基于液相色谱-质谱技术的乳腺癌转移相关 代谢标志物的筛选

摘要：目的 应用代谢组学技术筛选与乳腺癌转移相关的代谢标志物。方法 收集100例乳腺癌患者和50例 健康志愿者的血清标本，采用高效液相色谱-轨道离子阱质谱联用（HPLC-LTQ Orbitrap XL MS）代谢组学研究平台分 析乳腺癌未转移患者、乳腺癌转移患者和健康人群血清标本的代谢轮廓，并通过模式识别方法结合非参数检验对数 据进行分析。结果 由乳腺癌未转移组、乳腺癌转移组和健康对照组的代谢轮廓构建的正交偏最小二乘判别分析 （OPLS-DA）模型具有很好的判别能力（R2=95.2%，Q2=86.7%），可以鉴别出用于区分乳腺癌转移与否的8个代谢标志 物，包括溶血磷脂酸［18∶1（9Z）/0∶0］、溶血磷脂酰胆碱（18∶0）、溶血磷脂酰胆碱［20∶3（5Z，8Z，11Z）］、胆碱、磷酸二羟 丙酮（18∶0e）、2R，3S-番石榴酸、芥酸、L-氢化乳清酸。结论 利用代谢组学方法获得的血清代谢轮廓可以用来构建 区分模型和寻找乳腺癌转移相关的代谢标志物，为乳腺癌的早期诊治、预后评估和药物治疗靶点的选择提供支持和 依据。

Screening of the metabolic markers associated with breast cancer metastasis based on liquid chromatography-mass spectrometry technology

Abstract: Objective To screen the metabolic markers associated with breast cancer metastasis through metabonomics technology. Methods Serum samples from 100 patients with breast cancer and 50 healthy volunteers were collected. The metabolic profiles of serum specimens from breast cancer patients with metastasis, breast cancer patients without metastasis and healthy people were analyzed by high performance liquid chromatography combined with a LTQ Orbitrap XL mass spectrometer (HPLC-LTQ Orbitrap XL MS) platform, and then the data were analyzed by the pattern recognition methods and nonparametric test. Results The orthogonal partial least squares-discriminant analysis (OPLS DA) mode (R2=95.2%, Q2=86.7%) was constructed by the serum metabolic profiles of breast cancer patients with metastasis, breast cancer patients without metastasis and healthy people, which showed good discrimination ability and could identify eight metabolic markers including LPA［18∶1(9Z)/0∶0］, LysoPC(18∶0), LysoPC［20∶3(5Z,8Z,11Z)］, Choline, DHAP(18∶0e), (2R,3S)-Piscidic acid, Erucic acid, L-Dihydroorotic acid used to distinguish breast cancer metastasis. Conclusion The serum metabolic profiling obtained by metabolomic methods can be used to construct the discrimination mode and discover the metabolic biomarkers associated with breast cancer metastasis, which will provide the support and basis for the early diagnosis and treatment, prognosis evaluation, choice of drug therapy targets for breast cancer.