Ovarian cancer (OC) is the most common cause of death from gynecologic malignancies in women. The identification of reliable diagnostic biomarkers for the early detection of this deadly disease is critical for reducing the mortality rate of OC. Plasma lysophosphatidic acid (LPA) levels were increased from OC patients vs. healthy controls. Therefore, lipidomics may represent an excellent developing prospect for the discovery of diagnostic biomarkers of OC. In this study, a nontargeted lipidomics approach based on ultra performance liquid chromatography-electrospray ionization-QTOF-mass spectrometry (UPLC-ESI-QTOF-MS) combined with multivariate data analysis, including principal component analysis (PCA) and (orthogonal) partial least squared discriminant analysis [(O)PLS-DA] was applied for the investigation of potential diagnostic biomarkers in plasma of OC patients. Patients with OC could be distinguished from healthy individuals and patients with benign gynecological tumor disease by this method, which shows a significant lipid perturbation in this disease. With the assistance of high resolution and high accuracy of MS and MS/MS data, the potential markers including lysophosphatidylcholines (LPCs), phosphatidylcholines (PCs) and triacylglycerols (TGs) with specific fatty acid chains, were identified. Interestingly, LPCs were up-regulated and PCs and TGs were down-regulated, compared OC group with benign tumor and normal control groups, and the glycerophospholipid metabolism emerged as a key pathway, in particular, the phospholipase A2 (PLA2) enzyme activity, that was disregulated in the disease. This study may provide new insight into underlying mechanisms for OC and proves that MS-based lipidomics is a powerful method in discovering new potential clinical biomarkers for diseases.
Keywords: Glycerophospholipid metabolism; Lipidomics; Mass spectrometry; Multivariate data analysis; Ovarian cancer.
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