Department of Biochemistry, Western University, London, ON, Canada.
Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada.
J Ovarian Res. 2024 Jul 17;17(1):149. doi: 10.1186/s13048-024-01471-8.
The five-year prognosis for patients with late-stage high-grade serous carcinoma (HGSC) remains dismal, underscoring the critical need for identifying early-stage biomarkers. This study explores the potential of extracellular vesicles (EVs) circulating in blood, which are believed to harbor proteomic cargo reflective of the HGSC microenvironment, as a source for biomarker discovery.
We conducted a comprehensive proteomic profiling of EVs isolated from blood plasma, ascites, and cell lines of patients, employing both data-dependent (DDA) and data-independent acquisition (DIA) methods to construct a spectral library tailored for targeted proteomics. Our investigation aimed at uncovering novel biomarkers for the early detection of HGSC by comparing the proteomic signatures of EVs from women with HGSC to those with benign gynecological conditions. The initial cohort, comprising 19 donors, utilized DDA proteomics for spectral library development. The subsequent cohort, involving 30 HGSC patients and 30 control subjects, employed DIA proteomics for a similar purpose. Support vector machine (SVM) classification was applied in both cohorts to identify combinatorial biomarkers with high specificity and sensitivity (ROC-AUC > 0.90). Notably, MUC1 emerged as a significant biomarker in both cohorts when used in combination with additional biomarkers. Validation through an ELISA assay on a subset of benign (n = 18), Stage I (n = 9), and stage II (n = 9) plasma samples corroborated the diagnostic utility of MUC1 in the early-stage detection of HGSC.
This study highlights the value of EV-based proteomic analysis in the discovery of combinatorial biomarkers for early ovarian cancer detection.
晚期高级别浆液性卵巢癌（HGSC）患者的五年预后仍然很差，这突显了识别早期生物标志物的迫切需求。本研究探讨了循环血液中外泌体（EVs）的潜力，这些 EVs 被认为携带有反映 HGSC 微环境的蛋白质组货物，可作为发现生物标志物的来源。
我们采用依赖数据（DDA）和独立数据获取（DIA）方法对来自患者血浆、腹水和细胞系的 EVs 进行了全面的蛋白质组学分析，构建了针对靶向蛋白质组学的专用光谱库。我们旨在通过比较 HGSC 患者和良性妇科疾病患者 EVs 的蛋白质组特征，发现早期检测 HGSC 的新型生物标志物。初始队列包括 19 名供体，使用 DDA 蛋白质组学进行光谱库开发。随后的队列包括 30 名 HGSC 患者和 30 名对照，使用 DIA 蛋白质组学进行类似的分析。两个队列都应用支持向量机（SVM）分类来识别具有高特异性和灵敏度的组合生物标志物（ROC-AUC>0.90）。值得注意的是，MUC1 作为一个重要的生物标志物在两个队列中出现，当与其他生物标志物结合使用时。通过对一组良性（n=18）、I 期（n=9）和 II 期（n=9）血浆样本的 ELISA 检测验证，MUC1 在早期 HGSC 检测中的诊断效用得到了证实。
本研究强调了基于 EV 的蛋白质组学分析在发现早期卵巢癌检测的组合生物标志物方面的价值。
