Distinct plasma lipids profiles of recurrent ovarian cancer by liquid chromatography-mass spectrometry
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Junnan Li1,*, Hongyu Xie1,*, Ang Li1, Jinlong Cheng2, Kai Yang1, Jingtao Wang1, Wenjie Wang1, Fan Zhang1, Zhenzi Li1, Harman S. Dhillon3, Margarita S. Openkova3, Xiaohua Zhou4, Kang Li1 and Yan Hou1,5
1Department of Epidemiology and Biostatistics, School of Public Health, Harbin Medical University, Harbin 150086, P.R. China
2Department of Gynecology Oncology, The Affiliated Tumor Hospital of Harbin Medical University, Harbin 150086, P.R. China
3Harbin Medical University, Harbin 150086, P.R. China
4Department of Biostatistics, University of Washington, Seattle 96596, WA, U.S.A
5Key Laboratory of Cardiovascular Medicine Research (Harbin Medical University), Ministry of Education, Harbin 150086, P.R. China
*These authors contributed equally to this work
Yan Hou, email: firstname.lastname@example.org
Kang Li, email: email@example.com
Keywords: epithelial ovarian cancer, lipidomics, recurrence, early recurrence
Received: June 03, 2016 Accepted: July 29, 2016 Published: August 25, 2016
Epithelial ovarian cancer (EOC) is the most deadly gynecologic malignancy worldwide due to its high recurrence rate after surgery and chemotherapy. There is a critical need for discovery of novel biomarkers for EOC recurrence providing higher prediction power than that of the present ones. Lipids have been reported to associate with development and progression of cancer. In the current study, we aim to identify and validate the lipids which were relevant to the ovarian cancer recurrence based on plasma lipidomics performed by ultra-performance liquid chromatography coupled with mass spectrometry. In order to fulfill this objective, plasma from 70 EOC patients with follow up information was obtained. The results revealed that patients with and without recurrence could be clearly distinguished based on their lipid profiles. Thirty-one lipid metabolites were identified as potential biomarkers for EOC recurrence. The AUC value of these metabolite combinations for predicting EOC recurrence was 0.897. In terms of clinical applicability, LysoPG(20:5) arose as a potential EOC recurrence predictive biomarker to increase the predictive power of clinical predictors from AUC value 0.739 to 0.875. Additionally, we still found that individuals with early relapses (< 6 months) had a distinctive metabolomic pattern compared with late EOC and non-EOC recurrence subjects. Interestingly, decreased levels of triglycerides (TGs) were found to be a specific metabolic feature foreshadowing an early relapse. In conclusion, plasma lipidomics study could be used for predicting EOC recurrences, as well as early and late recurrent cases. The lipid biomarker research improves the predictive power of clinical predictors and the identified biomarkers are of great prognostic and therapeutic potential.
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