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Purpose <p>Diagnostics for urothelial carcinoma have low sensitivity, thereby negatively impacting diagnostic outcomes. Herein, we present Biovue<sup>Uro</sup>, a machine learning-based urine DNA biomarker tool for urothelial carcinoma detection. We developed Biovue<sup>Uro</sup> to monitor DNA mutation and methylation in urine samples.</p> Methods <p>The study involved 63 patients with malignant urothelial carcinoma, 13 patients with benign lesions, 115 patients with urinary symptoms, and 106 healthy participants undergoing physical check-ups. Detection was performed using real-time fluorescence quantitative polymerase chain reaction. Biovue<sup>Uro</sup> can detect six single nucleotide polymorphism sites in <i>FGFR3</i>, <i>PIK3CA</i>, and <i>TERT</i>, and aberrant methylation in <i>HIST1H4F</i>, <i>NRN1</i>, and <i>POU4F2</i>. Ten machine-learning algorithms were employed to train the data and classify patients and healthy controls.</p> Results <p>The optional model demonstrated high diagnostic efficacy in distinguishing patients with high-grade urothelial carcinoma from non-patients. In the validation set, the receiver operating characteristic curve, sensitivity, specificity, accuracy, and positive and negative predictive values were 96.1% (95% CI 89.3–100), 94.7%, 99.1%, 98.5%, 94.7%, and 99.1%, respectively. For distinguishing patients with early-stage urothelial carcinoma from non-patients, the values in the training set were 91.7% (95% CI 84.2–99.1), 80%, 98.3%, 95.1%, 90.9%, and 95.8%, and those in the validation set were 92.5% (95% CI 86.6–98.4%), 80%, 95.7%, 93%, 80%, and 95.7%, respectively. The study’s limitations include a relatively small cohort size, restricted regional diversity and a lack of long-term follow-up data to assess disease progression and treatment outcomes.</p> Conclusions <p>In summary, Biovue<sup>Uro</sup>, a machine learning-enhanced multi-analyte liquid biopsy integrating mutation polymorphisms and methylation profiling, demonstrates superior diagnostic accuracy for urothelial carcinoma compared to conventional urinary tests and methylation-based assays. Its robust sensitivity for early-stage tumors, high specificity for high-grade lesions, and non-invasive nature underscore its potential as a clinically actionable tool for urothelial carcinoma detection. Longer-term outcomes and regional diversity should be evaluated.</p>

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Liquid biopsy based on multi-targeted capture of urinary tumor DNA combined with machine learning to detect urothelial carcinoma: a multicenter prospective study

  • 期刊论文

  • 发表日期：

    2025年9月25日

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  • Chaozhi Tang,
  • Tianlong Wang,
  • Huarong Luo,
  • Jingdong Xue,
  • Miaojun Zhu,
  • Zhe Hong,
  • Fang Ding,
  • Fengwu Zhang,
  • Yihao Zhu,
  • Ruoying Tan,
  • Denglong Wu

摘要

Purpose

Diagnostics for urothelial carcinoma have low sensitivity, thereby negatively impacting diagnostic outcomes. Herein, we present BiovueUro, a machine learning-based urine DNA biomarker tool for urothelial carcinoma detection. We developed BiovueUro to monitor DNA mutation and methylation in urine samples.

Methods

The study involved 63 patients with malignant urothelial carcinoma, 13 patients with benign lesions, 115 patients with urinary symptoms, and 106 healthy participants undergoing physical check-ups. Detection was performed using real-time fluorescence quantitative polymerase chain reaction. BiovueUro can detect six single nucleotide polymorphism sites in FGFR3, PIK3CA, and TERT, and aberrant methylation in HIST1H4F, NRN1, and POU4F2. Ten machine-learning algorithms were employed to train the data and classify patients and healthy controls.

Results

The optional model demonstrated high diagnostic efficacy in distinguishing patients with high-grade urothelial carcinoma from non-patients. In the validation set, the receiver operating characteristic curve, sensitivity, specificity, accuracy, and positive and negative predictive values were 96.1% (95% CI 89.3–100), 94.7%, 99.1%, 98.5%, 94.7%, and 99.1%, respectively. For distinguishing patients with early-stage urothelial carcinoma from non-patients, the values in the training set were 91.7% (95% CI 84.2–99.1), 80%, 98.3%, 95.1%, 90.9%, and 95.8%, and those in the validation set were 92.5% (95% CI 86.6–98.4%), 80%, 95.7%, 93%, 80%, and 95.7%, respectively. The study’s limitations include a relatively small cohort size, restricted regional diversity and a lack of long-term follow-up data to assess disease progression and treatment outcomes.

Conclusions

In summary, BiovueUro, a machine learning-enhanced multi-analyte liquid biopsy integrating mutation polymorphisms and methylation profiling, demonstrates superior diagnostic accuracy for urothelial carcinoma compared to conventional urinary tests and methylation-based assays. Its robust sensitivity for early-stage tumors, high specificity for high-grade lesions, and non-invasive nature underscore its potential as a clinically actionable tool for urothelial carcinoma detection. Longer-term outcomes and regional diversity should be evaluated.