Identification of metabolic biomarkers for diagnosis of epithelial ovarian cancer using internal extraction electrospray ionization mass spectrometry (iEESI-MS)
Affiliations: [a] Department of Gynecologic Oncology, The First Hospital of Jilin University, Changchun, Jilin, China | [b] Weiming Environmental Molecular Diagnostics (Changshu) Co.Ltd. Changshun, Jiangsu, China | [c] College of New Energy and Environment, Key Lab of Groundwater Resource and Environment Ministry of Education, Jilin University, Changchun, Jilin, China
Correspondence:
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Corresponding author: Zhentong Wei and Songling Zhang, Department of Gynecologic Oncology, The First Hospital of Jilin University, No. 1 Xinmin Street, Chaoyang District, Changchun, Jilin 130021, China. E-mail: [email protected] and [email protected].
Note: [1] These authors have contributed equally to this work and share first authorship.
Abstract: BACKGROUND: Epithelial ovarian cancer (EOC) is the leading cause of death from gynecologic malignancies. The poor prognosis of EOC is mainly due to its asymptomatic early stage, lack of effective screening methods, and a late diagnosis in the advanced stages of the disease. OBJECTIVE: This study investigated metabolomic abnormalities in epithelial ovarian cancers. METHODS: Our study developed a novel strategy to rapidly identify the metabolic biomarkers in the plasma of the EOC patients using Internal Extraction Electrospray Ionization Mass Spectrometry (IEESI-MS) and Liquid Chromatography-mass Spectrometry (HPLC-MS), which could distinguish the differential metabolites in between plasma samples collected from 98 patients with epithelial ovarian cancer, including 78 cases with original (P), and 20 cases with self-configuration (ZP), as well as 60 healthy subjects, including 30 cases in the original sample (H), 30 cases in self-configuration (ZH), and 6 cases in a blind sample (B). RESULTS: Our study detected 880 metabolites based on criteria variable importance in projection (VIP) > 1, among which 26 metabolites were selected for further identification. They are mainly metabolism-related lipids, amino acids, nucleic acids, and others. The metabolic pathways associated with the differential metabolites were explored by the KEGG analysis, a comprehensive database that integrates genome, chemistry, and system function information. The abnormal metabolites of EOC patients identified by IEESI-MS and HPLC-MS included Lysophosphatidylcholine (16:0) [Lyso PC (16:0)], L-Phenylalanine, L-Leucine, Phenylpyruvic acid, L-Tryptophan, and L-Histidine. CONCLUSIONS: Identifying the abnormal metabolites of EOC patients through metabolomics analyses could provide a new strategy to identify valuable potential biomarkers for the screening and early diagnosis of EOC.
