色谱 ›› 2022, Vol. 40 ›› Issue (6): 541-546.DOI: 10.3724/SP.J.1123.2022.01001

• 研究论文 • 上一篇    下一篇

基于液相色谱-串联质谱的结肠癌血清氧固醇标志物筛选

马占君1, 李振国2, 王欢1, 王仁军1, 韩晓菲1,*()   

  1. 1.大连大学生命科学与技术学院, 辽宁省糖脂代谢研究重点实验室, 大连合成生物学重点实验室, 辽宁 大连 116622
    2.大连医科大学附属第二医院, 辽宁 大连 116023
  • 收稿日期:2022-01-05 出版日期:2022-06-08 发布日期:2022-05-26
  • 通讯作者: 韩晓菲
  • 基金资助:
    国家自然科学基金(81400337);国家自然科学基金(81673417)

Screening of serum oxysterol biomarkers for colon cancer by liquid chromatography-tandem mass spectrometry

MA Zhanjun1, LI Zhenguo2, WANG Huan1, WANG Renjun1, HAN Xiaofei1,*()   

  1. 1. College of Life Science and Technology, Dalian University, Key Laboratory of Carbohydrate and Lipid Metabolism Research, Key Laboratory of Dalian Synthetic Biology, Dalian 116622, China
    2. The Second Hospital of Dalian Medical University, Dalian 116023, China
  • Received:2022-01-05 Online:2022-06-08 Published:2022-05-26
  • Contact: HAN Xiaofei
  • Supported by:
    National Natural Science Foundation of China(81400337);National Natural Science Foundation of China(81673417)

摘要:

结肠癌(CC)是全球常见恶性肿瘤之一,发病率呈逐年上升趋势,目前没有有效的标志物用于疾病早期诊断和干预跟踪。胆固醇及其氧化衍生物氧固醇在众多恶性肿瘤发生发展中发挥关键作用。该研究采用液相色谱-串联质谱(LC-MS/MS)技术,对CC临床血清样本中胆固醇及相关10种氧固醇代谢物进行了定性定量分析,并采用偏最小二乘判别分析(PLS-DA)和正交偏最小二乘判别分析(OPLS-DA)进行多元统计分析,发现上述目标代谢物能够较好地区分CC组与健康对照组。为防止数据过拟合,该研究在PLS-DA模型各代谢物变量投影重要性(VIP)基础上,结合最优组分数及K-均值聚类结果,筛选得到3种代谢标志物。通过受试者操作特征曲线(ROC)的曲线下面积(AUC)分析,发现筛选得到的3种潜在标志物联合预测CC达到0.998,说明模型性能优良。GO(基因本体论)富集分析显示3种潜在标志物主要分布在内质网和包被囊泡上,参与胆固醇代谢、运输、低密度脂蛋白重塑等生物进程,发挥胆固醇运输活性和低密度脂蛋白颗粒受体结合的分子功能。KEGG(京都基因与基因组百科全书)通路分析显示3种潜在标志物富集于类固醇生物合成、PPAR(过氧化物酶体增殖物激活受体)信号通路及ABC(ATP结合盒)转运等通路上。该研究为寻找CC标志物及进一步阐明胆固醇及氧固醇在CC发病过程中的作用奠定了一定的基础。

关键词: 液相色谱-串联质谱, 结肠癌, 氧固醇, 代谢组学

Abstract:

Colon cancer (CC) is one of the most common malignant tumors worldwide. As there are no effective biomarkers for the early diagnosis and intervention tracking, the incidence of CC is increasing every year. Cholesterol is an important component of cell membrane, and it has been shown to be associated with CC. Oxysterol is an oxidized derivative of cholesterol, which plays an important role in many malignant tumors. In this study, liquid chromatography-tandem mass spectrometry (LC-MS/MS) was used to determine serum cholesterol and ten oxysterol metabolites related to cholesterol in CC patients and healthy controls, and qualitative and quantitative analyses were carried out. Raw data were processed and analyzed using GraphPad Prism 8.3.0 and the MetaboAnalyst 5.0 platform (https://www.metaboanalyst.ca/MetaboAnalyst/ModuleView.xhtml). To perform the independent sample t-test, it was necessary to ensure that all the sample data followed a normal distribution; therefore, the normal distribution test was performed in advance. The Mann-Whitney U test, which is a nonparametric test, was adopted for samples without a normal distribution. For the processed data, we used the statistical analysis function module of the MetaboAnalyst 5.0 platform to perform partial least-square discriminant analysis (PLS-DA) and orthogonal partial least-square discriminant analysis (OPLS-DA). Both PLS-DA and OPLS-DA are supervised discriminant analysis methods. The OPLS-DA model is based on the PLS-DA model and eliminates variables that are unrelated to the experiment. In both models, the samples from the two groups were well separated by the score plot. In the PLS-DA model, the horizontal and vertical coordinates of the score plot represent the interpretation rates of the principal components of the model. The horizontal coordinates show the differences between groups, and the vertical coordinates show the differences within groups. In addition to the score plot in the PLS-DA model, another crucial factor is variable importance in the projection (VIP). When VIP>1, the compound makes an important contribution to the model and is also used as a criterion for screening differential metabolites. Based on 10-fold cross-validation (CV) of the PLS-DA model, the performance of the model was the best when the number of components was three. To avoid overfitting of the data, three metabolic markers were selected by using not only the VIP values of metabolites of the PLS-DA model, but also the optimal compositions and K-mean clusters. The three biomarkers were 4β-hydroxycholesterol (4β-OHC), cholestane-3β,5α,6β-triol (Triol), and cholesterol. A receiver operating characteristic (ROC) curve was constructed. The area under the curve (AUC) was generally between 0.5 and 1.0. In the case of AUC>0.5, the closer the AUC is to 1, the better is the performance of the model. In this study, the area under the ROC curve constructed jointly by the three metabolic markers was 0.998, indicating that their combined ability to predict CC was strong and that the diagnostic performance was excellent. In addition, to understand the role of the three metabolic markers in the pathogenesis of CC, the genes associated with the metabolic markers were identified using GeneCards (https://www.genecards.org/). Finally, 110 genes were identified. Gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to analyze the biological processes, metabolic pathways, and possible roles in the body. GO enrichment showed that the three markers are mainly distributed in the endoplasmic reticulum lumen and coated vesicles, and they are mainly involved in biological processes such as cholesterol metabolism, transportation, and low-density lipoprotein particle remodeling. Their molecular functions are cholesterol transfer activity and low-density lipoprotein particle receptor binding. KEGG pathway analysis showed that biomarkers are enriched in steroid biosynthesis, PPAR (peroxisome proliferator-activated receptor) signaling pathways, and ABC (ATP-binding cassette) transport pathways. The results of this study are helpful to understand the role of cholesterol and oxysterol in the pathogenesis of CC and to elucidate the pathogenesis of CC.

Key words: liquid chromatography-tandem mass spectrometry (LC-MS/MS), colon cancer (CC), oxysterol, metabolomics

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