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Related Experiment Video

Updated: Jul 13, 2026

Cell-Free DNA Integrity Analysis in Urine Samples
07:58

Cell-Free DNA Integrity Analysis in Urine Samples

Published on: January 5, 2017

Urinary nucleosides based potential biomarker selection by support vector machine for bladder cancer recognition.

Yong Mao1, Xiaoping Zhao, Shufang Wang

  • 1Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310027, PR China.

Analytica Chimica Acta
|August 19, 2007
PubMed
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Urinary nucleosides show promise as bladder cancer biomarkers. A new method identified specific nucleosides with high accuracy for bladder cancer diagnosis, offering a potential non-invasive detection approach.

Area of Science:

  • Biochemistry
  • Oncology
  • Analytical Chemistry

Background:

  • Urinary nucleosides are recognized as potential biomarkers for various cancers.
  • Their role in bladder cancer detection has been underexplored.
  • This study investigates urinary nucleosides for bladder cancer diagnosis.

Purpose of the Study:

  • To validate urinary nucleosides as biomarkers for bladder cancer.
  • To identify specific urinary nucleosides with high discriminative performance for bladder cancer recognition.

Main Methods:

  • Collected and analyzed 20 urinary samples from bladder cancer patients and 20 controls.
  • Utilized capillary electrophoresis-mass spectrometry to generate urinary nucleoside profiles.
  • Employed Support Vector Machine Recursive Feature Elimination (SVM-RFE) and a novel Support Vector Machine based Partial Exhaustive Search Algorithm (SVM-PESA) for biomarker identification.

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Last Updated: Jul 13, 2026

Cell-Free DNA Integrity Analysis in Urine Samples
07:58

Cell-Free DNA Integrity Analysis in Urine Samples

Published on: January 5, 2017

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Magnetic Resonance Imaging Assessment of Carcinogen-induced Murine Bladder Tumors

Published on: March 29, 2019

Main Results:

  • SVM-PESA identified 22 optimal feature subsets (3-4 features each) with 95% cross-validation accuracy, 100% sensitivity, and 90% specificity.
  • The performance of SVM-PESA surpassed that of SVM-RFE.
  • Urinary nucleosides with m/z 317, 290, and 304 were identified as potential bladder cancer biomarkers.

Conclusions:

  • Urinary nucleosides demonstrate potential as effective tumor biomarkers for bladder cancer.
  • The novel biomarker selection method (SVM-PESA) proved effective in identifying diagnostic signatures.