Metabolomic prediction of endometrial cancer

Ray O. Bahado-Singh
Amit Lugade, Roswell Park Cancer Institute
Jayson Field
Zaid Al-Wahab
Beom Soo Han, University of Alberta
Rupasri Mandal, University of Alberta
Trent C. Bjorndahl, University of Alberta
Onur Turkoglu
Stewart F. Graham
David Wishart, University of Alberta
Kunle Odunsi, Roswell Park Cancer Institute


© 2017, Springer Science+Business Media, LLC. 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.