Fan Zhang1, Yuanyuan Zhang1, Chaofu Ke2, Ang Li1, Wenjie Wang1, Kai Yang1, Huijuan Liu1, Hongyu Xie1, Kui Deng1, Weiwei Zhao1, Chunyan Yang1, Ge Lou3, Yan Hou4, Kang Li5. 1. Department of Biostatistics, Public Health School, Harbin Medical University, Harbin, China. 2. Department of Epidemiology and Biostatistics, School of Public Health, Medical College of Soochow University, Suzhou, China. 3. Department of Gynecology Oncology, The Affiliated Tumor Hospital of Harbin Medical University, Harbin, China. louge@ems.hrbmu.edu.cn. 4. Department of Biostatistics, Public Health School, Harbin Medical University, Harbin, China. houyan@ems.hrbmu.edu.cn. 5. Department of Biostatistics, Public Health School, Harbin Medical University, Harbin, China. likang@ems.hrbmu.edu.cn.
Abstract
BACKGROUND: Previous metabolomic studies have revealed that plasma metabolic signatures may predict epithelial ovarian cancer (EOC) recurrence. However, few studies have performed metabolic profiling of pre- and post-operative specimens to investigate EOC prognostic biomarkers. OBJECTIVE: The aims of our study were to compare the predictive performance of pre- and post-operative specimens and to create a better model for recurrence by combining biomarkers from both metabolic signatures. METHODS: Thirty-five paired plasma samples were collected from 35 EOC patients before and after surgery. The patients were followed-up until December, 2016 to obtain recurrence information. Metabolomics using rapid resolution liquid chromatography-mass spectrometry was performed to identify metabolic signatures related to EOC recurrence. The support vector machine model was employed to predict EOC recurrence using identified biomarkers. RESULTS: Global metabolomic profiles distinguished recurrent from non-recurrent EOC using both pre- and post-operative plasma. Ten common significant biomarkers, hydroxyphenyllactic acid, uric acid, creatinine, lysine, 3-(3,5-diiodo-4-hydroxyphenyl) lactate, phosphohydroxypyruvic acid, carnitine, coproporphyrinogen, L-beta-aspartyl-L-glutamic acid and 24,25-hydroxyvitamin D3, were identified as predictive biomarkers for EOC recurrence. The area under the receiver operating characteristic (AUC) values in pre- and post-operative plasma were 0.815 and 0.909, respectively; the AUC value after combining the two sets reached 0.964. CONCLUSION: Plasma metabolomic analysis could be used to predict EOC recurrence. While post-operative biomarkers have a predictive advantage over pre-operative biomarkers, combining pre- and post-operative biomarkers showed the best predictive performance and has great potential for predicting recurrent EOC.
BACKGROUND: Previous metabolomic studies have revealed that plasma metabolic signatures may predict epithelial ovarian cancer (EOC) recurrence. However, few studies have performed metabolic profiling of pre- and post-operative specimens to investigate EOC prognostic biomarkers. OBJECTIVE: The aims of our study were to compare the predictive performance of pre- and post-operative specimens and to create a better model for recurrence by combining biomarkers from both metabolic signatures. METHODS: Thirty-five paired plasma samples were collected from 35 EOC patients before and after surgery. The patients were followed-up until December, 2016 to obtain recurrence information. Metabolomics using rapid resolution liquid chromatography-mass spectrometry was performed to identify metabolic signatures related to EOC recurrence. The support vector machine model was employed to predict EOC recurrence using identified biomarkers. RESULTS: Global metabolomic profiles distinguished recurrent from non-recurrent EOC using both pre- and post-operative plasma. Ten common significant biomarkers, hydroxyphenyllactic acid, uric acid, creatinine, lysine, 3-(3,5-diiodo-4-hydroxyphenyl) lactate, phosphohydroxypyruvic acid, carnitine, coproporphyrinogen, L-beta-aspartyl-L-glutamic acid and 24,25-hydroxyvitamin D3, were identified as predictive biomarkers for EOC recurrence. The area under the receiver operating characteristic (AUC) values in pre- and post-operative plasma were 0.815 and 0.909, respectively; the AUC value after combining the two sets reached 0.964. CONCLUSION: Plasma metabolomic analysis could be used to predict EOC recurrence. While post-operative biomarkers have a predictive advantage over pre-operative biomarkers, combining pre- and post-operative biomarkers showed the best predictive performance and has great potential for predicting recurrent EOC.
Entities:
Keywords:
Biomarkers; Epithelial ovarian cancer (EOC); Metabolomics; Recurrence
Authors: Haiyu Zhang; Tingting Ge; Xiaoming Cui; Yan Hou; Chaofu Ke; Meng Yang; Kai Yang; Jingtao Wang; Bing Guo; Fan Zhang; Ge Lou; Kang Li Journal: Mol Biosyst Date: 2014-11-26
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