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. 2016 Jun 23;7(10):1265-72.
doi: 10.7150/jca.15074. eCollection 2016.

Use of Plasma Metabolomics to Identify Diagnostic Biomarkers for Early Stage Epithelial Ovarian Cancer

Lijun Fan  1 , Mingzhu Yin  2 , Chaofu Ke  3 , Tingting Ge  2 , Guangming Zhang  3 , Wang Zhang  3 , Xiaohua Zhou  4 , Ge Lou  2 , Kang Li  3
Affiliations

Use of Plasma Metabolomics to Identify Diagnostic Biomarkers for Early Stage Epithelial Ovarian Cancer

Lijun Fan et al. J Cancer. .

Abstract

The early detection of ovarian carcinoma is difficult due to the absence of recognizable physical symptoms and a lack of sensitive screening methods. The currently available biomarkers (such as CA125 and HE4) are insufficiently reliable to distinguish early stage (I/II) epithelial ovarian cancer (EOC) patients from normal individuals because they possess a relatively poor sensitivity and specificity. To evaluate the application of metabolomics to biomarker discovery in the early stages of epithelial ovarian cancer (EOC), plasma samples from 21 early stage EOC patients and 31 healthy controls were analyzed with ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC/Q-Tof/MS) in conjunction with multivariate statistical analysis. Eighteen metabolites, including lysophospholipids, 2-piperidone and MG (18:2), were found to be disturbed in early stage EOC with satisfactory diagnostic accuracy (AUC=0.920). These biomarkers were specifically validated in the EOC nude mouse model, and five of the biomarkers (lysophospholipids, adrenoyl ethanolamide et al.) were highly suspected of being associated with EOC because they were differentially expressed with the same tendency in the EOC nude mice versus normal controls. In conclusion, the selected metabolic biomarkers have considerable utility and significant potential for diagnosing early ovarian cancer and investigating its underlying mechanisms.

Keywords: biomarkers; diagnosis.; epithelial ovarian cancer; metabolomics; plasma.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interest exists.

Figures

Figure 1
Figure 1
An overview of the workflow of plasma metabolomics for early stage (I/II) epithelial ovarian cancer (EOC) patients and controls using ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC/Q-Tof/MS).
Figure 2
Figure 2
The hierarchical clustering of the raw data. 'e' represents epithelial ovarian cancer patients, 'c' represents controls.
Figure 3
Figure 3
(A) Principle component analysis (PCA) plots of factor scores for the first three principal components (t, t, t3) showing the considerable separation achieved between EOC plasma samples (red box) and healthy premenopausal controls (black box); (B) Orthogonal partial least-squares discriminate analysis(OPLS-DA) scores plot for the top two components that differentiate EOC patients (red triangle) and controls (black dot); (C) the validation plot obtained from 100 permutation tests with R2(green triangle) and Q2(blue box); and (D) OPLS-DA scores plot for the subjects with high CA125 levels (red triangle) and low CA125 levels (black dot).
Figure 4
Figure 4
The relative quantity of the five potential biomarkers in the plasma of nude mice. Data represent the means and standard deviation of the metabolites in the EOC nude mice and controls. * represents p value of <0.001.
Figure 5
Figure 5
The pathway of LysoPC16:0.
See this image and copyright information in PMC

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