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Related Experiment Video

Updated: Apr 30, 2026

Nanosensors to Detect Protease Activity In Vivo for Noninvasive Diagnostics
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Urine-Based Noninvasive Detection of Prostate Cancer Using Human Olfactory Receptor-Embedded Nanodiscs.

Jin Yoo1,2, Yerin Kim1, Ku Kang2

  • 1School of Chemical and Biological Engineering, Institute of Chemical Processes, Seoul National University, Seoul 08826, Republic of Korea.

ACS Sensors
|April 28, 2026
PubMed
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This study introduces a novel urine test using olfactory receptors and machine learning to accurately detect prostate cancer (PCa). The new diagnostic platform offers a noninvasive alternative to biopsies, improving early detection of PCa.

Area of Science:

  • Biomedical Engineering
  • Analytical Chemistry
  • Oncology

Background:

  • Prostate cancer (PCa) diagnosis is hampered by the low specificity of prostate-specific antigen (PSA) testing.
  • This limitation leads to overdiagnosis and unnecessary invasive procedures like prostate biopsies.

Purpose of the Study:

  • To develop and validate a noninvasive, urine-based diagnostic platform for prostate cancer detection.
  • To utilize olfactory receptors (ORs) and machine learning to identify PCa-specific volatile organic compounds (VOCs).

Main Methods:

  • A sensor array of six human olfactory receptor-embedded nanodiscs (OR-NDs) was developed.
  • Fluorescence sensing and machine learning classifiers were employed to analyze urine samples.
  • The platform was validated using a dataset of 290 samples, including PCa patients and healthy controls.
Keywords:
fluorescence quenching analysisnanodiscolfactory receptorprostate cancerrandom foresturine biomarker

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Main Results:

  • The diagnostic platform achieved high accuracy (0.890) and AUC (0.964) in detecting PCa.
  • Specific ORs (OR2W1, OR51E1, OR51E2) were identified as key features for classification.
  • Urinary VOC patterns correlated better with Gleason score than serum PSA levels.

Conclusions:

  • A urine-based OR-ND sensor array combined with machine learning enables accurate, noninvasive detection and classification of PCa.
  • This approach offers a promising complementary tool to conventional biomarkers, capturing tumor-specific metabolic information.
  • The developed framework is extensible for diagnosing other VOC-associated diseases.