Mass spectrometry-based quantitative metabolomics revealed a distinct lipid profile in breast cancer patients

Yunping Qiu, Bingsen Zhou, Mingming Su, Sarah Baxter, Xiaojiao Zheng, Xueqing Zhao, Yun Yen, Wei Jia

研究成果: 雜誌貢獻文章同行評審

75 引文 斯高帕斯(Scopus)

摘要

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%.

原文英語
頁(從 - 到)8047-8061
頁數15
期刊International journal of molecular sciences
14
發行號4
DOIs
出版狀態已發佈 - 4月 2013
對外發佈

ASJC Scopus subject areas

  • 催化
  • 分子生物學
  • 光譜
  • 電腦科學應用
  • 物理與理論化學
  • 有機化學
  • 無機化學

指紋

深入研究「Mass spectrometry-based quantitative metabolomics revealed a distinct lipid profile in breast cancer patients」主題。共同形成了獨特的指紋。

引用此