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使用亚细胞蛋白质组学发现疑似胰腺癌生物标志物。

Discovery of putative pancreatic cancer biomarkers using subcellular proteomics.

机构信息

Proteomics Laboratory for Clinical and Translational Research, Carolinas HealthCare System, Charlotte, NC, USA.

出版信息

J Proteomics. 2011 Jan 1;74(1):79-88. doi: 10.1016/j.jprot.2010.08.006. Epub 2010 Aug 31.

DOI:10.1016/j.jprot.2010.08.006
PMID:20807598
Abstract

Pancreatic cancer (PC) is a highly aggressive disease that frequently remains undetected until it has progressed to an advanced, systemic stage. Successful treatment of PC is hindered by the lack of early detection. The application of proteomic analysis to PC combined with subcellular fractionation has introduced new possibilities in the field of biomarker discovery. We utilized matched pairs of pancreas tumor and non-tumor pancreas from patients undergoing tumor resection. The tissues were treated to obtain cellular protein fractions corresponding to cytosol, membrane, nucleus and cytoskeleton. The fractions were then separated by molecular weight and digested with trypsin, followed by liquid chromatography and tandem mass spectrometry. The spectra obtained were searched using Sequest engine and combined into a single analysis file to obtain a semi-quantitative number, spectral count, using Scaffold software. We identified 2393 unique proteins in non-tumor and cancer pancreas. Utilizing PLGEM statistical analysis we determined 104 proteins were significantly changed in cancer. From these, we further validated four secreted proteins that are up-regulated in cancer and have potential for development as minimally-invasive diagnostic markers. We conclude that subcellular fractionation followed by gel electrophoresis and tandem mass spectrometry is a powerful strategy for identification of differentially expressed proteins in pancreatic cancer.

摘要

胰腺癌(PC)是一种高度侵袭性的疾病,通常在进展到晚期全身性阶段之前仍未被发现。PC 的成功治疗受到缺乏早期检测的阻碍。将蛋白质组学分析应用于与亚细胞分级分离相结合的 PC 为生物标志物发现领域带来了新的可能性。我们利用来自接受肿瘤切除的患者的配对胰腺肿瘤和非肿瘤胰腺。对组织进行处理以获得对应于细胞质、膜、核和细胞骨架的细胞蛋白级分。然后通过分子量分离级分,并使用胰蛋白酶进行消化,然后进行液相色谱和串联质谱分析。使用 Sequest 引擎搜索获得的光谱,并将其组合到单个分析文件中,以使用 Scaffold 软件获得半定量数字,即光谱计数。我们在非肿瘤和癌症胰腺中鉴定了 2393 种独特的蛋白质。利用 PLGEM 统计分析,我们确定了 104 种在癌症中发生明显变化的蛋白质。在这些蛋白质中,我们进一步验证了四种在癌症中上调的分泌蛋白,它们有可能作为微创诊断标志物进行开发。我们得出结论,亚细胞分级分离后进行凝胶电泳和串联质谱分析是鉴定胰腺癌中差异表达蛋白的有力策略。

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