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Related Experiment Video

Updated: Dec 17, 2025

Studying Triple Negative Breast Cancer Using Orthotopic Breast Cancer Model
09:29

Studying Triple Negative Breast Cancer Using Orthotopic Breast Cancer Model

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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.

Scientific Reports
|June 28, 2020
PubMed
Summary
This summary is machine-generated.

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.

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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.