Profile of Extracellular Vesicle Proteins by a Plasmonic Fluorophores-enabled Chip Assay for Ovarian Cancer Diagnosis

The molecular analysis of extracellular vesicle (EV) proteins presents a promising avenue for liquid biopsy applications in the early diagnosis of ovarian cancer (OvCa) and the monitoring of cancer progression. However, existing methods for EV analysis in cancer diagnosis face limitations related to poor enrichment efficiency, inadequate detection sensitivity, and limited multiplexing capabilities. In this study, we introduce the plasmonic fluorophores (PFs)-enabled chip assay for high-throughput analysis of extracellular vesicle proteins (PFEX chip). The PFEX chip combines colloidal-assembled nanostructures for efficient EV enrichment and the ultrabright PFs that label the EVs through an enzyme-free reaction for convenient signal readout. This design enables the direct profiling of EV proteins from clinical plasma samples. Featuring eight channels, the chip facilitates multiplexed analysis of EV proteins, including CD316, CD49b, myoferlin, FACL4, CD49e, CD61, and FOLR1, contributing to the diagnosis of ovarian cancer. A machine learning algorithm is employed to generate the EV signature based on the signal levels of a seven-protein panel. The EV signature exhibits high sensitivity and specificity, distinguishing stage-1 OvCa from non-OvCa and healthy controls, and providing accurate classification of early-stage ovarian cancer, advanced-stage ovarian cancer, and healthy controls. Notably, the PFEX chip emerges as a powerful tool for diagnosing OvCa and evaluating cancer progression.