Discrimination between malignant and benign ovarian tumors by plasma metabolomic profiling using ultra performance liquid chromatography/mass spectrometry

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• 作者：Tao Zhang^a ; Xiaoyan Wu^a ; Mingzhu Yin^b ; Lijun Fan^a ; Haiyu Zhang^a ; Falin Zhao^c ; Wang Zhang^a ; Chaofu Ke^a ; Guangming Zhang^a ; Yan Hou^a ; Xiaohua Zhou^d ; Ge Lou^b ; ^{louge@ems.hrbmu.edu.cn} ; Kang Li^a ; ^{likang@ems.hrbmu.edu.cn}
• 关键词：EOC ; epithelial ovarian cancer ; BOT ; benign ovarian tumors ; CA125 ; cancer antigen 125 ; UPLC-QTOF/MS ; ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry ; RF ; random forest ; PLS-DA ; partial least squares discriminant analysi
• 刊名：Clinica Chimica Acta
• 出版年：2012
• 期刊代码：54_00098981
• 类别：med
• 出版时间：18 May, 2012
• 卷：413
• 期：9-10
• 页码：861-868
• 文件大小：1265 K

摘要

Background

Discrimination between epithelial ovarian cancer (EOC) and benign ovarian tumor (BOT) has always been difficult in clinical practice. We investigated the application of metabolomics in distinguishing EOC and BOT and tried to discover valuable biomarkers.

Methods

Plasma metabolomic profiling was performed using ultra-performance liquid chromatography mass spectrometry (UPLC/MS). Partial least-squares discriminant analysis was employed to classify EOC and BOT, and reveal their metabolic differences. The area under the receiver-operating characteristic curve (AUC) was utilized to evaluate the predictive performance of the metabolic profiles for external validation set.

Results

The metabolomic profiles consisting of 535 metabolites revealed a clear separation between EOC and BOT, with AUC of 0.86 for the external validation set. 6 metabolic biomarkers were identified, and the plasma concentrations of the 4 ascertained biomarkers (L-tryptophan, LysoPC(18:3), LysoPC(14:0), and 2-Piperidinone) were lower in EOC patients than those in BOT patients. Among them, tryptophan and LysoPC have been suspected to participate in cancer progression, and 2-Piperidinone might be a novel biomarker for EOC.

Conclusions

Metabolomics could be used to discriminate EOC from BOT in clinical practice, and the identified metabolic biomarkers might be important on investigating the biological mechanisms of EOC.