Dataset Information

Urine Cellular DNA Point Mutation and Methylation for Identifying Upper Tract Urinary Carcinoma.

ABSTRACT:

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.

SUBMITTER: Ouyang W

PROVIDER: S-EPMC9319988 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundConventional clinical detection methods such as CT, urine cytology, and ureteroscopy display low sensitivity and/or are invasive in the diagnosis of upper tract urinary carcinoma (UTUC), a factor precluding their use. Previous studies on urine biopsy have not shown satisfactory sensitivity and specificity in the application of both gene mutation or gene methylation panels. Therefore, these unfavorable factors call for an urgent need for a sensitive and non-invasive method for the diagnosis of UTUC.MethodsIn this study, a total of 161 hematuria patients were enrolled with (n = 69) or without (n = 92) UTUC. High-throughput sequencing of 17 genes and methylation analysis for ONECUT2 CpG sites were combined as a liquid biopsy test panel. Further, a logistic regression prediction model that contained several significant features was used to evaluate the risk of UTUC in these patients.ResultsIn total, 86 UTUC- and 64 UTUC+ case samples were enrolled for the analysis. A logistic regression analysis of significant features including age, the mutation status of TERT promoter, and ONECUT2 methylation level resulted in an optimal model with a sensitivity of 94.0%, a specificity of 93.1%, the positive predictive value of 92.2% and a negative predictive value of 94.7%. Notably, the area under the curve (AUC) was 0.957 in the training dataset while internal validation produced an AUC of 0.962. It is worth noting that during follow-up, a patient diagnosed with ureteral inflammation at the time of diagnosis exhibiting both positive mutation and methylation test results was diagnosed with ureteral carcinoma 17 months after his enrollment.ConclusionThis work utilized the epigenetic biomarker ONECUT2 for the first time in the detection of UTUC and discovered its superior performance. To improve its sensitivity, we combined the biomarker with high-throughput sequencing of 17 genes test. It was found that the selected logistic regression model diagnosed with ureteral cancer can evaluate upper tract urinary carcinoma risk of patients with hematuria and outperform other existing panels in providing clinical recommendations for the diagnosis of UTUC. Moreover, its high negative predictive value is conducive to rule to exclude patients without UTUC.