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Background <p>Diagnostic 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.</p> Methods <p>Whole-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.</p> Results <p>Validation in a cohort of 432 subjects demonstrated that the UC diagnostic model, incorporating three hypermethylated markers (<i>VIM</i>, <i>TMEM220</i>, and <i>PPM1N</i>), 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 &lt; 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.</p> Conclusions <p>The 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.</p>

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A diagnostic model for non-invasive urothelial cancer early detection based on methylation of urinary tumor DNA

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月15日

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  • Ningning Wu,
  • Zhen Wu,
  • Yanwen Wang,
  • Anqi Zhang,
  • Yongfei Peng,
  • Yan Cheng,
  • Hongsong Lei,
  • Siwen Liu,
  • Jie Zhao,
  • Tianbao Li,
  • Guangpeng Zhou

摘要

Background

Diagnostic 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.

Methods

Whole-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.

Results

Validation 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.

Conclusions

The 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.