Human plasma metabolomics for identifying differential metabolites and predicting molecular subtypes of breast cancer.
Human plasma metabolomics for identifying differential metabolites and predicting molecular subtypes of breast cancer.
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DOI：
10.18632/oncotarget.7155
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发表时间：
2016-03-01
期刊：
影响因子：
--
通讯作者：
Qi LW
中科院分区：
文献类型：
--
作者：
Fan Y;Zhou X;Xia TS;Chen Z;Li J;Liu Q;Alolga RN;Chen Y;Lai MD;Li P;Zhu W;Qi LW
这项工作的目的是确定差异代谢物和预测乳腺癌（BC）的分子亚型。从96名BC患者和79名正常参与者中收集血浆样本。采用液相色谱-质谱联用和气相色谱-质谱联用技术，基于多元统计数据分析确定代谢特征。我们观察到64个差异代谢物在BC和正常组之间。与人表皮生长因子受体2（HER 2）阴性患者相比，HER 2阳性组表现出有氧糖酵解升高，脂肪酸合成增加，Krebs循环减少。与雌激素受体（ER）阴性组相比，ER阳性组丙氨酸、天冬氨酸和谷氨酸代谢升高，甘油脂质代谢降低，嘌呤代谢增强。一组8个差异代谢物，包括肉毒碱，溶血磷脂酰胆碱（20：4），脯氨酸，丙氨酸，溶血磷脂酰胆碱（16：1），甘氨鹅去氧胆酸，缬氨酸，和2-辛烯二酸，被确定为BC亚型的分类。这些标志物显示出潜在的诊断价值，训练集（n=51）的平均曲线下面积为0.925（95% CI 0.867-0.983），测试集（n=45）的平均曲线下面积为0.893（95% CI 0.847-0.939）。人血浆代谢组学可用于识别差异代谢物和预测乳腺癌亚型。
This work aims to identify differential metabolites and predicting molecular subtypes of breast cancer (BC). Plasma samples were collected from 96 BC patients and 79 normal participants. Metabolic profiles were determined by liquid chromatography-mass spectrometry and gas chromatography-mass spectrometry based on multivariate statistical data analysis. We observed 64 differential metabolites between BC and normal group. Compared to human epidermal growth factor receptor 2 (HER2)-negative patients, HER2-positive group showed elevated aerobic glycolysis, gluconeogenesis, and increased fatty acid biosynthesis with reduced Krebs cycle. Compared with estrogen receptor (ER)-negative group, ER-positive patients showed elevated alanine, aspartate and glutamate metabolism, decreased glycerolipid catabolism, and enhanced purine metabolism. A panel of 8 differential metabolites, including carnitine, lysophosphatidylcholine (20:4), proline, alanine, lysophosphatidylcholine (16:1), glycochenodeoxycholic acid, valine, and 2-octenedioic acid, was identified for the classification of BC subtypes. These markers showed potential diagnostic value with average area under the curve at 0.925 (95% CI 0.867-0.983) for the training set (n=51) and 0.893 (95% CI 0.847-0.939) for the test set (n=45). Human plasma metabolomics is useful in identifying differential metabolites and predicting breast cancer subtypes.