ArticleNano letters2023
Accurate and Convenient Lung Cancer Diagnosis through Detection of Extracellular Vesicle Membrane Proteins via Förster Resonance Energy Transfer.
Article in Nano letters, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed, 31 citations in OpenAlex.
- Precision nanomedicine for pulmonary diseases: from molecular targeting to clinical translation.Signal transduction and targeted therapy · 2026Review
- Extracellular Vesicles in Cancer: Biomarkers, Mechanisms, and Emerging Diagnostic Technologies.Advanced healthcare materials · 2026Review
- Liquid biopsy techniques and lung cancer: diagnosis, monitoring and evaluation.Journal of experimental & clinical cancer research : CR · 2024Review
- From biogenesis to aptasensors: advancements in analysis for tumor-derived extracellular vesicles research.Theranostics · 2024Review
- Article
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Authors and funding
11 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Tumor-derived extracellular vesicles (EVs) are promising to monitor early stage cancer. Unfortunately, isolating and analyzing EVs from a patient's liquid biopsy are challenging. For this, we devised an EV membrane proteins detection system (EV-MPDS) based on Förster resonance energy transfer (FRET) signals between aptamer quantum dots and AIEgen dye, which eliminated the EV extraction and purification to conveniently diagnose lung cancer. In a cohort of 80 clinical samples, this system showed enhanced accuracy (100% versus 65%) and sensitivity (100% versus 55%) in cancer diagnosis as compared to the ELISA detection method. Improved accuracy of early screening (from 96.4% to 100%) was achieved by comprehensively profiling five biomarkers using a machine learning analysis system. FRET-based tumor EV-MPDS is thus an isolation-free, low-volume (1 μL), and highly accurate approach, providing the potential to aid lung cancer diagnosis and early screening.
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Registered trials
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