ArticleCancers2023
A Novel Methylation Marker NRN1 plus TERT and FGFR3 Mutation Using Urine Sediment Enables the Detection of Urothelial Bladder Carcinoma.
Article in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Assessment of forensic individual identification and kinship analysis using transcript SNPs derived from public transcriptome sequencing data.BMC genomics · 2026Article
- Review
- Maternal Nutrient Restriction Programs Fetal Hepatic DNA Methylation in Ovine Monozygotic Twins.International journal of molecular sciences · 2026Article
- Unleashing the potential of urine DNA methylation detection: Advancements in biomarkers, clinical applications, and emerging technologies.Current urology · 2025Article
- Recent advances on gene-related DNA methylation in cancer diagnosis, prognosis, and treatment: a clinical perspective.Clinical epigenetics · 2025Review
- Bladder cancer detection in urine by novel methylation markers.Scientific reports · 2024Article
- TRIM27 revealing by tumor educated platelet RNA-sequencing, as a potential biomarker for malignant ground-glass opacities diagnosis mediates glycolysis of non-small cell lung cancer cells partially through HOXM1.Translational lung cancer research · 2024Article
- Article
- Urine-Based Biomarker Test UromonitorCancers · 2024Review
- Urinary DNA methylation-based risk stratification model to triage patients for repeat transurethral resection of bladder tumours.Clinical and translational medicine · 2024Article
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Authors and funding
15 authors.
Funding
Abstract
backgroundAberrant 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.
methodsA 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.
resultsIn 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.
conclusionsThe diagnostic tool exhibited a highly specific and robust performance. It may be used as a replaceable approach for the detection of UBC.
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