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Updated: Jun 29, 2026

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Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer
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Biomarkers That Differentiate Benign Prostatic Hyperplasia from Prostate Cancer: A Literature Review.
Christopher J McNally1,2, Mark W Ruddock1, Tara Moore2
1Randox Laboratories Ltd, Crumlin, Co. Antrim BT29 4QY, Northern Ireland.
Cancer Management and Research
|July 17, 2020
Summary
New biomarkers are needed to accurately detect prostate cancer in primary care, improving upon current tests like prostate-specific antigen (PSA). Multivariate models combining biomarkers and clinical data show promise for better patient stratification.
Area of Science:
- Urology
- Oncology
- Biomarker Discovery
Background:
- Current prostate cancer detection relies on serum total prostate-specific antigen (tPSA) and digital rectal examination, which have limited accuracy.
- These limitations lead to over-diagnosis and unnecessary invasive biopsies, highlighting a need for improved diagnostic tools.
- Distinguishing benign prostatic hyperplasia (BPH) from early-stage prostate cancer remains a clinical challenge.
Purpose of the Study:
- To review research from 2009-2019 on biomarkers for differentiating prostate cancer from BPH.
- To identify promising biomarkers and assess their potential for clinical application in primary care settings.
Main Methods:
- Systematic review of published research papers.
- Focus on studies reporting biomarkers identified in urine, serum, tissue, and semen.
- Evaluation of biomarker candidates for their ability to differentiate between prostate cancer and BPH.
Main Results:
- Hundreds of potential biomarkers were identified across various sample types.
- Many candidate biomarkers require further validation before clinical implementation.
- No single biomarker has proven definitively superior for differentiating prostate cancer from BPH.
Conclusions:
- Multivariate panels combining biomarker candidates with clinical parameters are essential for validation.
- Risk prediction calculators incorporating these panels can improve patient stratification in primary care.
- This approach offers tangible benefits for patients and healthcare systems by enabling more accurate diagnosis.

