Urquidi Virginia, Netherton Mandy, Gomes-Giacoia Evan, Serie Daniel J, Eckel-Passow Jeanette, Rosser Charles J, Goodison Steve
Nonagen Bioscience Corporation, Jacksonville, FL, USA.
Cancer Research Institute, MD Anderson Cancer Center, Orlando, FL, USA.
Oncotarget. 2016 Dec 27;7(52):86290-86299. doi: 10.18632/oncotarget.13382.
The development of accurate, non-invasive urinary assays for bladder cancer would greatly facilitate the detection and management of a disease that has a high rate of recurrence and progression. In this study, we employed a discovery and validation strategy to identify microRNA signatures that can perform as a non-invasive bladder cancer diagnostic assay. Expression profiling of 754 human microRNAs (TaqMan low density arrays) was performed on naturally voided urine samples from a cohort of 85 subjects of known bladder disease status (27 with active BCa). A panel of 46 microRNAs significantly associated with bladder cancer were subsequently monitored in an independent cohort of 121 subjects (61 with active BCa) using quantitative real-time PCR (RT-PCR). Multivariable modeling identified a 25-target diagnostic signature that predicted the presence of BCa with an estimated sensitivity of 87% at a specificity of 100% (AUC 0.982). With additional validation, the monitoring of a urinary microRNA biomarker panel could facilitate the non-invasive evaluation of patients under investigation for BCa.
开发用于膀胱癌的准确、非侵入性尿液检测方法将极大地促进对这种具有高复发率和进展率疾病的检测和管理。在本研究中,我们采用了一种发现和验证策略来识别可作为非侵入性膀胱癌诊断检测方法的微小RNA特征。对来自85名已知膀胱疾病状态受试者(27名患有活动性膀胱癌)队列的自然排尿尿液样本进行了754种人类微小RNA的表达谱分析(TaqMan低密度阵列)。随后,使用定量实时PCR(RT-PCR)在121名受试者(61名患有活动性膀胱癌)的独立队列中监测了一组与膀胱癌显著相关的46种微小RNA。多变量建模确定了一个25靶点的诊断特征,该特征预测膀胱癌的存在,估计敏感性为87%,特异性为100%(AUC 0.982)。通过进一步验证,监测尿液微小RNA生物标志物组可促进对正在接受膀胱癌调查患者的非侵入性评估。