Abstract
Breast cancer accounts for the largest number of newly diagnosed cases in female cancer patients. Although mammography is a powerful screening tool, about 20% of breast cancer cases cannot be detected by this method. New diagnostic biomarkers for breast cancer are necessary. Here, we used a mass spectrometry-based quantitative metabolomics method to analyze plasma samples from 55 breast cancer patients and 25 healthy controls. A number of 30 patients and 20 age-matched healthy controls were used as a training dataset to establish a diagnostic model and to identify potential biomarkers. The remaining samples were used as a validation dataset to evaluate the predictive accuracy for the established model. Distinct separation was obtained from an orthogonal partial least squares-discriminant analysis (OPLS-DA) model with good prediction accuracy. Based on this analysis, 39 differentiating metabolites were identified, including significantly lower levels of lysophosphatidylcholines and higher levels of sphingomyelins in the plasma samples obtained from breast cancer patients compared with healthy controls. Using logical regression, a diagnostic equation based on three metabolites (lysoPC a C16:0, PC ae C42:5 and PC aa C34:2) successfully differentiated breast cancer patients from healthy controls, with a sensitivity of 98.1% and a specificity of 96.0%.
Highlights
According to the American Cancer Society estimation, breast cancer remains to be one of the most commonly diagnosed and death-related cancers in women in the United States [1]
A clear separation between sample groups was observed, with most of the healthy controls scattering at the top of the plot, while most of the breast cancer samples were scattered across the bottom half (Figure 1)
To further specify the metabolic variations associated with cancer morbidity, a supervised orthogonal partial least squares-discriminant analysis (OPLS-DA) model was established with one predictive component and two orthogonal components (R2X = 0.464, R2Y = 0.884, Q2 = 0.756)
Summary
According to the American Cancer Society estimation, breast cancer remains to be one of the most commonly diagnosed and death-related cancers in women in the United States [1]. Mammography is the most acceptable and effective screening procedure for the detection of breast cancer and was recommended by the U.S Preventive Services Task Force (USPSTF) to women over 40 years old [3]. Because of the high false positive rate of this screen, the USPSTF revised their recommendation to a reduced frequency of mammogram screening in 2009 [4] Other imaging techniques, such as ultrasonography and magnetic resonance imaging, have been used in breast cancer screening. Plasma (or serum) biomarkers (such as antigens and protein patterns) are promising [6,7]; they are still far from clinical use Some tumor markers, such as CA15.3 and CA27.29, are recommended only for therapeutic monitoring, but not screening [8]. New effective biomarkers for breast cancer screening that can be used individually or in combination with other existing methods are urgently needed
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