title: Metabolic profiling and novel plasma biomarkers for predicting survival in epithelial ovarian cancer.
abstract: 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.
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Harbin Medical University, Harbin 150086, China.
authors.forename: Hongyu
authors.initials: H
authors.lastname: Xie
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Harbin Medical University, Harbin 150086, China.
authors.forename: Yan
authors.initials: Y
authors.lastname: Hou
authors.affiliation: Department of Gynecology Oncology, the Tumor Hospital, Harbin Medical University, Harbin 150086, China.
authors.forename: Jinlong
authors.initials: J
authors.lastname: Cheng
authors.affiliation: Harbin Medical University, Harbin 150086, China.
authors.forename: Margarita S
authors.initials: MS
authors.lastname: Openkova
authors.affiliation: Department of Gynecology Oncology, the Tumor Hospital, Harbin Medical University, Harbin 150086, China.
authors.forename: Bairong
authors.initials: B
authors.lastname: Xia
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Harbin Medical University, Harbin 150086, China.
authors.forename: Wenjie
authors.initials: W
authors.lastname: Wang
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Harbin Medical University, Harbin 150086, China.
authors.forename: Ang
authors.initials: A
authors.lastname: Li
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Harbin Medical University, Harbin 150086, China.
authors.forename: Kai
authors.initials: K
authors.lastname: Yang
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Harbin Medical University, Harbin 150086, China.
authors.forename: Junnan
authors.initials: J
authors.lastname: Li
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Harbin Medical University, Harbin 150086, China.
authors.forename: Huan
authors.initials: H
authors.lastname: Xu
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Harbin Medical University, Harbin 150086, China.
authors.forename: Chunyan
authors.initials: C
authors.lastname: Yang
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Harbin Medical University, Harbin 150086, China.
authors.forename: Libing
authors.initials: L
authors.lastname: Ma
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Harbin Medical University, Harbin 150086, China.
authors.forename: Zhenzi
authors.initials: Z
authors.lastname: Li
authors.affiliation: School of Basic Medical Sciences, Heilongjiang University of Chinese Medicine, Harbin 150040, China.
authors.forename: Xin
authors.initials: X
authors.lastname: Fan
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Harbin Medical University, Harbin 150086, China.
authors.forename: Kang
authors.initials: K
authors.lastname: Li
authors.affiliation: Department of Gynecology Oncology, the Tumor Hospital, Harbin Medical University, Harbin 150086, China.
authors.forename: Ge
authors.initials: G
authors.lastname: Lou
mesh_terms: D014408:Biomarkers, Tumor; D000077216:Carcinoma, Ovarian Epithelial; D002853:Chromatography, Liquid; D019295:Computational Biology / Q000379:methods; D005500:Follow-Up Studies; D006801:Humans; D053208:Kaplan-Meier Estimate; D013058:Mass Spectrometry; D055432:Metabolomics / Q000379:methods; D009375:Neoplasms, Glandular and Epithelial / Q000097:blood* / Q000401:mortality*; D010051:Ovarian Neoplasms / Q000097:blood* / Q000401:mortality*; D011379:Prognosis; D012372:ROC Curve; D015203:Reproducibility of Results; D057188:Workflow
chemical_list: D014408:Biomarkers, Tumor
keywords: epithelial ovarian cancer; metabolomics; plasma; prediction; survival