ArticleScientific reports2020
Machine learning analysis identifies genes differentiating triple negative breast cancers.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it, 47 citations in OpenAlex.
- Unraveling Biomarker Signatures in Triple-Negative Breast Cancer: A Systematic Review for Targeted Approaches.International journal of molecular sciences · 2024Pooled it
- Strategies for constructing context-specific protein-protein interaction networks.Briefings in bioinformatics · 2026Review
- Dual Immunological Prognostic Models for Risk Stratification and Treatment Insights in Triple-Negative Breast Cancer.International journal of molecular sciences · 2026Article
- An Intelligent Hybrid Ensemble Model for Early Detection of Breast Cancer in Multidisciplinary Healthcare Systems.Diagnostics (Basel, Switzerland) · 2026Article
- Artificial intelligence for triple-negative breast cancer from imaging to multi-omics.Frontiers in oncology · 2026Review
- Innovative Approaches to EMT-Related Biomarker Identification in Breast Cancer: Multi-Omics and Machine Learning Methods.Biotech (Basel (Switzerland)) · 2025Review
- Application of Virtual Drug Study to New Drug Research and Development: Challenges and Opportunity.Clinical pharmacokinetics · 2024Review
- Advancements in triple-negative breast cancer sub-typing, diagnosis and treatment with assistance of artificial intelligence : a focused review.Journal of cancer research and clinical oncology · 2024Review
- Gene expression profiling and the isocitrate dehydrogenase mutational landscape of temozolomide‑resistant glioblastoma.Oncology letters · 2024Article
- Evaluation of lncRNAs as Potential Biomarkers for Diagnosis of Metastatic Triple-Negative Breast Cancer through Bioinformatics and Machine Learning.Iranian journal of biotechnology · 2024Article
- Article
- Artificial intelligence: opportunities and challenges in the clinical applications of triple-negative breast cancer.British journal of cancer · 2023Review
- GeneFriends: gene co-expression databases and tools for humans and model organisms.Nucleic acids research · 2023Article
- INO80 function is required for mouse mammary gland development, but mutation alone may be insufficient for breast cancer.Frontiers in cell and developmental biology · 2023Article
- ROSIE: RObust Sparse ensemble for outlIEr detection and gene selection in cancer omics data.Statistical methods in medical research · 2022Article
- Lactadherin: From a Well-Known Breast Tumor Marker to a Possible Player in Extracellular Vesicle-Mediated Cancer Progression.International journal of molecular sciences · 2022Review
- Eupalinolide O Induces Apoptosis in Human Triple-Negative Breast Cancer Cells via Modulating ROS Generation and Akt/p38 MAPK Signaling Pathway.Journal of oncology · 2022Article
- Machine learning assisted analysis of breast cancer gene expression profiles reveals novel potential prognostic biomarkers for triple-negative breast cancer.Computational and structural biotechnology journal · 2022Article
- Molecular Classification Models for Triple Negative Breast Cancer Subtype Using Machine Learning.Journal of personalized medicine · 2021Article
- Article
Corrections and comments
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Authors and funding
10 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Triple negative breast cancer (TNBC) is one of the most aggressive form of breast cancer (BC) with the highest mortality due to high rate of relapse, resistance, and lack of an effective treatment. Various molecular approaches have been used to target TNBC but with little success. Here, using machine learning algorithms, we analyzed the available BC data from the Cancer Genome Atlas Network (TCGA) and have identified two potential genes, TBC1D9 (TBC1 domain family member 9) and MFGE8 (Milk Fat Globule-EGF Factor 8 Protein), that could successfully differentiate TNBC from non-TNBC, irrespective of their heterogeneity. TBC1D9 is under-expressed in TNBC as compared to non-TNBC patients, while MFGE8 is over-expressed. Overexpression of TBC1D9 has a better prognosis whereas overexpression of MFGE8 correlates with a poor prognosis. Protein-protein interaction analysis by affinity purification mass spectrometry (AP-MS) and proximity biotinylation (BioID) experiments identified a role for TBC1D9 in maintaining cellular integrity, whereas MFGE8 would be involved in various tumor survival processes. These promising genes could serve as biomarkers for TNBC and deserve further investigation as they have the potential to be developed as therapeutic targets for TNBC.
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