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Updated: Dec 17, 2025
Studying Triple Negative Breast Cancer Using Orthotopic Breast Cancer Model
Published on: March 20, 2020
Machine learning analysis identifies genes differentiating triple negative breast cancers
Charu Kothari1,2, Mazid Abiodoun Osseni1,3, Lynda Agbo1,2
1Département de Médecine Moléculaire, Faculté de médecine, Université Laval, Québec City, QC, Canada.
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.
Insights
Machine learning identified TBC1D9 and MFGE8 genes to distinguish triple negative breast cancer (TNBC). TBC1D9 shows promise as a biomarker and therapeutic target for TNBC, while MFGE8 indicates poor prognosis.
Area of Science:
- Oncology
- Genomics
- Bioinformatics
Background:
- Triple negative breast cancer (TNBC) is an aggressive subtype of breast cancer with high mortality.
- Current treatments for TNBC are limited, necessitating novel therapeutic strategies and biomarkers.
- TNBC exhibits significant heterogeneity, complicating diagnosis and treatment.
Purpose of the Study:
- To identify novel molecular targets for triple negative breast cancer (TNBC) using machine learning.
- To discover potential biomarkers capable of differentiating TNBC from other breast cancer subtypes.
- To explore the therapeutic potential of identified genes in TNBC.
Main Methods:
- Analysis of The Cancer Genome Atlas Network (TCGA) breast cancer data using machine learning algorithms.
- Gene expression profiling to compare TNBC and non-TNBC samples.
- Protein-protein interaction studies including affinity purification mass spectrometry (AP-MS) and proximity biotinylation (BioID).
Main Results:
- Two genes, TBC1D9 and MFGE8, were identified as potential discriminators for TNBC.
- TBC1D9 is underexpressed in TNBC, and its overexpression correlates with better prognosis.
- MFGE8 is overexpressed in TNBC, and its overexpression is associated with poor prognosis.
- TBC1D9 plays a role in cellular integrity, while MFGE8 is involved in tumor survival processes.
Conclusions:
- TBC1D9 and MFGE8 show potential as diagnostic biomarkers for TNBC.
- These genes represent promising candidates for developing targeted therapies against TNBC.
- Further investigation is warranted to validate TBC1D9 and MFGE8 as therapeutic targets for TNBC.
