A plasma metabolomic signature discloses human breast cancer.
A plasma metabolomic signature discloses human breast cancer.
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DOI：
10.18632/oncotarget.14521
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发表时间：
2017-03-21
期刊：
影响因子：
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通讯作者：
Pamplona R
中科院分区：
文献类型：
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作者：
Jové M;Collado R;Quiles JL;Ramírez-Tortosa MC;Sol J;Ruiz-Sanjuan M;Fernandez M;de la Torre Cabrera C;Ramírez-Tortosa C;Granados-Principal S;Sánchez-Rovira P;Pamplona R
代谢组学是对生物样品中代谢物的综合性全球研究。在这项回顾性试点研究中，我们探讨了血清代谢组学特征是否可以区分人类乳腺癌的存在，无论癌症亚型如何。使用液相色谱-质谱平台对健康女性 (n = 20) 和诊断后乳腺癌患者 (n = 91) 的血浆样本进行分析。使用多变量统计和随机森林 (RF) 分类器创建用于诊断人类乳腺癌的代谢组学组。代谢组学正确区分了乳腺癌患者和健康对照受试者。在比较乳腺癌和健康对照组的 RF 监督类别预测分析中，RF 对乳腺癌患者和健康对照的样本均进行了 100% 准确分类。因此，袋内和袋外的组内误差均为 0。我们还在健康对照和癌症患者的血浆中发现了 1269 种不同浓度的代谢物；根据精确质量、保留时间和同位素分布，我们鉴定了 35 种代谢物。这些代谢物主要通过提供能量和合成必需生物分子的基石来支持细胞生长，并充当信号转导分子。 RF、显着性检验和错误发现率分析的综合结果确定了几种与乳腺癌密切相关的代谢物。在乳腺癌中，存在癌症的代谢组学特征，并且无论乳腺癌类型如何，都可以在患者血浆中检测到。
Metabolomics is the comprehensive global study of metabolites in biological samples. In this retrospective pilot study we explored whether serum metabolomic profile can discriminate the presence of human breast cancer irrespective of the cancer subtype. Plasma samples were analyzed from healthy women (n = 20) and patients with breast cancer after diagnosis (n = 91) using a liquid chromatography-mass spectrometry platform. Multivariate statistics and a Random Forest (RF) classifier were used to create a metabolomics panel for the diagnosis of human breast cancer. Metabolomics correctly distinguished between breast cancer patients and healthy control subjects. In the RF supervised class prediction analysis comparing breast cancer and healthy control groups, RF accurately classified 100% both samples of the breast cancer patients and healthy controls. So, the class error for both group in and the out-of-bag error were 0. We also found 1269 metabolites with different concentration in plasma from healthy controls and cancer patients; and basing on exact mass, retention time and isotopic distribution we identified 35 metabolites. These metabolites mostly support cell growth by providing energy and building stones for the synthesis of essential biomolecules, and function as signal transduction molecules. The collective results of RF, significance testing, and false discovery rate analysis identified several metabolites that were strongly associated with breast cancer. In breast cancer a metabolomics signature of cancer exists and can be detected in patient plasma irrespectively of the breast cancer type.