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Updated: Aug 10, 2025

Microarray-based Identification of Individual HERV Loci Expression: Application to Biomarker Discovery in Prostate Cancer
Published on: November 2, 2013
A Novel Modality Enables New Evidence-Based Individual Risk Stratification That Can Potentially Lead to Decisive
Meir Weksler1, Avi Simon1, Robert E Lenkinski2
1R&D Department, Prosight Ltd., Bay-Yam 5697439, Israel.
This study introduces a new method to assess prostate cancer (PCa) risk by measuring zinc levels in fresh biopsy samples. This technique improves diagnostic accuracy and allows for personalized risk stratification.
Area of Science:
- Oncology
- Biochemistry
- Medical Diagnostics
Background:
- Accurate risk stratification is crucial for managing suspected prostate cancer (PCa).
- Decreased zinc (Zn) levels are a known metabolic hallmark of PCa malignancy and aggressiveness.
- Previous methods for Zn measurement were limited by sample fixation requirements, hindering clinical adoption.
Purpose of the Study:
- To present a novel modality for PCa risk assessment based on Zn depletion.
- To address limitations of prior Zn measurement techniques for clinical application.
- To enable more precise risk stratification and treatment decisions for PCa patients.
Main Methods:
- Measurement of Zn levels in fresh prostate tissue samples during biopsy.
- Real-time interactive guidance during biopsy to improve sample quality.
- Estimation of Zn levels and gland compactness in scanned tissue for diagnosis.
- Development of a malignancy score for the entire prostate.
Main Results:
- The novel modality successfully measures Zn depletion in fresh biopsy samples.
- Real-time guidance improved biopsy quality.
- A comprehensive malignancy score was established for personalized risk stratification.
- The method facilitates a reliable assessment of disease aggressiveness.
Conclusions:
- This approach offers a direct metabolic sign of PCa malignancy and aggressiveness.
- It overcomes previous limitations by analyzing fresh, unfixed tissue.
- Enables higher granularity personalized risk stratification and more decisive treatment decisions for PCa patients.
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