ArticleBMC cancer2020
Integrative analysis of breast cancer profiles in TCGA by TNBC subgrouping reveals novel microRNA-specific clusters, including miR-17-92a, distinguishing basal-like 1 and basal-like 2 TNBC subtypes.
Article in BMC cancer, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 26 citations in OpenAlex.
- Role of tumor‑infiltrating lymphocytes and miR‑155 in breast cancer: Insights into carcinogenesis and their potential as prognostic biomarkers (Review).Oncology reports · 2026Review
- Review
- Unveiling Novel miRNA-mRNA Interactions and Their Prognostic Roles in Triple-Negative Breast Cancer: Insights into miR-210, miR-183, miR-21, and miR-181b.International journal of molecular sciences · 2025Article
- MicroRNAs Associated with Androgen Receptor and Metastasis in Triple-Negative Breast Cancer.Cancers · 2024Article
- An integrated analysis of microRNAs regulating DNA damage response in triple-negative breast cancer.Breast cancer (Tokyo, Japan) · 2023Article
- Breast Cancer Subtype-Specific miRNAs: Networks, Impacts, and the Potential for Intervention.Biomedicines · 2022Review
- Global Increase in Breast Cancer Incidence: Risk Factors and Preventive Measures.BioMed research international · 2022Review
- Review
- Intraoperative opioids are associated with improved recurrence-free survival in triple-negative breast cancer.British journal of anaesthesia · 2021Article
- Modulatory Role of microRNAs in Triple Negative Breast Cancer with Basal-Like Phenotype.Cancers · 2020Review
- Vitamin D in Triple-Negative and BRCA1-Deficient Breast Cancer-Implications for Pathogenesis and Therapy.International journal of molecular sciences · 2020Review
- Cellular Mechanisms Triggered by the Cotreatment of Resveratrol and Doxorubicin in Breast Cancer: A Translational In Vitro-In Silico Model.Oxidative medicine and cellular longevity · 2020Article
- Integrative Network Fusion: A Multi-Omics Approach in Molecular Profiling.Frontiers in oncology · 2020Article
- A Machine Learning Approach to Differentiate Two Specific Breast Cancer Subtypes Using Androgen Receptor Pathway Genes.Technology in cancer research & treatmentArticle
Corrections and comments
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Authors and funding
5 authors at 1 institution in 2 countries.
Funding
Abstract
backgroundThe term triple-negative breast cancer (TNBC) is used to describe breast cancers without expression of estrogen receptor, progesterone receptor or HER2 amplification. To advance targeted treatment options for TNBC, it is critical that the subtypes within this classification be described in regard to their characteristic biology and gene expression. The Cancer Genome Atlas (TCGA) dataset provides not only clinical and mRNA expression data but also expression data for microRNAs.
resultsIn this study, we applied the Lehmann classifier to TCGA-derived TNBC cases which also contained microRNA expression data and derived subtype-specific microRNA expression patterns. Subsequent analyses integrated known and predicted microRNA-mRNA regulatory nodes as well as patient survival data to identify key networks. Notably, basal-like 1 (BL1) TNBCs were distinguished from basal-like 2 TNBCs through up-regulation of members of the miR-17-92 cluster of microRNAs and suppression of several known miR-17-92 targets including inositol polyphosphate 4-phosphatase type II, INPP4B.
conclusionsThese data demonstrate TNBC subtype-specific microRNA and target mRNA expression which may be applied to future biomarker and therapeutic development studies.
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