Jiang Yuan-Hong, Liu Yu-Shu, Wei Yu-Chung, Jhang Jia-Fong, Kuo Hann-Chorng, Huang Hsin-Hui, Chan Michael W Y, Lin Guan-Ling, Cheng Wen-Chi, Lin Shu-Chuan, Wang Hung-Jung
Department of Urology, Hualien Tzu Chi Hospital, Tzu Chi University, Hualien 970374, Taiwan.
Guzip Biomarkers Corporation, Hsinchu City 302041, Taiwan.
Diagnostics (Basel). 2024 Feb 21;14(5):468. doi: 10.3390/diagnostics14050468.
Bladder cancer (BCa) is a significant health issue and poses a healthcare burden on patients, highlighting the importance of an effective detection method. Here, we developed a urine DNA methylation diagnostic panel for distinguishing between BCa and non-BCa. In the discovery stage, an analysis of the TCGA database was conducted to identify BCa-specific DNA hypermethylation markers. In the validation phase, DNA methylation levels of urine samples were measured with real-time quantitative methylation-specific PCR (qMSP). Comparative analysis of the methylation levels between BCa and non-BCa, along with the receiver operating characteristic (ROC) analyses with machine learning algorithms (logistic regression and decision tree methods) were conducted to develop practical diagnostic panels. The performance evaluation of the panel shows that the individual biomarkers of ZNF671, OTX1, and IRF8 achieved AUCs of 0.86, 0.82, and 0.81, respectively, while the combined yielded an AUC of 0.91. The diagnostic panel using the decision tree algorithm attained an accuracy, sensitivity, and specificity of 82.6%, 75.0%, and 90.9%, respectively. Our results show that the urine-based DNA methylation diagnostic panel provides a sensitive and specific method for detecting and stratifying BCa, showing promise as a standard test that could enhance the diagnosis and prognosis of BCa in clinical settings.
膀胱癌(BCa)是一个重大的健康问题,给患者带来了医疗负担,凸显了有效检测方法的重要性。在此,我们开发了一种用于区分膀胱癌和非膀胱癌的尿液DNA甲基化诊断面板。在发现阶段,对TCGA数据库进行分析以识别膀胱癌特异性DNA高甲基化标志物。在验证阶段,使用实时定量甲基化特异性PCR(qMSP)测量尿液样本的DNA甲基化水平。对膀胱癌和非膀胱癌之间的甲基化水平进行比较分析,并使用机器学习算法(逻辑回归和决策树方法)进行受试者操作特征(ROC)分析,以开发实用的诊断面板。该面板的性能评估表明,ZNF671、OTX1和IRF8的单个生物标志物的曲线下面积(AUC)分别达到0.86、0.82和0.81,而联合使用时AUC为0.91。使用决策树算法的诊断面板的准确率、灵敏度和特异性分别为82.6%、75.0%和90.9%。我们的结果表明,基于尿液的DNA甲基化诊断面板为检测和分层膀胱癌提供了一种灵敏且特异的方法,有望成为一种标准检测方法,可在临床环境中提高膀胱癌的诊断和预后水平。