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. 2023 Jan 19;15(3):615.
doi: 10.3390/cancers15030615.

A Novel Methylation Marker NRN1 plus TERT and FGFR3 Mutation Using Urine Sediment Enables the Detection of Urothelial Bladder Carcinoma

Junjie Zhang  1   2 , Ran Xu  3 , Qiang Lu  4 , Zhenzhou Xu  5 , Jianye Liu  1 , Pei Li  6 , Yaqun Zhang  7 , Chuanchi Zhou  1 , Lufeng Luo  1 , Wei Tang  6 , Zhenting Wang  8 , Manman Cao  6 , Jian Cao  5 , Genming Xu  6 , Long Wang  1
Affiliations

A Novel Methylation Marker NRN1 plus TERT and FGFR3 Mutation Using Urine Sediment Enables the Detection of Urothelial Bladder Carcinoma

Junjie Zhang et al. Cancers (Basel). .

Abstract

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.

Keywords: DNA methylation region; SNPs; diagnostic tool; urothelial bladder carcinoma.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Flow chart of the study design.
Figure 2
Figure 2
The heatmap of different methylation regions in cohort 1. The parameter “condition” represents the benign controls in blue and the UBC tumor samples in red.
Figure 3
Figure 3
The box plot and AUC of different methylation biomarkers in cohort 2 by detecting methylation regions. (A–G) represents the ΔCt value distributions of each biomarker in UBC and control groups. (A1–G7) represents the AUC values of each biomarker. The classifier of the AUC analysis was Δct value of biomarkers in each sample.
Figure 4
Figure 4
The heatmap of different SNP sites in cohort 3. The parameter “condition” represents the benign controls in blue and the UBC tumor samples in red.
Figure 5
Figure 5
The ROC curve of diagnostic tool consisting of 1 methylation biomarker and 2 SNPs in cohort 3. The classifier of the AUC analysis was Δct value of biomarkers in each sample.
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