Triple-Encoding Platform for Multiplexed In Situ Profiling of Extracellular Vesicle miRNAs Enables Precision Diagnosis of Ovarian Cancer

Ovarian cancer, the most lethal gynecologic malignancy, urgently demands early and accurate diagnostic methods. Tumor-derived extracellular vesicles (tEVs) hold great promise as noninvasive biomarkers, yet their clinical translation is severely hampered by inherent heterogeneity and the inability of conventional assays to detect multiple types of biomarkers. To break this bottleneck, we developed a novel one-pot, triple-encoding platform that integrates size-fluorescence-encoded beads with a membrane fusion strategy. This enables simultaneous in situ profiling of 11 miRNAs across three distinct EV subpopulations defined by EpCAM/PD-L1 expression. Leveraging machine learning, an 11-miRNA logistic panel achieved tumor versus health discrimination (AUC = 0.995), and a combinatorial signature based on EV subpopulations and miRNA profiles enabled the classification of ovarian cancer subtypes, achieving 78.6% accuracy in the validation cohort. Our work provides a versatile and scalable approach for multiparameter EV analysis, enabling comprehensive EV miRNA profiling and ovarian cancer classification.