Targeted metabolomic profiling of low and high grade serous epithelial ovarian cancer tissues: a pilot study

Gunjal Garg, Ali Yilmaz, Praveen Kumar, Onur Turkoglu, David G. Mutch, Matthew A. Powell, Barry Rosen, Ray O. Bahado-Singh, Stewart F. Graham

Research output: Contribution to journalArticlepeer-review

20 Scopus citations


Introduction: Epithelial ovarian cancer (EOC) remains the leading cause of death from gynecologic malignancies and has an alarming global fatality rate. Besides the differences in underlying pathogenesis, distinguishing between high grade (HG) and low grade (LG) EOC is imperative for the prediction of disease progression and responsiveness to chemotherapy. Objectives: The aim of this study was to investigate, the tissue metabolome associated with HG and LG serous epithelial ovarian cancer. Methods: A combination of one dimensional proton nuclear magnetic resonance (1D H NMR) spectroscopy and targeted mass spectrometry (MS) was employed to profile the tissue metabolome of HG, LG serous EOCs, and controls. Results: Using partial least squares-discriminant analysis, we observed significant separation between all groups (p < 0.05) following cross validation. We identified which metabolites were significantly perturbed in each EOC grade as compared with controls and report the biochemical pathways which were perturbed due to the disease. Among these metabolic pathways, ascorbate and aldarate metabolism was identified, for the first time, as being significantly altered in both LG and HG serous cancers. Further, we have identified potential biomarkers of EOC and generated predictive algorithms with AUC (CI) = 0.940 and 0.929 for HG and LG, respectively. Conclusion: These previously unreported biochemical changes provide a framework for future metabolomic studies for the development of EOC biomarkers. Finally, pharmacologic targeting of the key metabolic pathways identified herein could lead to novel and effective treatments of EOC.

Original languageEnglish
Article number154
Issue number12
StatePublished - Dec 1 2018


  • H NMR
  • Metabolomics
  • Multivariate data analysis
  • Serous ovarian cancer
  • Targeted mass spectrometry


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