high death rate globally is breast cancer (BRCA). BRCA recurrence, metastasis, and medication resistance are all significantly impacted by cancer stem cells (CSCs). However, the relationship between CSCs and the tumor microenvironment in BRCA individuals remains unknown, and this information is critically needed. Our research utilized bioinformatics techniques and TCGA data to explore the complex relationship between CSCs and BRCA development. We identified 26 stem cell gene sets from the Stem Checker database and classified BRCA samples into stemness subtypes using consensus clustering. Prognosis, tumor microenvironment (TME) elements, and treatment responses varied across subtypes. Using LASSO, Cox regression, and differential expression analysis, we developed a stemness-risk model. BRCA patients were divided into two groups (Cluster A and Cluster B). Cluster B exhibited an improved prognosis, higher PIK3CA mutation frequency, and increased levels of CD8 T cells and regulatory Tregs. A 5-gene stemness model was constructed, showing that higher stemness scores correlated with poorer prognosis. The model was validated using the METABRIC cohort data from cBioPortal. Our findings identify two stemness-related subgroups with distinct prognoses and TME patterns. Further experimental validation is necessary before this model can be considered for clinical application.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.