Metabolomics (2018) 14:6
Metabolomic prediction of endometrial cancer
Ray O. Bahado‑Singh
· Amit Lugade
· Jayson Field
· Zaid Al‑Wahab
· BeomSoo Han
· Rupasri Mandal
Trent C. Bjorndahl
· Onur Turkoglu
· Stewart F. Graham
· David Wishart
· Kunle Odunsi
Received: 13 June 2017 / Accepted: 25 October 2017 / Published online: 1 December 2017
© Springer Science+Business Media, LLC 2017
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.
Keywords Endometrial cancer · Metabolomics · Biomarker · Nuclear magnetic resonance · Mass spectrometry
There has been an explosion of publications related to the
use of metabolomics for the analysis of complex disorders.
Cancer analytics has been a principal focus. Applications
and areas of promise for cancer metabolomics include early
detection (Urayama et al. 2010; Garcia et al. 2011) and dis-
ease staging (Fujiwaki et al. 2000). Further, metabolomics
also has the potential to develop personalized prognostic,
diagnostic approaches, and can also be applied to the moni-
toring of disease progression (personalized medicine) and
determining the eﬀects of therapeutic agents on cancer cells
(Nagrath et al. 2011).
To date relatively few studies in the literature have
addressed the metabolomics of gynecologic cancers. In
such studies, the focus has mainly been on ovarian cancer
(Garcia et al. 2011; Odunsi et al. 2005; Odunsi 2007; Turko-
glu et al. 2016). Based on the U.S. national cancer database
Electronic supplementary material The online version of this
article (https://doi.org/10.1007/s11306-017-1290-z) contains
supplementary material, which is available to authorized users.
* Ray O. Bahado-Singh
Department of Obstetrics and Gynecology, William
Beaumont Health, Royal Oak, MI 48073, USA
Center for Immunotherapy, Roswell Park Cancer Institute,
Buﬀalo, NY, USA
Department of Gynecologic Oncology, William Beaumont
Health, Royal Oak, MI, USA
Departments of Biological Sciences, University of Alberta,
Edmonton, AB T6G 2E8, Canada
Department of Computing Sciences, University of Alberta,
Edmonton, AB T6G 2E8, Canada
Department of Gynecologic Oncology, Roswell Park Cancer
Institute, Buﬀalo, NY, USA