Metabolic profiling and novel plasma biomarkers for predicting survival in epithelial ovarian cancer
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Hongyu Xie1,*, Yan Hou1,*, Jinlong Cheng2, Margarita S. Openkova3, Bairong Xia2, Wenjie Wang1, Ang Li1, Kai Yang1, Junnan Li1, Huan Xu1, Chunyan Yang1, Libing Ma1, Zhenzi Li1, Xin Fan4, Kang Li1 and Ge Lou2
1Department of Epidemiology and Biostatistics, School of Public Health, Harbin Medical University, Harbin 150086, China
2Department of Gynecology Oncology, the Tumor Hospital, Harbin Medical University, Harbin 150086, China
3Harbin Medical University, Harbin 150086, China
4School of Basic Medical Sciences, Heilongjiang University of Chinese Medicine, Harbin 150040, China
*These authors have contributed equally to this work
Kang Li, email: firstname.lastname@example.org
Ge Lou, email: email@example.com
Keywords: epithelial ovarian cancer, metabolomics, plasma, survival, prediction
Received: October 17, 2016 Accepted: February 22, 2017 Published: March 31, 2017
Epithelial ovarian cancer (EOC) is one of the most lethal gynecological malignancies around the world, and patients with ovarian cancer always have an extremely poor chance of survival. Therefore, it is meaningful to develop a highly efficient model that can predict the overall survival for EOC. In order to investigate whether metabolites could be used to predict the survival of EOC, we performed a metabolic analysis of 98 plasma samples with follow-up information, based on the ultra-performance liquid chromatography mass spectrometry (UPLC/MS) systems in both positive (ESI+) and negative (ESI-) modes. Four metabolites: Kynurenine, Acetylcarnitine, PC (42:11), and LPE(22:0/0:0) were selected as potential predictive biomarkers. The AUC value of metabolite-based risk score, together with pathological stages in predicting three-year survival rate was 0.80. The discrimination performance of these four biomarkers between short-term mortality and long-term survival was excellent, with an AUC value of 0.82. In conclusion, our plasma metabolomics study presented the dysregulated metabolism related to the survival of EOC, and plasma metabolites could be utilized to predict the overall survival and discriminate the short-term mortality and long-term survival for EOC patients. These results could provide supplementary information for further study about EOC survival mechanism and guiding the appropriate clinical treatment.
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