ArticleBreast cancer (Tokyo, Japan)2022
A risk scoring system based on tumor microenvironment cells to predict prognosis and immune activity in triple-negative breast cancer.
Article in Breast cancer (Tokyo, Japan), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Ultrasound genomics related mitochondrial gene signature for prognosis and neoadjuvant chemotherapy resistance in triple negative breast cancer.Oncology research · 2025Article
- Construction of a prognostic survival model with tumor immune-related genes for breast cancer.Translational cancer research · 2024Article
- Advances in Immunotherapy for Endometrial Cancer: Insights into MMR Status and Tumor Microenvironment.Cancers · 2024Review
- GPR81-mediated reprogramming of glucose metabolism contributes to the immune landscape in breast cancer.Discover oncology · 2023Article
- Subclassification of B-acute lymphoblastic leukemia according to age, immunophenotype and microenvironment, predicts MRD risk in Mexican children from vulnerable regions.Frontiers in oncology · 2023Article
- Combination of Immune-Related Network and Molecular Typing Analysis Defines a Three-Gene Signature for Predicting Prognosis of Triple-Negative Breast Cancer.Biomolecules · 2022Article
- Review
- Systematic evaluation of tumor microenvironment and construction of a machine learning model to predict prognosis and immunotherapy efficacy in triple-negative breast cancer based on data mining and sequencing validation.Frontiers in pharmacology · 2022Article
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9 authors.
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Abstract
The tumor microenvironment (TME) interacting with the malignant cells plays a vital role in cancer development. Herein, we aim to establish and verify a scoring system based on the characteristics of TME cells for prognosis prediction and personalized treatment guidance in patients with triple-negative breast cancer (TNBC). 158 TNBC samples from The Cancer Genome Atlas (TCGA) were included as the training cohort, and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) (N = 297), as well as GSE58812 (N = 107), were included as the validation cohort. The enrichment scores of 64 immune and stromal cells were estimated by the xCell algorithm. In the training cohort, cells with prognostic significance were found out using univariate Cox regression analysis and further applied to the random survival forest (RSF) model. Based on the scores of M2 macrophages, CD8
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