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Updated: Jan 13, 2026

Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer

Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer

Published on: November 2, 2013

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

Ahmed Al Marouf¹, Tarek A Bismar^2,3,4,5,6, Sunita Ghosh⁷

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

|October 29, 2025

Summary

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:

XGBoost biomarker identification prostate cancer tissue microarray

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Last Updated: Jan 13, 2026

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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.