ArticleFrontiers in pharmacology2023
Comprehensively analysis of immunophenotyping signature in triple-negative breast cancer patients based on machine learning.
Article in Frontiers in pharmacology, 2023. 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.
- Triple-Negative Breast Cancer: Molecular Subtypes; Immune Escape; Limitations of Current Immunotherapy; and the BTLA/HVEM/CD160 Axis as an Emerging Target.Current issues in molecular biology · 2026Review
- Development of a machine learning-based risk prediction model and analysis of risk factors for docetaxel-induced bone marrow suppression in breast cancer patients.International journal of clinical pharmacy · 2025Article
- BRCAGenie: A machine learning-driven 43-gene polygenic risk score model for precision prediction of breast cancer survival.Journal of translational medicine · 2025Article
- The predictive effect of the CD155-TIGIT immune checkpoint axis complex on neoadjuvant chemotherapy efficacy in triple-negative breast cancer: A preliminary study.Cancer biomarkers : section A of Disease markers · 2025Article
- Recent Advances in the Application of Cucurbitacin B as an Anticancer Agent.International journal of molecular sciences · 2025Review
- Assessment of Untargeted Metabolomics by Hydrophilic Interaction Liquid Chromatography-Mass Spectrometry to Define Breast Cancer Liquid Biopsy-Based Biomarkers in Plasma Samples.International journal of molecular sciences · 2024Article
- A multi-organ map of the human immune system across age, sex and ethnicity.bioRxiv : the preprint server for biology · 2024Article
- Identification of CD160-TM as a tumor target on triple negative breast cancers: possible therapeutic applications.Breast cancer research : BCR · 2024Article
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11 authors.
Funding
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Abstract
Immunotherapy is a promising strategy for triple-negative breast cancer (TNBC) patients, however, the overall survival (OS) of 5-years is still not satisfactory. Hence, developing more valuable prognostic signature is urgently needed for clinical practice. This study established and verified an effective risk model based on machine learning methods through a series of publicly available datasets. Furthermore, the correlation between risk signature and chemotherapy drug sensitivity were also performed. The findings showed that comprehensive immune typing is highly effective and accurate in assessing prognosis of TNBC patients. Analysis showed that IL18R1, BTN3A1, CD160, CD226, IL12B, GNLY and PDCD1LG2 are key genes that may affect immune typing of TNBC patients. The risk signature plays a robust ability in prognosis prediction compared with other clinicopathological features in TNBC patients. In addition, the effect of our constructed risk model on immunotherapy response was superior to TIDE results. Finally, high-risk groups were more sensitive to MR-1220, GSK2110183 and temsirolimus, indicating that risk characteristics could predict drug sensitivity in TNBC patients to a certain extent. This study proposes an immunophenotype-based risk assessment model that provides a more accurate prognostic assessment tool for patients with TNBC and also predicts new potential compounds by performing machine learning algorithms.
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