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Updated: Apr 15, 2026

Author Spotlight: Engineering Molecular Tools for Disease Detection and Imaging
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Enzymatic Cascade-Powered Urinary Diagnostics for Visual Cancer Monitoring.

Lu Wang1,2, Ruiyue Chen1, Zhiyong Liu1

  • 1Department of Gastric and Hernia Surgery, Nanjing Drum Tower Hospital, College of Engineering and Applied Sciences, State Key Laboratory of Analytical Chemistry for Life Science, Nanjing University, Nanjing 210023, China.

ACS Nano
|April 13, 2026
PubMed
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This summary is machine-generated.

This study introduces a novel dual-enzyme nanosensor for noninvasive cancer monitoring in urine. It enables visual detection of tumor enzymes, offering a promising tool for personalized cancer therapy guidance.

Area of Science:

  • Biomedical Engineering
  • Nanotechnology
  • Cancer Diagnostics

Background:

  • Accurate, real-time, noninvasive diagnostic tools are crucial for personalized cancer therapy.
  • Urine-based diagnostics offer a promising noninvasive approach for continuous monitoring.
  • Current methods face limitations due to biomarker instability, low abundance, and poor specificity.

Purpose of the Study:

  • To develop a dual-enzyme-powered nanosensor for sensitive and specific detection of tumor-associated enzymatic activity in urine.
  • To enable visual, instrument-free detection of cancer biomarkers for companion diagnostics.
  • To translate intracellular enzyme activity into stable urinary signals for longitudinal monitoring.

Main Methods:

  • A nanosensor was constructed using gold nanoclusters (AuNCs), a synthetic DNA probe (A1T2), and hyaluronic acid (HA).
Keywords:
early diagnosisengineered biomarkermicrofluidicspersonalized medicinepoint-of-care

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  • The DNA probe was engineered for sequential cleavage by apurinic/apyrimidinic endonuclease 1 (APE1) and extension by telomerase (TE).
  • A competitive lateral flow assay (LFA) was employed for visual detection of AuNC-tagged DNA fragments in urine.

    • Main Results:

    • The dual-enzyme system amplified signals, enabling visual detection of enzymatic activity.
    • The nanosensor demonstrated improved specificity and sensitivity compared to conventional methods.
    • The strategy successfully translated intracellular enzyme activity into stable, detectable urinary signals.

    Conclusions:

    • The developed nanosensor provides a sensitive, visual, and instrument-free method for urine-based cancer diagnostics.
    • This technology holds potential for low-cost companion diagnostics to guide personalized cancer treatment.
    • The approach facilitates real-time, noninvasive longitudinal monitoring of cancer therapy.