ArticleCancer cell international2025
A diagnostic model for non-invasive urothelial cancer early detection based on methylation of urinary tumor DNA.
Article in Cancer cell international, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Methylation entropy as a novel dimension in liquid biopsy: enhanced multimodal framework for cancer detection and tissue-of-origin classification.Journal of translational medicine · 2026Article
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11 authors.
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Abstract
backgroundDiagnostic methods for urothelial cancer (UC) are often invasive, while urinary cytology, a non-invasive alternative, suffers from limited sensitivity. This study aimed to identify differentially methylated markers in urinary tumor DNA and develop a diagnostic method to enhance the sensitivity of non-invasive UC detection.
methodsWhole-genome bisulfite sequencing and deep methylation sequencing were employed to identify significantly hypermethylated UC-associated genes in clinical samples and public UC datasets. Further screening was conducted using tumor biopsies and urine samples from patients, leading to the selection of three hypermethylated UC markers. A diagnostic model based on these markers was constructed and validated in a cohort (N = 432) comprising patients with UC, other cancers, benign lesions, and non-UC urinary tract diseases.
resultsValidation in a cohort of 432 subjects demonstrated that the UC diagnostic model, incorporating three hypermethylated markers (VIM, TMEM220, and PPM1N), achieved an overall sensitivity of 94.44% in 108 UC patients. Specificities were 96.34%, 90.76%, and 87.72% in 191 non-neoplastic individuals, 76 patients with benign lesions, and 57 patients with other cancers, respectively, resulting in an overall specificity of 93.52%. Methylation level analysis revealed significantly higher methylation (P < 0.001) for three markers in UC samples compared to non-UC samples. Furthermore, the model exhibited sensitivities of 80% and 88.57% for detecting stage 0a/0is and stage I UC, respectively.
conclusionsThe UC diagnostic model demonstrates excellent diagnostic performance, particularly in the early detection of UC. This non-invasive approach, characterized by high sensitivity and specificity, holds significant potential for further clinical evaluation and development as a reliable tool for UC diagnosis using urine samples.
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