Accurate and Convenient Lung Cancer Diagnosis through Detection of Extracellular Vesicle Membrane Proteins via Forster Resonance Energy Transfer

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 Fo''rster 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 mu L), and highly accurate approach, providing the potential to aid lung cancer diagnosis and early screening.

Automated summary

Researchers have developed an EV-MPDS system based on FRET signals, which allows convenient diagnosis of lung cancer without the need for EV extraction and purification. In clinical samples, this system demonstrated improved accuracy and sensitivity compared to the ELISA detection method, with early screening accuracy further enhanced through machine learning analysis of five biomarkers.