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Related Experiment Video

Updated: Sep 16, 2025

Detection of Human Leukocyte Antigen Biomarkers in Breast Cancer Utilizing Label-free Biosensor Technology
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Biomarker discovery for early breast cancer diagnosis using machine learning on transcriptomic data for biosensor

Kalaumari Mayoral-Peña1, Omar Israel González Peña2, Natalie Artzi3

  • 1School of Engineering and Sciences, Campus Queretaro, Tecnologico de Monterrey, Queretaro, 76130, Mexico; Department of Medicine, Division of Engineering in Medicine, Brigham and Women's, Hospital Harvard Medical School, Boston, MA, 02115, USA.

Computers in Biology and Medicine
|July 11, 2025
PubMed
Summary

This study developed a bioinformatics pipeline using machine learning to find genetic biomarkers for breast cancer classification. These biomarkers show potential for early detection and improved biosensor diagnostics, especially in low-income regions.

Keywords:
Artificial intelligenceBioinformaticsFactorial designGene selectionGenetic algorithmsMolecular classificationOncologySurfaceomeTranscriptomicsTransmembrane genesTriple negative breast cancer

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Area of Science:

  • Bioinformatics and Computational Biology
  • Genomics and Molecular Biology
  • Oncology and Cancer Research

Background:

  • Breast cancer is a leading cause of death globally, necessitating advanced diagnostic tools for early detection.
  • Biosensors utilizing reliable biomarkers are crucial, particularly for resource-limited settings.
  • Accurate classification of breast cancer subtypes (non-malignant, non-triple-negative, triple-negative) is vital for effective treatment.

Purpose of the Study:

  • To identify novel genetic biomarkers for classifying breast cancer subtypes using a bioinformatics pipeline.
  • To evaluate the performance of different gene selection approaches and machine learning algorithms.
  • To assess the predictive power of identified biomarkers for patient survival and relapse.

Main Methods:

  • Developed a novel bioinformatics pipeline integrating machine learning algorithms (MLAs) and five Gene Selection Approaches (GSAs).
  • Employed LASSO, Membrane LASSO, Surfaceome LASSO, Network Analysis, and Feature Importance Score (FIS) for gene selection.
  • Utilized Recursive Feature Elimination (RFE) and Genetic Algorithms (GAs) to reduce gene sets while maintaining high classification performance (F1 Macro ≥80%).

Main Results:

  • Achieved high classification performance (F1 Macro or Accuracy 70.3%–97.2%) using selected gene sets.
  • Identified 13 genes with significant predictive capabilities for five-year survival and 4 genes for relapse-free survival.
  • Found overlap between identified genes and those in commercial diagnostic panels, validating their clinical relevance.

Conclusions:

  • The novel bioinformatics pipeline effectively identifies robust genetic biomarkers for breast cancer classification.
  • The identified biomarkers demonstrate potential for developing advanced biosensors for early breast cancer diagnosis and treatment monitoring.
  • This approach offers a promising strategy for improving breast cancer diagnostics, especially in underserved regions.