Oncotarget

Research Papers:

Metabonomic analysis of ovarian tumour cyst fluid by proton nuclear magnetic resonance spectroscopy

Michael Kyriakides, Nona Rama, Jasmin Sidhu, Hani Gabra, Hector C. Keun and Mona El-Bahrawy _

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Oncotarget. 2016; 7:7216-7226. https://doi.org/10.18632/oncotarget.6891

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Abstract

Michael Kyriakides1, Nona Rama1, Jasmin Sidhu1, Hani Gabra1, Hector C. Keun1, Mona El-Bahrawy2,3

1Department of Surgery and Cancer, Imperial College London, London, United Kingdom

2Department of Histopathology, Hammersmith Hospital, Imperial College London, London, United Kingdom

3Department of Pathology, Faculty of Medicine, University of Alexandria, Alexandria, Egypt

Correspondence to:

Mona El-Bahrawy, e-mail: m.elbahrawy@imperial.ac.uk

Keywords: cyst, metabonomics, ovarian, tumour, metabolite

Received: June 25, 2015    Accepted: December 30, 2015    Published: January 12, 2016

ABSTRACT

The majority of ovarian tumours are of the epithelial type, which can be sub classified as benign, borderline or malignant. Epithelial tumours usually have cystic spaces filled with cyst fluid, the metabolic profile of which reflects the metabolic activity of the tumour cells, due to their close proximity. The approach of metabonomics using 1H-NMR spectroscopy was employed to characterize the metabolic profiles of ovarian cyst fluid samples (n = 23) from benign, borderline and malignant ovarian tumours in order to shed more light into ovarian tumour and cancer development. The analysis revealed that citrate was elevated in benign versus malignant tumours, while the amino acid lysine was elevated in malignant versus non-malignant tumours, both at a 5% significance level. Choline and lactate also had progressively increasing levels from benign to borderline to malignant samples. Finally, hypoxanthine was detected exclusively in a sub-cohort of the malignant tumours. This metabonomic study demonstrates that ovarian cyst fluid samples have potential to be used to distinguish between the different types of ovarian epithelial tumours. Furthermore, the respective metabolic profiles contain mechanistic information which could help identify biomarkers and therapeutic targets for ovarian tumours.


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