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Updated: Apr 20, 2026

Murine Model for Non-invasive Imaging to Detect and Monitor Ovarian Cancer Recurrence
Published on: November 2, 2014
Prediction of advanced ovarian cancer recurrence by plasma metabolic profiling
Haiyu Zhang1, Tingting Ge, Xiaoming Cui
1Department of Epidemiology and Biostatistics, Harbin Medical University, Harbin 150081, P.R. China. likang@ems.hrbmu.edu.cn.
Abstract:
Epithelial ovarian cancer (EOC) is the most lethal of gynecologic malignancies due to the high rate of recurrence and poor prognosis. Predicting the prognosis in patients with EOC is clinically challenging, partly because appropriate biomarkers of recurrence have yet to be explored. In this prospective study, pre-treatment plasma samples were collected from 38 patients with stage III or IV EOC who were subsequently followed up. Ultra-performance liquid chromatography mass spectrometry was used to perform metabolic profiling, which yielded five metabolites that were potential biomarkers for EOC recurrence: l-tryptophan, kynurenine, bilirubin, LysoPC (14 : 0) and LysoPE (18 : 2). A combination of these five potential biomarkers strongly predicted recurrence, the area under the curve being 0.91. In summary, the candidate biomarkers identified in this study may both facilitate clinical prediction of EOC recurrence and prognosis and serve as potential therapeutic targets in patients with EOC.
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