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  1. Home
  3. Leveraging Machine Learning For Severity Level-wise Biomarker Identification In Prostate Cancer Microarray Gene Expression Data.
  1. Home
  3. Leveraging Machine Learning For Severity Level-wise Biomarker Identification In Prostate Cancer Microarray Gene Expression Data.

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Leveraging Machine Learning for Severity Level-Wise Biomarker Identification in Prostate Cancer Microarray Gene

Ahmed Al Marouf1, Tarek A Bismar2,3,4,5,6, Sunita Ghosh7

  • 1Department of Computer Science, University of Calgary, Calgary, AB T2N 1N4, Canada.

Biomedicines
|October 29, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

Machine learning models accurately identified biomarkers for prostate cancer grading groups, achieving 96.85% accuracy with XGBoost. This approach aids in distinguishing cancer severity for improved treatment guidance.

Keywords:
XGBoostbiomarker identificationprostate cancertissue microarray

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Area of Science:

  • Computational biology
  • Genomics
  • Oncology

Background:

  • Prostate cancer is a prevalent cancer in men, making its accurate detection and treatment crucial.
  • The Gleason Grading Group (GGC) score is vital for determining prostate cancer severity and guiding treatment decisions.

Purpose of the Study:

  • To develop and validate a machine learning (ML) framework for identifying potential biomarkers associated with different Gleason Grading Group (GGC) scores in prostate cancer.
  • To map prostate cancer GGC scores into five distinct severity levels: low, intermediate-low, intermediate, intermediate-high, and high.

Main Methods:

  • Utilized traditional ML classification methods including Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and XGBoost (XGB).
  • Employed a framework incorporating missing value imputation, SMOTE-Tomek link for class imbalance, and stratified k-fold cross-validation for robust biomarker selection.
  • Microarray data from immunohistochemical tests on 1119 prostate cancer samples were used for framework evaluation.

    • Main Results:

    • The ML framework successfully distinguished critical biomarkers for various prostate cancer severity levels.
    • The XGBoost method achieved a high accuracy of 96.85% in classifying GGC scores.
    • Four experimental setups using combinations of high- and low-aggressive signatures demonstrated the approach's effectiveness.

    Conclusions:

    • Machine learning provides a robust platform for identifying prostate cancer biomarkers, supporting domain expert involvement.
    • The study's satisfactory results suggest potential for a physician-in-the-loop approach to enhance diagnostic impact in future research.