Proteomic profiling of single extracellular vesicles as a promising new approach for the diagnosis and treatment modality of advanced ovarian cancer. (PubMed, NPJ Precis Oncol)
Furthermore, risk models incorporating specific protein signatures effectively stratified patients by platinum sensitivity/resistance (9-protein panel: ILK, CDCP1, CD86, CLDN4, CLEC1B, CDHR5, CLDN11, JAM2, FOLH1), lymph node metastasis status (7-protein panel: APOE, CD28, CLDN4, FOLH1, ITGAL, JAML, ULBP3), and post-surgical residual disease burden (4-protein panel: CD44, CLMP, ITGA4, AMIGO1), with Cluster 13 (ITGB1-high) also significantly associated with residual disease. This work demonstrates the power of single-EV proteomics combined with machine learning for non-invasive diagnosis and clinical outcome assessment in advanced ovarian cancer, though the absence of early-stage patients limits its applicability for early detection.