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本文引用的文献

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MetaboHunter: an automatic approach for identification of metabolites from 1H-NMR spectra of complex mixtures.MetaboHunter:一种从复杂混合物的 1H-NMR 光谱中自动识别代谢物的方法。
BMC Bioinformatics. 2011 Oct 14;12:400. doi: 10.1186/1471-2105-12-400.
2
MAP kinase-interacting kinase 1 regulates SMAD2-dependent TGF-β signaling pathway in human glioblastoma.丝裂原活化蛋白激酶相互作用激酶 1 调控人胶质母细胞瘤中 SMAD2 依赖的 TGF-β信号通路。
Cancer Res. 2011 Mar 15;71(6):2392-402. doi: 10.1158/0008-5472.CAN-10-3112. Epub 2011 Mar 14.
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Regulation of cancer cell metabolism.癌细胞代谢的调控。
Nat Rev Cancer. 2011 Feb;11(2):85-95. doi: 10.1038/nrc2981.
4
17-allyamino-17-demethoxygeldanamycin treatment results in a magnetic resonance spectroscopy-detectable elevation in choline-containing metabolites associated with increased expression of choline transporter SLC44A1 and phospholipase A2.17-烯丙氨基-17-去甲氧基格尔德霉素治疗导致磁共振波谱检测到胆碱代谢物升高,与胆碱转运蛋白 SLC44A1 和磷脂酶 A2 的表达增加有关。
Breast Cancer Res. 2010;12(5):R84. doi: 10.1186/bcr2729. Epub 2010 Oct 14.
5
Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in PDGFRA, IDH1, EGFR, and NF1.整合基因组分析确定了具有 PDGFRA、IDH1、EGFR 和 NF1 异常的胶质母细胞瘤的临床相关亚型。
Cancer Cell. 2010 Jan 19;17(1):98-110. doi: 10.1016/j.ccr.2009.12.020.
6
Rethinking the Warburg effect with Myc micromanaging glutamine metabolism.重新思考 Myc 精细调控谷氨酰胺代谢的瓦博格效应。
Cancer Res. 2010 Feb 1;70(3):859-62. doi: 10.1158/0008-5472.CAN-09-3556. Epub 2010 Jan 19.
7
Glioblastoma subclasses can be defined by activity among signal transduction pathways and associated genomic alterations.胶质母细胞瘤亚类可以通过信号转导通路的活性和相关的基因组改变来定义。
PLoS One. 2009 Nov 13;4(11):e7752. doi: 10.1371/journal.pone.0007752.
8
NMR metabolic analysis of samples using fuzzy K-means clustering.使用模糊 K-均值聚类对样本进行 NMR 代谢分析。
Magn Reson Chem. 2009 Dec;47 Suppl 1:S96-104. doi: 10.1002/mrc.2502.
9
HMDB: a knowledgebase for the human metabolome.HMDB:人类代谢组知识库。
Nucleic Acids Res. 2009 Jan;37(Database issue):D603-10. doi: 10.1093/nar/gkn810. Epub 2008 Oct 25.
10
Epidermal growth factor receptor and claudin-2 participate in A549 permeability and remodeling: implications for non-small cell lung cancer tumor colonization.表皮生长因子受体和紧密连接蛋白2参与A549细胞的通透性和重塑:对非小细胞肺癌肿瘤定植的影响。
Mol Carcinog. 2009 Jun;48(6):488-97. doi: 10.1002/mc.20485.

1H NMR 代谢组学分析胶质母细胞瘤亚型:代谢组学与基因表达特征的相关性。

1H NMR metabolomics analysis of glioblastoma subtypes: correlation between metabolomics and gene expression characteristics.

机构信息

National Research Council of Canada, Moncton, New Brunswick E1A 7R1, Canada.

出版信息

J Biol Chem. 2012 Jun 8;287(24):20164-75. doi: 10.1074/jbc.M111.337196. Epub 2012 Apr 23.

DOI:10.1074/jbc.M111.337196
PMID:22528487
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3370199/
Abstract

Glioblastoma multiforme (GBM) is the most common form of malignant glioma, characterized by unpredictable clinical behaviors that suggest distinct molecular subtypes. With the tumor metabolic phenotype being one of the hallmarks of cancer, we have set upon to investigate whether GBMs show differences in their metabolic profiles. (1)H NMR analysis was performed on metabolite extracts from a selection of nine glioblastoma cell lines. Analysis was performed directly on spectral data and on relative concentrations of metabolites obtained from spectra using a multivariate regression method developed in this work. Both qualitative and quantitative sample clustering have shown that cell lines can be divided into four groups for which the most significantly different metabolites have been determined. Analysis shows that some of the major cancer metabolic markers (such as choline, lactate, and glutamine) have significantly dissimilar concentrations in different GBM groups. The obtained lists of metabolic markers for subgroups were correlated with gene expression data for the same cell lines. Metabolic analysis generally agrees with gene expression measurements, and in several cases, we have shown in detail how the metabolic results can be correlated with the analysis of gene expression. Combined gene expression and metabolomics analysis have shown differential expression of transporters of metabolic markers in these cells as well as some of the major metabolic pathways leading to accumulation of metabolites. Obtained lists of marker metabolites can be leveraged for subtype determination in glioblastomas.

摘要

多形性胶质母细胞瘤(GBM)是最常见的恶性神经胶质瘤,其临床表现不可预测,提示存在不同的分子亚型。肿瘤代谢表型是癌症的标志之一,我们着手研究 GBM 是否在其代谢谱中存在差异。对来自 9 种胶质母细胞瘤细胞系的选择的代谢物提取物进行了(1)H NMR 分析。直接对光谱数据和使用本工作中开发的多元回归方法从光谱获得的代谢物相对浓度进行了分析。定性和定量的样本聚类表明,细胞系可以分为 4 组,确定了最显著不同的代谢物。分析表明,一些主要的癌症代谢标志物(如胆碱、乳酸盐和谷氨酰胺)在不同的 GBM 组中的浓度有很大差异。为亚组获得的代谢标志物列表与相同细胞系的基因表达数据相关。代谢分析通常与基因表达测量结果一致,在某些情况下,我们详细展示了如何将代谢结果与基因表达分析相关联。结合基因表达和代谢组学分析表明,这些细胞中代谢标志物的转运蛋白以及导致代谢物积累的一些主要代谢途径存在差异表达。获得的标记代谢物列表可用于胶质母细胞瘤的亚型确定。