posted on 2023-08-29, 20:31authored byShuting Xiao, Yi Yao, Shuilin Liao, Bin Xu, Xue Li, Yuxiao Zhang, Lei Zhang, Qiang Chen, Haoneng Tang, Qibin Song, Ming Dong
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.