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通过凝集素糖芯片和凝集素印迹法进行血浆糖蛋白分析以鉴定结直肠癌生物标志物

Plasma glycoprotein profiling for colorectal cancer biomarker identification by lectin glycoarray and lectin blot.

作者信息

Qiu Yinghua, Patwa Tasneem H, Xu Li, Shedden Kerby, Misek David E, Tuck Missy, Jin Gracie, Ruffin Mack T, Turgeon Danielle K, Synal Sapna, Bresalier Robert, Marcon Norman, Brenner Dean E, Lubman David M

机构信息

Department of Chemistry, University of Michigan, Ann Arbor, Michigan 48109, USA.

出版信息

J Proteome Res. 2008 Apr;7(4):1693-703. doi: 10.1021/pr700706s. Epub 2008 Feb 27.

Abstract

Colorectal cancer (CRC) remains a major worldwide cause of cancer-related morbidity and mortality largely due to the insidious onset of the disease. The current clinical procedures utilized for disease diagnosis are invasive, unpleasant, and inconvenient; hence, the need for simple blood tests that could be used for the early detection of CRC. In this work, we have developed methods for glycoproteomics analysis to identify plasma markers with utility to assist in the detection of colorectal cancer (CRC). Following immunodepletion of the most abundant plasma proteins, the plasma N -linked glycoproteins were enriched using lectin affinity chromatography and subsequently further separated by nonporous silica reversed-phase (NPS-RP)-HPLC. Individual RP-HPLC fractions were printed on nitrocellulose coated slides which were then probed with lectins to determine glycan patterns in plasma samples from 9 normal, 5 adenoma, and 6 colorectal cancer patients. Statistical tools, including principal component analysis, hierarchical clustering, and Z-statistics analysis, were employed to identify distinctive glycosylation patterns. Patients diagnosed with colorectal cancer or adenomas were shown to have dramatically higher levels of sialylation and fucosylation as compared to normal controls. Plasma glycoproteins with aberrant glycosylation were identified by nano-LC-MS/MS, while a lectin blotting methodology was used to validate proteins with significantly altered glycosylation as a function of cancer progression. The potential markers identified in this study for diagnosis to distinguish colorectal cancer from adenoma and normal include elevated sialylation and fucosylation in complement C3, histidine-rich glycoprotein, and kininogen-1. These potential markers of colorectal cancer were subsequently validated by lectin blotting in an independent set of plasma samples obtained from 10 CRC patients, 10 patients with adenomas, and 10 normal subjects. These results demonstrate the utility of this strategy for the identification of N -linked glycan patterns as potential markers of CRC in human plasma, and may have the utility to distinguish different disease states.

摘要

结直肠癌(CRC)仍然是全球癌症相关发病和死亡的主要原因,这主要归因于该疾病隐匿性的发病过程。目前用于疾病诊断的临床程序具有侵入性、令人不适且不方便;因此,需要能够用于早期检测结直肠癌的简单血液检测方法。在这项研究中,我们开发了糖蛋白质组学分析方法,以鉴定有助于检测结直肠癌(CRC)的血浆标志物。在对最丰富的血浆蛋白进行免疫去除后,使用凝集素亲和色谱法富集血浆N-连接糖蛋白,随后通过无孔硅胶反相(NPS-RP)-HPLC进一步分离。将各个RP-HPLC馏分点样在硝酸纤维素包被的载玻片上,然后用凝集素进行检测,以确定来自9名正常受试者、5名腺瘤患者和6名结直肠癌患者的血浆样本中的聚糖模式。采用包括主成分分析、层次聚类和Z统计分析在内的统计工具来识别独特的糖基化模式。结果显示,与正常对照组相比,被诊断为结直肠癌或腺瘤的患者的唾液酸化和岩藻糖基化水平显著更高。通过纳米液相色谱-串联质谱(nano-LC-MS/MS)鉴定糖基化异常的血浆糖蛋白,同时使用凝集素印迹法验证随着癌症进展糖基化显著改变的蛋白质。本研究中鉴定出的用于区分结直肠癌与腺瘤和正常状态的潜在标志物包括补体C3、富含组氨酸的糖蛋白和激肽原-1中唾液酸化和岩藻糖基化水平升高。随后,通过凝集素印迹法在从10名结直肠癌患者、10名腺瘤患者和10名正常受试者获得的另一组独立血浆样本中对这些结直肠癌潜在标志物进行了验证。这些结果证明了该策略在识别N-连接聚糖模式作为人血浆中结直肠癌潜在标志物方面的实用性,并且可能有助于区分不同的疾病状态。

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本文引用的文献

1
Cancer biomarker discovery in plasma using a tissue-targeted proteomic approach.
Cancer Epidemiol Biomarkers Prev. 2007 Oct;16(10):1915-7. doi: 10.1158/1055-9965.EPI-07-0420.
3
Isolation of N-linked glycopeptides from plasma.
Anal Chem. 2007 Aug 1;79(15):5826-37. doi: 10.1021/ac0623181. Epub 2007 Jun 26.
4
Serum biomarkers to differentiate benign and malignant mammographic lesions.
J Am Coll Surg. 2007 May;204(5):1065-71; discussion 1071-3. doi: 10.1016/j.jamcollsurg.2007.01.036.
8
Human body fluid proteome analysis.
Proteomics. 2006 Dec;6(23):6326-53. doi: 10.1002/pmic.200600284.
9
Mass spectrometric detection of tissue proteins in plasma.
Mol Cell Proteomics. 2007 Jan;6(1):64-71. doi: 10.1074/mcp.M600160-MCP200. Epub 2006 Oct 9.

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