Kemesiene Jurate, Nicolau Carlos, Cholstauskas Gytis, Zviniene Kristina, Lopeta Mantvydas, Veneviciute Simona, Asmenaviciute Ieva, Tamosauskaite Kamile, Pikuniene Ingrida, Jievaltas Mindaugas
Department of Radiology, Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania.
Department of Radiology, Hospital Clinic, University of Barcelona, Barcelona, Spain.
Res Rep Urol. 2025 Mar 29;17:95-103. doi: 10.2147/RRU.S511523. eCollection 2025.
Prostate cancer (PCa) remains a significant health concern due to its high incidence and associated mortality. Conventional screening approaches, like PSA testing, often lack specificity, resulting in unnecessary biopsies and overtreatment. This study seeks to overcome these limitations by assessing the integration of novel urinary biomarkers into established risk prediction models.
This study aimed to evaluate the performance of incorporating urinary biomarkers - prostate cancer antigen 3 (PCA3) and transmembrane serine protease 2 (TMPRSS2) gene and ETS-related gene (ERG) fusion genes (T:E) - into the Prostate Cancer Prevention Trial Risk Calculator version 2 (PCPTRC2) in a Lithuanian cohort to enhance the detection of clinically significant prostate cancer (csPCa).
A single-centre prospective study included 246 men scheduled for initial prostate biopsy between January 2021 and August 2024 due to elevated total PSA levels or abnormal digital rectal examination (DRE). Following ethical approval and informed consent, urinary samples were collected post-DRE and analysed for PCA3 and T:E. Each patient's risk was calculated using the basic PCPTRC2 and updated versions incorporating biomarkers. Biopsies were performed based on multiparametric magnetic resonance imaging (mpMRI) findings.
Of 209 biopsy samples analysed, 111 (53.1%) were diagnosed with csPCa. The AUC for PCa detection was 59.6% for the original PCPTRC2, improving to 76.2% with PCA3 and further to 79.5% when both PCA3 and T:E were included. Both updated versions demonstrated significantly higher sensitivity compared to the original (p<0.001). However, no significant differences were noted in distinguishing csPCa from non-csPCa.
Incorporating PCA3 and T:E into PCPTRC2 substantially enhances diagnostic accuracy for detecting PCa in biopsy-naïve patients. Despite limitations, these findings underscore the potential for optimizing risk calculators in clinical practice, advocating for larger cohorts to validate these results.
前列腺癌（PCa）因其高发病率和相关死亡率，仍然是一个重大的健康问题。传统的筛查方法，如前列腺特异性抗原（PSA）检测，往往缺乏特异性，导致不必要的活检和过度治疗。本研究旨在通过评估将新型尿液生物标志物纳入既定风险预测模型来克服这些局限性。
本研究旨在评估将尿液生物标志物——前列腺癌抗原3（PCA3）、跨膜丝氨酸蛋白酶2（TMPRSS2）基因和ETS相关基因（ERG）融合基因（T:E）——纳入前列腺癌预防试验风险计算器第2版（PCPTRC2）在立陶宛队列中的表现，以提高对临床显著性前列腺癌（csPCa）的检测。
一项单中心前瞻性研究纳入了246名男性，这些男性因总PSA水平升高或直肠指检（DRE）异常，于2021年1月至2024年8月期间计划进行初次前列腺活检。在获得伦理批准和知情同意后，在DRE后收集尿液样本，并分析PCA3和T:E。使用基本的PCPTRC2和纳入生物标志物的更新版本计算每位患者的风险。根据多参数磁共振成像（mpMRI）结果进行活检。
在分析的209份活检样本中，111份（53.1%）被诊断为csPCa。原始PCPTRC2检测前列腺癌的曲线下面积（AUC）为59.6%，加入PCA3后提高到76.2%，同时加入PCA3和T:E时进一步提高到79.5%。两个更新版本的敏感性均显著高于原始版本（p<0.001）。然而，在区分csPCa和非csPCa方面未发现显著差异。
将PCA3和T:E纳入PCPTRC2可显著提高在未进行活检的患者中检测前列腺癌的诊断准确性。尽管存在局限性，但这些发现强调了在临床实践中优化风险计算器的潜力，提倡更大规模的队列研究来验证这些结果。
