ArticleMetabolomics : Official journal of the Metabolomic Society2018
Predicting ovarian cancer recurrence by plasma metabolic profiles before and after surgery.
Article in Metabolomics : Official journal of the Metabolomic Society, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed, 34 citations in OpenAlex.
- Exploring translational relevance of baseline and longitudinal metabolic profiling in the blood of ovarian cancer patients.NPJ precision oncology · 2026Article
- Emerging role of histone and non-histone lactylation in metabolic reprogramming of female-specific malignancies.World journal of surgical oncology · 2025Review
- Identification of biomarkers in gynecologic cancers: a machine learning approach for metabolomics.Translational and clinical pharmacology · 2025Article
- Circulating tumor cells help differentiate benign ovarian lesions from cancer before surgery: A literature review and proof of concept study using flow cytometry with fluorescence imaging.Oncology letters · 2024Article
- Exploring the causal role of multiple metabolites on ovarian cancer: a two sample Mendelian randomization study.Journal of ovarian research · 2024Article
- Predicting the Recurrence of Ovarian Cancer Based on Machine Learning.Cancer management and research · 2024Article
- Application of Nonlinear Models Combined with Conventional Laboratory Indicators for the Diagnosis and Differential Diagnosis of Ovarian Cancer.Journal of clinical medicine · 2023Article
- Applications of machine learning in metabolomics: Disease modeling and classification.Frontiers in genetics · 2022Review
- Integration of global metabolomics and lipidomics approaches reveals the molecular mechanisms and the potential biomarkers for postoperative recurrence in early-stage cholangiocarcinoma.Cancer & metabolism · 2021Article
- Metabolome analysis reveals a diversity of cancer tissues in advanced epithelial ovarian cancer.Cancer cell international · 2021Article
- Applications of Multi-omics Approaches for Exploring the Molecular Mechanism of Ovarian Carcinogenesis.Frontiers in oncology · 2021Review
- Role of biomarkers for early detection of ovarian cancer recurrence.Gland surgery · 2020Review
- Progress of Artificial Intelligence in Gynecological Malignant Tumors.Cancer management and research · 2020Review
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Authors and funding
14 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPrevious metabolomic studies have revealed that plasma metabolic signatures may predict epithelial ovarian cancer (EOC) recurrence. However, few studies have performed metabolic profiling of pre- and post-operative specimens to investigate EOC prognostic biomarkers.
objectiveThe aims of our study were to compare the predictive performance of pre- and post-operative specimens and to create a better model for recurrence by combining biomarkers from both metabolic signatures.
methodsThirty-five paired plasma samples were collected from 35 EOC patients before and after surgery. The patients were followed-up until December, 2016 to obtain recurrence information. Metabolomics using rapid resolution liquid chromatography-mass spectrometry was performed to identify metabolic signatures related to EOC recurrence. The support vector machine model was employed to predict EOC recurrence using identified biomarkers.
resultsGlobal metabolomic profiles distinguished recurrent from non-recurrent EOC using both pre- and post-operative plasma. Ten common significant biomarkers, hydroxyphenyllactic acid, uric acid, creatinine, lysine, 3-(3,5-diiodo-4-hydroxyphenyl) lactate, phosphohydroxypyruvic acid, carnitine, coproporphyrinogen, L-beta-aspartyl-L-glutamic acid and 24,25-hydroxyvitamin D3, were identified as predictive biomarkers for EOC recurrence. The area under the receiver operating characteristic (AUC) values in pre- and post-operative plasma were 0.815 and 0.909, respectively; the AUC value after combining the two sets reached 0.964.
conclusionPlasma metabolomic analysis could be used to predict EOC recurrence. While post-operative biomarkers have a predictive advantage over pre-operative biomarkers, combining pre- and post-operative biomarkers showed the best predictive performance and has great potential for predicting recurrent EOC.
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