Department of Urology, Stanford University School of Medicine, Stanford, USA; Veterans Affairs Palo Alto Health Care System, Palo Alto, USA.
Department of Biomedical Data Science, Stanford University, Stanford, USA; Department of Epidemiology and Medical Statistics, School of Public Health, Guangzhou Medical University, Guangzhou, China.
Urol Oncol. 2021 Aug;39(8):497.e9-497.e15. doi: 10.1016/j.urolonc.2021.02.011. Epub 2021 Mar 22.
The risk of bladder cancer (BCa) diagnosis and recurrence necessitates cystoscopy. Improved risk stratification may inform personalized triage and surveillance strategies. We aim to develop a urinary mRNA biomarker panel for risk stratification in patients undergoing BCa screening and surveillance.
Urine samples were collected from patients undergoing cystoscopy for BCa screening or surveillance. In patients who underwent transurethral resection of bladder tumor, urine samples were categorized based on tumor histopathology, size, and focality. Subjects with intermediate and high-risk BCa based on American Urological Association (AUA) guideline for non-muscle invasive bladder cancer were classified as "increased-risk"; those with no cancer and AUA low-risk BCa were classified as "low-risk". Urine was evaluated for ROBO1, WNT5A, CDC42BPB, ABL1, CRH, IGF2, ANXA10, and UPK1B expression. A diagnostic model to detect "increased-risk" BCa was created using forward logistic regression analysis of cycle threshold values. Model validation was performed with ten-fold cross-validation. Sensitivity and specificity for detection of "increased-risk" BCa was determined and net benefit analysis performed.
Urine samples (n = 257) were collected from 177 patients (95 screening, 76 surveillance, 6 both). There were 65 diagnoses of BCa (12 low, 22 intermediate, 31 high risk). ROBO1, CRH, and IGF2 expression correlated with "increased-risk" disease yielding sensitivity of 92.5% (95% CI, 84.9%-98.1%) and specificity of 73.5% (95% CI, 67.7-79.9%). The overall calculated standardized net benefit of the model was 0.81 (95%CI, 0.71-0.90).
A 3-marker urinary mRNA panel allows for non-invasive identification of "increased-risk" BCa and with further validation may prove to be a tool to reduce the need for cystoscopies in low-risk patients.
膀胱癌(BCa)的诊断和复发风险需要进行膀胱镜检查。改善风险分层可以为个性化分诊和监测策略提供信息。我们旨在开发用于 BCa 筛查和监测患者风险分层的尿液 mRNA 生物标志物面板。
从接受 BCa 筛查或监测的患者中收集尿液样本。对于接受经尿道膀胱肿瘤切除术的患者,根据肿瘤组织病理学、大小和局灶性对尿液样本进行分类。根据美国泌尿外科学会(AUA)非肌肉浸润性膀胱癌指南,将基于美国泌尿外科学会(AUA)非肌肉浸润性膀胱癌指南的中高危 BCa 患者归类为“高风险”;将无癌症和 AUA 低危 BCa 的患者归类为“低危”。评估尿液中 ROBO1、WNT5A、CDC42BPB、ABL1、CRH、IGF2、ANXA10 和 UPK1B 的表达。使用循环阈值值的正向逻辑回归分析创建用于检测“高风险”BCa 的诊断模型。使用十折交叉验证进行模型验证。确定检测“高风险”BCa 的敏感性和特异性,并进行净效益分析。
从 177 名患者(95 名筛查,76 名监测,6 名两者兼有)中收集了 257 份尿液样本。共诊断出 65 例 BCa(12 例低危,22 例中危,31 例高危)。ROBO1、CRH 和 IGF2 的表达与“高风险”疾病相关,其敏感性为 92.5%(95%CI,84.9%-98.1%),特异性为 73.5%(95%CI,67.7%-79.9%)。该模型的总体计算标准化净效益为 0.81(95%CI,0.71-0.90)。
一种 3 标志物尿液 mRNA 面板可实现非侵入性识别“高风险”BCa,并且经过进一步验证,可能成为减少低风险患者膀胱镜检查需求的工具。