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Few-Layer Graphene-Based Optical Nanobiosensors for the Early-Stage Detection of Ovarian Cancer Using Liquid Biopsy and an Active Learning Strategy

  • 0Department of Cancer Biology, University of Kansas Medical Center, Kansas City, KS 66160, USA.
Cells +

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March 12, 2025

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March 12, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

Early ovarian cancer detection is crucial for survival. New graphene-based optical nanobiosensors (G-NBSs) accurately detect early-stage ovarian cancer using protease activity, achieving 94.5% accuracy.

Area Of Science

  • Biomedical Engineering
  • Nanotechnology
  • Oncology

Background

  • Ovarian cancer survival rates are significantly impacted by the stage at diagnosis.
  • Early-stage detection is critical for improving patient outcomes and survival.
  • Liquid biopsies offer a promising avenue for non-invasive early cancer detection.

Purpose Of The Study

  • To develop and validate graphene-based optical nanobiosensors (G-NBSs) for early ovarian cancer detection.
  • To quantify protease activities using a panel of G-NBSs for specific ovarian cancer identification.
  • To implement a predictive model for early-stage ovarian cancer diagnosis.

Main Methods

  • Development of few-layer explosion graphene nanobiosensors with hydrophilic coating.
  • Utilizing fluorescently labeled consensus sequences for specific protease activity quantification.
  • Employing a hierarchical framework with active learning (AL) for data analysis and prediction.

Main Results

  • G-NBSs demonstrated statistically significant differences in protease activities between early-stage ovarian cancer, late-stage, and healthy controls.
  • The active learning model achieved an overall accuracy of 94.5% for early-stage ovarian cancer detection.
  • The system exhibited high sensitivity (0.94) and specificity (0.94) in distinguishing early-stage disease.

Conclusions

  • Graphene-based optical nanobiosensors are effective tools for quantifying protease activities relevant to ovarian cancer.
  • The developed G-NBSs panel and active learning framework show high potential for accurate and sensitive early-stage ovarian cancer detection.
  • This approach represents a significant advancement in liquid biopsy technology for improving ovarian cancer diagnostics.

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