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蛋白质组学模式:其在疾病诊断中的潜力。

Proteomic patterns: their potential for disease diagnosis.

作者信息

Xiao Zhen, Prieto DaRue, Conrads Thomas P, Veenstra Timothy D, Issaq Haleem J

机构信息

Laboratory of Proteomics and Analytical Technologies, SAIC-Frederick Inc., National Cancer Institute at Frederick, P.O. Box B, Frederick, MD 21702, USA.

出版信息

Mol Cell Endocrinol. 2005 Jan 31;230(1-2):95-106. doi: 10.1016/j.mce.2004.10.010.

Abstract

Alterations in proteins abundance, structure, or function, act as useful indicators of pathological abnormalities prior to development of clinical symptoms and as such are often useful diagnostic and prognostic biomarkers. The underlying mechanism of diseases such as cancer are, however, quite complicated in that often multiple dysregulated proteins are involved. It is for this reason that recent hypotheses suggest that detection of panels of biomarkers may provide higher sensitivities and specificities for disease diagnosis than is afforded with single markers. Recently, a novel approach based on the analysis of protein patterns has emerged that may provide a more effective means to diagnose diseases, such as ovarian and prostate cancer. The method is based on the use of surface-enhanced laser desorption/ionization (SELDI) time-of-flight mass spectrometry (TOF-MS) to detect differentially captured proteins from clinical samples, such as serum and plasma. This analysis results in the detection of "proteomic" patterns that have been shown in recent investigations to distinguish diseased and unaffected subjects to varying degrees. This review will discuss the basics of SELDI protein chip technology and highlight its recent applications in disease biomarker discovery with emphasis on cancer diagnosis.

摘要

蛋白质丰度、结构或功能的改变,在临床症状出现之前可作为病理异常的有用指标,因此常作为有用的诊断和预后生物标志物。然而,诸如癌症等疾病的潜在机制相当复杂,因为通常涉及多种失调的蛋白质。正是由于这个原因,最近的假说表明,与单一标志物相比,检测生物标志物组合可能为疾病诊断提供更高的灵敏度和特异性。最近,一种基于蛋白质模式分析的新方法出现了,它可能为诊断疾病(如卵巢癌和前列腺癌)提供更有效的手段。该方法基于使用表面增强激光解吸/电离(SELDI)飞行时间质谱(TOF-MS)来检测从临床样本(如血清和血浆)中差异捕获的蛋白质。这种分析导致检测到“蛋白质组”模式,最近的研究表明这些模式在不同程度上能够区分患病和未患病的个体。本综述将讨论SELDI蛋白质芯片技术的基础知识,并重点介绍其在疾病生物标志物发现中的最新应用,尤其是在癌症诊断方面。

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