Background: 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. Methods: 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. Results: 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. Conclusions: The diagnostic tool exhibited a highly specific and robust performance. It may be used as a replaceable approach for the detection of UBC.
Background: To improve the selection of patients for ureteroscopy, avoid excessive testing and reduce costs, we aimed to develop and validate a diagnostic urine assay for upper tract urinary carcinoma (UTUC). Methods: In this cohort study we recruited 402 patients from six Hunan hospitals who underwent ureteroscopy for hematuria, including 95 patients with UTUC and 307 patients with non-UTUC findings. Midstream morning urine samples were collected before ureteroscopy and surgery. DNA was extracted and qPCR was used to analyze mutations in TERT and FGFR3 and the methylation of NRN1. In the training set, the random forest algorithm was used to build an optimal panel. Lastly, the Beijing cohort (n = 76) was used to validate the panel. Results: The panel combining the methylation with mutation markers led to an AUC of 0.958 (95% CI: 0.933–0.975) with a sensitivity of 91.58% and a specificity of 94.79%. The panel presented a favorable diagnostic value for UTUC vs. other malignant tumors (AUC = 0.920) and UTUC vs. benign disease (AUC = 0.975). Furthermore, combining the panel with age revealed satisfactory results, with 93.68% sensitivity, 94.44% specificity, AUC = 0.970 and NPV = 98.6%. In the external validation process, the model showed an AUC of 0.971, a sensitivity of 95.83% and a specificity of 92.31, respectively. Conclusions: A novel diagnostic model for analyzing hematuria patients for the risk of UTUC was developed, which could lead to a reduction in the need for invasive examinations. Combining NRN1 methylation and gene mutation (FGFR3 and TERT) with age resulted in a validated accurate prediction model.
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