Abstract
High performance mass spectrometry was employed to interrogate the serum metabolome of early-stage ovarian cancer (OC) patients and age-matched control women. The resulting spectral features were used to establish a linear support vector machine (SVM) model of sixteen diagnostic metabolites that are able to identify early-stage OC with 100% accuracy in our patient cohort. The results provide evidence for the importance of lipid and fatty acid metabolism in OC and serve as the foundation of a clinically significant diagnostic test.
Publication types
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Research Support, Non-U.S. Gov't
MeSH terms
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Adult
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Aged
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Biomarkers, Tumor / blood*
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CA-125 Antigen / blood
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Case-Control Studies
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Chromatography, High Pressure Liquid
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Early Detection of Cancer / standards*
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Female
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Humans
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Lysophospholipids / blood
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Metabolome
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Metabolomics*
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Middle Aged
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Ovarian Neoplasms / blood*
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Ovarian Neoplasms / metabolism
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Ovarian Neoplasms / pathology*
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Principal Component Analysis
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Sensitivity and Specificity
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Support Vector Machine
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Tandem Mass Spectrometry
Substances
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Biomarkers, Tumor
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CA-125 Antigen
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Lysophospholipids
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lysophosphatidylethanolamine
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lysophosphatidylinositol