Urquidi Virginia, Netherton Mandy, Gomes-Giacoia Evan, Serie Daniel, Eckel-Passow Jeanette, Rosser Charles J, Goodison Steve
Cancer Research Institute, MD Anderson Cancer Center, Orlando, FL, USA.
Department of Health Sciences Research, Mayo Clinic, Jacksonville, FL, USA.
Oncotarget. 2016 Jun 21;7(25):38731-38740. doi: 10.18632/oncotarget.9587.
The early detection of bladder cancer is important as the disease has a high rate of recurrence and progression. The development of accurate, non-invasive urinary assays would greatly facilitate detection. In previous studies, we have reported the discovery and initial validation of mRNA biomarkers that may be applicable in this context. In this study, we evaluated the diagnostic performance of proposed molecular signatures in an independent cohort.Forty-four mRNA transcripts were monitored blindly in urine samples obtained from a cohort of 196 subjects with known bladder disease status (89 with active BCa) using quantitative real-time PCR (RT-PCR). Statistical analyses defined associations of individual biomarkers with clinical data and the performance of predictive multivariate models was assessed using ROC curves. The majority of the candidate mRNA targets were confirmed as being associated with the presence of BCa over other clinical variables. Multivariate models identified an optimal 18-gene diagnostic signature that predicted the presence of BCa with a sensitivity of 85% and a specificity of 88% (AUC 0.935). Analysis of mRNA signatures in naturally micturated urine samples can provide valuable information for the evaluation of patients under investigation for BCa. Additional refinement and validation of promising multi-target signatures will support the development of accurate assays for the non-invasive detection and monitoring of BCa.
膀胱癌的早期检测很重要,因为该疾病具有高复发率和进展率。准确、非侵入性尿液检测方法的开发将极大地促进检测。在先前的研究中,我们报告了可能适用于此背景的mRNA生物标志物的发现和初步验证。在本研究中,我们在一个独立队列中评估了所提出的分子特征的诊断性能。使用定量实时PCR(RT-PCR)对从196名已知膀胱疾病状态的受试者(89名患有活动性膀胱癌)队列中获得的尿液样本中的44种mRNA转录本进行了盲测。统计分析确定了个体生物标志物与临床数据之间的关联,并使用ROC曲线评估了预测多变量模型的性能。大多数候选mRNA靶点被证实与膀胱癌的存在相关,而非其他临床变量。多变量模型确定了一个最佳的18基因诊断特征,其预测膀胱癌存在的敏感性为85%,特异性为88%(AUC 0.935)。对自然排尿尿液样本中的mRNA特征进行分析可为评估膀胱癌待查患者提供有价值的信息。对有前景的多靶点特征进行进一步优化和验证将支持开发用于膀胱癌非侵入性检测和监测的准确检测方法。