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Predicting ovarian cancer recurrence by plasma metabolic profiles before and after surgery

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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.

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Abbreviations

EOC:

Epithelial ovarian cancer

AUC:

Area under the receiver operating characteristic

RRLC/MS:

Rapid resolution liquid chromatography–mass spectrometry

ESI+/−:

Electrospray ionization positive/negative

QC:

Quality control

PCA:

Principal component analysis

PLS-DA:

Partial least-squares discriminant analysis

VIP:

Variable importance in the projection

SVM:

Support vector machine

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Funding

This study was funded by National Natural Science Foundation of China (81473072, 81573256, 81472028).

Author information

Authors and Affiliations

  1. Department of Biostatistics, Public Health School, Harbin Medical University, Harbin, China

    Fan Zhang, Yuanyuan Zhang, Ang Li, Wenjie Wang, Kai Yang, Huijuan Liu, Hongyu Xie, Kui Deng, Weiwei Zhao, Chunyan Yang, Yan Hou & Kang Li

  2. Department of Epidemiology and Biostatistics, School of Public Health, Medical College of Soochow University, Suzhou, China

    Chaofu Ke

  3. Department of Gynecology Oncology, The Affiliated Tumor Hospital of Harbin Medical University, Harbin, China

    Ge Lou

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  1. Fan Zhang
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  2. Yuanyuan Zhang
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  12. Ge Lou
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  13. Yan Hou
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  14. Kang Li
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Corresponding authors

Correspondence to Ge Lou, Yan Hou or Kang Li.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Informed consent was obtained from all individual participants included in the study.

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Zhang, F., Zhang, Y., Ke, C. et al. Predicting ovarian cancer recurrence by plasma metabolic profiles before and after surgery. Metabolomics 14, 65 (2018). https://doi.org/10.1007/s11306-018-1354-8

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