Candidate Transcript Panel in Semen Extracellular Vesicles Can Improve Prediction of Aggressiveness of Prostate Cancer

TL;DR

This study identifies specific genes in semen vesicles that can better predict prostate cancer severity, offering a more accurate diagnostic tool than PSA alone.

Contribution

The study introduces a candidate transcript panel in semen extracellular vesicles for predicting prostate cancer aggressiveness.

Findings

KLK3 and PCA3 genes in semen extracellular vesicles show differential expression between prostate cancer and healthy controls.

CREB3L4, CCNQ, and DUSP23 levels correlate with the severity of prostate cancer.

Combined transcript levels in semen vesicles serve as effective biomarkers for prostate cancer diagnosis and severity assessment.

Abstract

The need for prostate cancer (PCa)-specific biomarkers that enable more accurate detection of the disease and better prediction of tumor aggressiveness remains ongoing due to the low cancer specificity of PSA screening. Several potential mRNA markers for diagnosing PCa, in tissue and urine, have been reported in the literature. In this study, we aim to explore the potential of selected prostate-specific molecules and transcripts contained in small extracellular vesicles (sEVs) in semen to predict PCa risk reclassification for patients with moderately elevated PSA levels—a clinical scenario where identifying truly non-invasive biomarkers is especially critical. RT-qPCR analysis in semen sEVs successfully showed differential expression of KLK3 and PCA3 genes between PCa and healthy controls, whereas CREB3L4, CCNQ and DUSP23 levels were related to the severity or degree of PCa affectation. Our findings also present strong evidence that classifiers based on combined long transcript levels in semen sEVs serve as effective biomarkers. They can be used alone or in combination with blood PSA and/or semen citric acid levels to improve the diagnosis of PCa and assess its severity and disease progression with high accuracy. This strategy would allow a more comprehensive assessment, increase prognostic accuracy, and facilitate accurate clinical decision-making in the management of PCa.

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