Enhancing risk stratification models in localized prostate cancer by novel validated tissue biomarkers

Csilla Olah ¹, Fabian Mairinger ², Michael Wessolly ²

¹Department of Urology, University of Duisburg-Essen, Essen, Germany.
²Institute of Pathology, University Medicine Essen, University of Duisburg-Essen, Essen, Germany.
³Department of Urology, University Hospitals Leuven, Leuven, Belgium.
⁴Lindenhofspital, Bern, Switzerland.
⁵Urology Research Laboratory, Department for BioMedical Research, University of Bern, Bern, Switzerland.
⁶Department of Urology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.
⁷Department of Urology, Semmelweis University, Budapest, Hungary.
⁸Research Centre for Natural Sciences, Cancer Biomarker Research Group, Institute of Enzymology, Budapest, Hungary.
⁹Department of Bioinformatics, Semmelweis University, Budapest, Hungary.
¹⁰Dr. Senckenberg Institute of Pathology, University Hospital Frankfurt, Goethe University Frankfurt, Frankfurt am Main, Germany.
¹¹Department of Urology, University of Duisburg-Essen, Essen, Germany. tibor.szarvas@uk-essen.de.
¹²Department of Urology, Semmelweis University, Budapest, Hungary. tibor.szarvas@uk-essen.de.

⁰Department of Urology, University of Duisburg-Essen, Essen, Germany.

Prostate Cancer and Prostatic Diseases +

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November 14, 2024

View abstract on PubMed

Summary

New prognostic markers NCAPH, UBE2C, and ZWINT were identified for prostate cancer (PCa). High expression of these tissue markers improves risk stratification for localized PCa patients, aiding in distinguishing aggressive disease.

Area Of Science

Oncology
Molecular Biology
Biomarker Discovery

Background

Prostate cancer (PCa) exhibits significant heterogeneity.
Current risk stratification methods for localized PCa are limited.
Improved prognostic markers are needed to differentiate indolent from aggressive disease.

Purpose Of The Study

To identify novel prognostic markers for prostate cancer.
To enhance the accuracy of risk stratification in localized PCa.

Main Methods

In silico analysis of prostate cancer transcriptome datasets.
Validation of top prognostic genes in institutional and external cohorts using NanoString and immunohistochemistry (IHC).
Assessment of cancer-specific survival (CSS) and progression-free survival (PFS).

Main Results

Identified 113 prognostic genes in silico; validated seven.
Low expression of CENPO, P2RX5, ABCC5 and high expression of ASF1B, NCAPH, UBE2C, ZWINT associated with shorter CSS.
Higher NCAPH and ZWINT protein expression linked to shorter PFS in external cohort.
Combined markers improved existing risk stratification models (D'Amico, CAPRA, Cambridge).

Conclusions

High tissue levels of NCAPH, UBE2C, and ZWINT are validated prognostic risk factors in localized PCa.
These novel markers can enhance current risk estimation models for PCa patients.
Improved risk stratification aids in clinical decision-making for localized prostate cancer.