ArticleMetabolomics : Official journal of the Metabolomic Society2019
Metabolomic biomarkers in cervicovaginal fluid for detecting endometrial cancer through nuclear magnetic resonance spectroscopy.
Article in Metabolomics : Official journal of the Metabolomic Society, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it, 44 citations in OpenAlex.
- Cervicovaginal specimen biomarkers for early detection of ovarian and endometrial cancer: A review.Cancer medicine · 2024Pooled it
- Exploring metabolic signatures in urine using NMR for improved prognosis of gliomas.Metabolomics : Official journal of the Metabolomic Society · 2026Article
- Identification of biomarkers in gynecologic cancers: a machine learning approach for metabolomics.Translational and clinical pharmacology · 2025Article
- Detecting metabolic signatures in endometrial cancer: potential applications of Raman spectroscopy.Future oncology (London, England) · 2025Review
- Unlocking the Potential of Liquid Biopsy: A Paradigm Shift in Endometrial Cancer Care.Diagnostics (Basel, Switzerland) · 2025Review
- Article
- Cervicovaginal Metabolome and Tumor Characteristics for Endometrial Cancer Detection and Risk Stratification.Clinical cancer research : an official journal of the American Association for Cancer Research · 2024Article
- Metabolomic-Based Approaches for Endometrial Cancer Diagnosis and Prognosis: A Review.Cancers · 2023Review
- Urine metabolomics for assessing fertility-sparing treatment efficacy in endometrial cancer: a non-invasive approach using ultra-performance liquid chromatography mass spectrometry.BMC women's health · 2023Article
- A comparison of different machine-learning techniques for the selection of a panel of metabolites allowing early detection of brain tumors.Scientific reports · 2023Article
- Cancer metabolites: promising biomarkers for cancer liquid biopsy.Biomarker research · 2023Review
- A Fecal-Microbial-Extracellular-Vesicles-Based Metabolomics Machine Learning Framework and Biomarker Discovery for Predicting Colorectal Cancer Patients.Metabolites · 2023Article
- DNA Methylation of Window of Implantation Genes in Cervical Secretions Predicts Ongoing Pregnancy in Infertility Treatment.International journal of molecular sciences · 2023Article
- Endometrial cancer diagnostic and prognostic algorithms based on proteomics, metabolomics, and clinical data: a systematic review.Frontiers in oncology · 2023Review
- Multi-Omic Profiling of Multi-Biosamples Reveals the Role of Amino Acid and Nucleotide Metabolism in Endometrial Cancer.Frontiers in oncology · 2022Article
- Applications of machine learning in metabolomics: Disease modeling and classification.Frontiers in genetics · 2022Review
- Research on the Guiding Effect of CTCs on Postoperative Adjuvant Therapy for Patients with Early Stage Endometrial Cancer.Journal of oncology · 2022Article
- New Trends in the Detection of Gynecological Precancerous Lesions and Early-Stage Cancers.Cancers · 2021Review
- Grading of endometrial cancer usingScientific reports · 2021Article
- Free Amino Acid Alterations in Patients with Gynecological and Breast Cancer: A Review.Pharmaceuticals (Basel, Switzerland) · 2021Review
Corrections and comments
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Authors and funding
11 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionEndometrial cancer (EC) is one of the most common gynecologic neoplasms in developed countries but lacks screening biomarkers.
objectivesWe aim to identify and validate metabolomic biomarkers in cervicovaginal fluid (CVF) for detecting EC through nuclear magnetic resonance (NMR) spectroscopy.
methodsWe screened 100 women with suspicion of EC and benign gynecological conditions, and randomized them into the training and independent testing datasets using a 5:1 study design. CVF samples were analyzed using a 600-MHz NMR spectrometer equipped with a cryoprobe. Four machine learning algorithms-support vector machine (SVM), partial least squares discriminant analysis (PLS-DA), random forest (RF), and logistic regression (LR), were applied to develop the model for identifying metabolomic biomarkers in cervicovaginal fluid for EC detection.
resultsA total of 54 women were eligible for the final analysis, with 21 EC and 33 non-EC. From 29 identified metabolites in cervicovaginal fluid samples, the top-ranking metabolites chosen through SVM, RF and PLS-DA which existed in independent metabolic pathways, i.e. phosphocholine, malate, and asparagine, were selected to build the prediction model. The SVM, PLS-DA, RF, and LR methods all yielded area under the curve values between 0.88 and 0.92 in the training dataset. In the testing dataset, the SVM and RF methods yielded the highest accuracy of 0.78 and the specificity of 0.75 and 0.80, respectively.
conclusionPhosphocholine, asparagine, and malate from cervicovaginal fluid, which were identified and independently validated through models built using machine learning algorithms, are promising metabolomic biomarkers for the detection of EC using NMR spectroscopy.
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