Ovarian Cancer is the deadliest gynecological malignancy, with over 70% of patients being diagnosed at advanced stages, and more than 70% experiencing recurrence within 1-2 years after frontline treatment. The recommended tumor biomarkers for monitoring ovarian cancer progression, CA125 and HE4, still pose the risk of recurrence and disease progression when their levels are below the detection limit. Therefore, it is of paramount importance to search for new prognostic monitoring biomarkers for ovarian cancer in order to stratify the prognosis and implement personalized treatment, ultimately improving patient outcomes. Previous research and literature have indicated that metabolic biomarkers can directly reflect the biochemical changes, physiological status, and disease progression in cancer patients. In comparison to studying the relationship between metabolite expression levels at a single time point and disease prognosis, the dynamic changes in metabolite trajectories with multiple time points can better reflect the dynamic patterns of disease progression throughout the entire cancer cycle, providing more prognostic information for patients with ovarian cancer.
