Zhang Junjie, Xu Ran, Lu Qiang, Xu Zhenzhou, Liu Jianye, Li Pei, Zhang Yaqun, Zhou Chuanchi, Luo Lufeng, Tang Wei, Wang Zhenting, Cao Manman, Cao Jian, Xu Genming, Wang Long
Department of Urology, The Third Xiangya Hospital, Central South University, Changsha 410013, China.
Department of Thoracic Surgery, Xiangya Hospital, Central South University, Changsha 410008, China.
Cancers (Basel). 2023 Jan 19;15(3):615. doi: 10.3390/cancers15030615.
Aberrant DNA methylation is an early event during tumorigenesis. In the present study, we aimed to construct a methylation diagnostic tool using urine sediment for the detection of urothelial bladder carcinoma, and improved the diagnostic performance of the model by incorporating single-nucleotide polymorphism (SNP) sites.
A three-stage analysis was carried out to construct the model and evaluate the diagnostic performance. In stage I, two small cohorts from Xiangya hospital were recruited to validate and identify the detailed regions of collected methylation biomarkers. In stage II, proof-of-concept study cohorts from the Hunan multicenter were recruited to construct a diagnostic tool. In stage III, a blinded cohort comprising suspicious UBC patients was recruited from Beijing single center to further test the robustness of the model.
In stage I, single NRN1 exhibited the highest AUC compared with six other biomarkers and the Random Forest model. At the best cutoff value of 5.16, a single NRN1 biomarker gave a diagnosis with a sensitivity of 0.93 and a specificity of 0.97. In stage II, the Random Forest algorithm was applied to construct a diagnostic tool, consisting of NRN1, TERT C228T and FGFR3 p.S249C. The tool exhibited AUC values of 0.953, 0.946 and 0.951 in training, test and all cohorts. At the best cutoff value, the model resulted in a sensitivity of 0.871 and a specificity of 0.947. In stage III, the diagnostic tool achieved a good discrimination in the external validation cohort, with an overall AUC of 0.935, sensitivity of 0.864 and specificity of 0.895. Additionally, the model exhibited a superior sensitivity and comparable specificity compared with conventional cytology and FISH.
The diagnostic tool exhibited a highly specific and robust performance. It may be used as a replaceable approach for the detection of UBC.
异常DNA甲基化是肿瘤发生过程中的早期事件。在本研究中，我们旨在构建一种利用尿沉渣检测膀胱尿路上皮癌的甲基化诊断工具，并通过纳入单核苷酸多态性（SNP）位点来提高模型的诊断性能。
进行三阶段分析以构建模型并评估诊断性能。在第一阶段，招募了来自湘雅医院的两个小队列，以验证和确定收集的甲基化生物标志物的详细区域。在第二阶段，招募了来自湖南多中心的概念验证研究队列，以构建诊断工具。在第三阶段，从北京单中心招募了一个包含可疑膀胱尿路上皮癌患者的盲法队列，以进一步测试模型的稳健性。
在第一阶段，与其他六个生物标志物和随机森林模型相比，单个NRN1的AUC最高。在最佳临界值5.16时，单个NRN1生物标志物诊断的灵敏度为0.93，特异性为0.97。在第二阶段，应用随机森林算法构建了一个诊断工具，该工具由NRN1、TERT C228T和FGFR3 p.S249C组成。该工具在训练、测试和所有队列中的AUC值分别为0.953、0.946和0.951。在最佳临界值时，该模型的灵敏度为0.871，特异性为0.947。在第三阶段，该诊断工具在外部验证队列中具有良好的区分能力，总体AUC为0.935，灵敏度为0.864，特异性为0.895。此外，与传统细胞学和FISH相比，该模型表现出更高的灵敏度和相当的特异性。
该诊断工具表现出高度特异性和稳健的性能。它可作为检测膀胱尿路上皮癌的一种可替代方法。
