ArticleJournal of proteome research2011
Diagnosis of early stage ovarian cancer by 1H NMR metabonomics of serum explored by use of a microflow NMR probe.
Article in Journal of proteome research, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed, 81 citations in OpenAlex.
- Trial
- Composite proteomic and metabolomic plasma biomarkers for detection of colorectal, lung and ovarian cancers.Molecular cancer · 2026Article
- Clinical utility of various liquid biopsy samples for the early detection of ovarian cancer: a comprehensive review.Frontiers in oncology · 2025Review
- Metabolic responses to the occurrence and chemotherapy of pancreatic cancer: biomarker identification and prognosis prediction.Scientific reports · 2024Article
- Identification and analysis of pancreatic intraepithelial neoplasia: opportunities and challenges.Frontiers in endocrinology · 2024Review
- Peripheral Blood Serum NMR Metabolomics Is a Powerful Tool to Discriminate Benign and Malignant Ovarian Tumors.Metabolites · 2023Article
- Association of altered metabolic profiles and long non-coding RNAs expression with disease severity in breast cancer patients: analysis byMetabolomics : Official journal of the Metabolomic Society · 2023Article
- Acidic ascites inhibits ovarian cancer cell proliferation and correlates with the metabolomic, lipidomic and inflammatory phenotype of human patients.Journal of translational medicine · 2022Article
- Current and Emerging Methods for Ovarian Cancer Screening and Diagnostics: A Comprehensive Review.Cancers · 2022Review
- Free Amino Acid Alterations in Patients with Gynecological and Breast Cancer: A Review.Pharmaceuticals (Basel, Switzerland) · 2021Review
- Serum Free Amino Acid Profiling in Differential Diagnosis of Ovarian Tumors-A Comparative Study with Review of the Literature.International journal of environmental research and public health · 2021Review
- Prediction of Ovarian Cancer-Related Metabolites Based on Graph Neural Network.Frontiers in cell and developmental biology · 2021Article
- NMR-Based Metabolomics.Advances in experimental medicine and biology · 2021Article
- Emerging Role of Metabolomics in Ovarian Cancer Diagnosis.Metabolites · 2020Review
- One-Carbon Metabolism: Biological Players in Epithelial Ovarian Cancer.International journal of molecular sciences · 2018Review
- Metabolomic prediction of endometrial cancer.Metabolomics : Official journal of the Metabolomic Society · 2017Article
- Saturated fatty acids as possible important metabolites for epithelial ovarian cancer based on the free and esterified fatty acid profiles determined by GC-MS analysis.Cancer biomarkers : section A of Disease markers · 2016Article
- Metabolic Pathway Signatures Associated with Urinary Metabolite Biomarkers Differentiate Bladder Cancer Patients from Healthy Controls.Yonsei medical journal · 2016Article
- Metabolomics of biomarker discovery in ovarian cancer: a systematic review of the current literature.Metabolomics : Official journal of the Metabolomic Society · 2016Article
- Novel personalized pathway-based metabolomics models reveal key metabolic pathways for breast cancer diagnosis.Genome medicine · 2016Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
We show that (1)H NMR based metabonomicsof serum allows the diagnosis of early stage I/II epithelial ovarian cancer (EOC) required for successful treatment. Because patient specimens are highly precious, we conducted an exploratory study using a microflow probe requiring only 20 μL of serum. By use of logistic regression on principal components (PCs) of the NMR profiles, we built a 4-variable model for early stage EOC prediction (training set: 69 EOC specimens, 84 healthy controls; test set: 40 EOC, 44 controls) with operating characteristics estimated for the test set at 80% specificity [95% confidence interval (CI): 65-90%], 63% sensitivity (95% CI: 46-77%), and an area under the Receiver Operator Characteristic Curve (AUC) of 0.796. Independent validation (50 EOC, 50 controls) of the model yielded 95% specificity (95% CI: 86-99.5%), 68% sensitivity (95% CI: 53-80%) and an AUC of 0.949. A test on cancer type specificity showed that women diseased with renal cell carcinoma were not incorrectly diagnosed with EOC, indicating that metabonomics bears significant potential for cancer type-specific diagnosis. Our model can potentially be applied for women at high risk for EOC, and our study promises to contribute to developing a screening protocol for the general population.
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Registered trials
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