Exosome-based OCS Scores for Predicting Ovarian Cancer Recurrence

Ovarian cancer is the third most common malignant tumor in China's female reproductive system in terms of incidence rate and the first in terms of mortality. Surgery and systemic chemotherapy are the cornerstone of first-line treatment for patients with epithelial ovarian cancer. The efficacy rate of first-line platinum combined with taxus chemotherapy is more than 80%, and more than half of them reach complete remission (CR). However, even if advanced patients reach CR, 50% -70% still relapse, with a median disease-free interval of 16 months. Most patients lack typical symptoms when they relapse. The sensitivity of CA125 in monitoring recurrence is poor (43-74%), which means that more than 25% of ovarian cancer patients cannot detect recurrence in a timely manner during follow-up. Therefore, more sensitive biomarkers are needed for monitoring ovarian cancer recurrence after treatment. More and more studies have explored the application value of extracellular vesicle technology in the diagnosis and treatment of ovarian cancer, including auxiliary diagnosis, prognosis, monitoring, etc. China already has an approved exosome ovarian cancer diagnostic product by the National Medical Products Administration, namely the Ovarian Cancer Diagnostic Score (OCS) product based on exosome technology, which is also the world's first exosome technology diagnostic kit for ovarian cancer. Clinical trial data shows that OCS products have demonstrated excellent performance in distinguishing between benign and malignant ovarian tumors. Under high specificity (90.2%), it has a higher sensitivity than serum CA125, reaching 95.5% (95% CI 92.7% -97.3%), and has a sensitivity of 89.7% in the diagnosis of stage I ovarian cancer. OCS exhibits better sensitivity than CA125, especially in early ovarian cancer, suggesting that OCS may be a better tool for monitoring recurrence in ovarian cancer patients.The main purpose of this study is to establish an OCS scoring ovarian cancer recurrence prediction and monitoring model based on exosome technology, providing an effective tool for clinical ovarian cancer recurrence prediction and monitoring. Secondly, we hope to explore the correlation between OCS score and tumor staging, chemotherapy sensitivity, PFS, OS indicators, etc., and determine the performance of recurrence prediction models in different subgroups, such as different pathological subtypes of ovarian cancer and different treatment methods.