Proteomic profiling of single extracellular vesicles as a promising new approach for the diagnosis and treatment modality of advanced ovarian cancer.

ABSTRACT: This study utilized a novel Proximity Barcoding Assay to perform high-resolution proteomic profiling of individual plasma extracellular vesicles from 85 patients with advanced high-grade serous ovarian carcinoma (OC) and 95 healthy controls (HC). Single-EV analysis identified 119 differentially expressed proteins and 17 distinct EV subpopulations. Cluster 7 (enriched in integrins ITGB3, ITGB1, and ITGA6) was significantly elevated in OC plasma (4.47% in HC vs. 14.79-15.82% in OC). Machine learning (SVM-RFE, LASSO, Random Forest) identified a diagnostic panel (ITGA6, ITGB2, ILK) achieving exceptional accuracy in distinguishing OC from HC (AUC = 0.999 training; 1.000 validation). Furthermore, risk models incorporating specific protein signatures effectively stratified patients by platinum se

SUBMITTER: Wu B 

PROVIDER: S-EPMC12905296 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature