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Metabolomic prediction of endometrial cancer

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Abstract

Introduction

Endometrial cancer (EC) is associated with metabolic disturbances including obesity, diabetes and metabolic syndrome. Identifying metabolite biomarkers for EC detection has a crucial role in reducing morbidity and mortality.

Objective

To determine whether metabolomic based biomarkers can detect EC overall and early-stage EC.

Methods

We performed NMR and mass spectrometry based metabolomic analyses of serum in EC cases versus controls. A total of 46 early-stage (FIGO stages I–II) and 10 late-stage (FIGO stages III–IV) EC cases constituted the study group. A total of 60 unaffected control samples were used. Patients and controls were divided randomly into a discovery group (n = 69) and an independent validation group (n = 47). Predictive algorithms based on biomarkers and demographic characteristics were generated using logistic regression analysis.

Results

A total of 181 metabolites were evaluated. Extensive changes in metabolite levels were noted in the EC versus the control group. The combination of C14:2, phosphatidylcholine with acyl-alkyl residue sum C38:1 (PCae C38:1) and 3-hydroxybutyric acid had an area under the receiver operating characteristics curve (AUC) (95% CI) = 0.826 (0.706–0.946) and a sensitivity = 82.6%, and specificity = 70.8% for EC overall. For early EC prediction: BMI, C14:2 and PC ae C40:1 had an AUC (95% CI) = 0.819 (0.689–0.95) and a sensitivity = 72.2% and specificity = 79.2% in the validation group.

Conclusions

EC is characterized by significant perturbations in important cellular metabolites. Metabolites accurately detected early-stage EC cases and EC overall which could lead to the development of non-invasive biomarkers for earlier detection of EC and for monitoring disease recurrence.

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Authors and Affiliations

  1. Department of Obstetrics and Gynecology, William Beaumont Health, Royal Oak, MI, 48073, USA

    Ray O. Bahado-Singh, Onur Turkoglu & Stewart F. Graham

  2. Center for Immunotherapy, Roswell Park Cancer Institute, Buffalo, NY, USA

    Amit Lugade & Kunle Odunsi

  3. Department of Gynecologic Oncology, William Beaumont Health, Royal Oak, MI, USA

    Jayson Field & Zaid Al-Wahab

  4. Departments of Biological Sciences, University of Alberta, Edmonton, AB, T6G 2E8, Canada

    BeomSoo Han, Rupasri Mandal, Trent C. Bjorndahl & David Wishart

  5. Department of Computing Sciences, University of Alberta, Edmonton, AB, T6G 2E8, Canada

    David Wishart

  6. Department of Gynecologic Oncology, Roswell Park Cancer Institute, Buffalo, NY, USA

    Kunle Odunsi

Authors
  1. Ray O. Bahado-Singh
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  2. Amit Lugade
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  8. Onur Turkoglu
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  9. Stewart F. Graham
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  10. David Wishart
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Corresponding author

Correspondence to Ray O. Bahado-Singh.

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Conflict of interest

The authors declare no potential conflicts of interest.

Ethical approval

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. The study protocol was approved by the IRB - I 03103 “RPCI Data Bank and BioRepository (DBBR)”. Samples and de-identified clinical data was distributed for analysis under RPCI IRB-approved protocol BDR 048414 “Metabolomic analysis of gynecologic and non-gynecologic cancers: a pilot study”.

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

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Bahado-Singh, R.O., Lugade, A., Field, J. et al. Metabolomic prediction of endometrial cancer. Metabolomics 14, 6 (2018). https://doi.org/10.1007/s11306-017-1290-z

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