Study on the Dynamic Changes of Metabolic Biomarkers and Their Prognostic Relationship in Ovarian Cancer

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

RECRUITING

Total Enrollment

100 participants

Study Classification

OBSERVATIONAL

Study Start Date

2023-10-26

Study Completion Date

2027-09-10

Brief Summary

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Ovarian cancer is a highly lethal gynecological malignancy, often diagnosed at an advanced stage, with high rates of recurrence within 1-2 years after frontline treatment. Current guidelines recommend monitoring tumor markers CA125 and HE4 for disease progression, but these markers may not detect recurrence or disease progression when their levels are below the detection limit. Therefore, there is a need to identify new prognostic biomarkers and monitor their dynamic changes for effective risk stratification and personalized treatment in patients with ovarian cancer

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Detailed Description

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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.

Conditions

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Ovarian Cancer

Study Design

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Observational Model Type

CASE_ONLY

Study Time Perspective

PROSPECTIVE

Interventions

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No intervention

Intervention Type OTHER

Eligibility Criteria

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Inclusion Criteria

* Age between 18 and 75 years;
* Pathological diagnosis of high-grade serous ovarian cancer;
* Newly diagnosed ovarian cancer case without prior neoadjuvant therapy.

Exclusion Criteria

* Non-primary (recurrent) patients;
* Ovarian cancer patients who have not undergone surgical treatment;
* Patients with a history of other malignancies.

Minimum Eligible Age

18 Years

Maximum Eligible Age

75 Years

Eligible Sex

FEMALE

Accepts Healthy Volunteers

No