ArticleClinical and experimental medicine2023
A novel model associated with tumor microenvironment on predicting prognosis and immunotherapy in triple negative breast cancer.
Article in Clinical and experimental medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it, 7 citations in OpenAlex.
- Survival prediction landscape: an in-depth systematic literature review on activities, methods, tools, diseases, and databases.Frontiers in artificial intelligence · 2024Pooled it
- PatchSight-ImmuneMap-LifeSpan as a unified AI framework for breast cancer diagnosis, immune profiling and prognostic prediction.Discover oncology · 2026Article
- Natural Killer Cell Activity and Response to Neoadjuvant Treatment in Breast Cancer Patients.International journal of molecular sciences · 2025Article
- Current Status of Breast Cancer Immunotherapy and Prognosis-Related Markers.Breast cancer (Dove Medical Press) · 2025Review
- Advances in the application of computational pathology in diagnosis, immunomicroenvironment recognition, and immunotherapy evaluation of breast cancer: a narrative review.Journal of cancer research and clinical oncology · 2023Review
- Radiomic Models Predict Tumor Microenvironment Using Artificial Intelligence-the Novel Biomarkers in Breast Cancer Immune Microenvironment.Technology in cancer research & treatmentReview
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Triple negative breast cancer (TNBC) is the most aggressive and malignant subtype in breast cancer. Immunotherapy is a currently promising and effective treatment for TNBC, while not all patients are responsive. Therefore, it is necessary to explore novel biomarkers to screen sensitive populations for immunotherapy. All mRNA expression profiles of TNBC from The Cancer Genome Atlas (TCGA) database were clustered into two subgroups by analyzing tumor immune microenvironment (TIME) with single sample gene set enrichment analysis (ssGSEA). A risk score model was constructed based on differently expressed genes (DEGs) identified from two subgroups using Cox and Least Absolute Shrinkage and Selector Operation (LASSO) regression model. And it was validated by Kaplan-Meier analysis and Receiver Operating Characteristic (ROC) analysis in Gene Expression Omnibus (GEO) and the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) databases. Multiplex immunofluorescence (mIF) and Immunohistochemical (IHC) staining were performed on clinical TNBC tissue samples. The relationship between risk score and immune checkpoint blockades (ICB) related signatures was further investigated, as well as the biological processes were performed by gene set enrichment analysis (GSEA). We obtained three DEGs positively related to prognosis and infiltrating immune cells in TNBC. Our risk score model could be an independent prognostic factor and the low risk group exhibited a prolonged overall survival (OS). Patients in low risk group were more likely to present a higher immune infiltration and stronger response to immunotherapy. GSEA revealed the model was associated with immune-related pathways. We constructed and validated a novel model based on three prognostic genes related to TIME in TNBC. The model contributed a robust signature that could predict the prognosis in TNBC, especially for the efficacy of immunotherapy.
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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 OpenQuestion graph.