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<p>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 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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月21日

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  • Beier Wu,
  • Xuping Yang,
  • Yanling Cai,
  • Shihan Wan,
  • Jingfang Liu,
  • Jie Xing,
  • Xin Chen,
  • Jiejie Zhang,
  • Yanlu Jin,
  • Aijun Yu,
  • Li Yang

摘要

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 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.