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脑膜瘤的定量蛋白质组学分析以鉴定替代蛋白标志物

Quantitative proteomic analysis of meningiomas for the identification of surrogate protein markers.

作者信息

Sharma Samridhi, Ray Sandipan, Moiyadi Aliasgar, Sridhar Epari, Srivastava Sanjeeva

机构信息

Department of Biosciences and Bioengineering, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India.

Department of Neurosurgery, Advanced Center for Treatment Research and Education in Cancer, Tata Memorial Center, Kharghar, Navi Mumbai 410210, India.

出版信息

Sci Rep. 2014 Nov 21;4:7140. doi: 10.1038/srep07140.

Abstract

Meningiomas are the most common non-glial tumors of the brain and spine. Pathophysiology and definite histological grading of meningiomas are frequently found to be deceptive due to their unusual morphological features and locations. Here for the first time we report a comprehensive serum proteomic analysis of different grades of meningiomas by using multiple quantitative proteomic and immunoassay-based approaches to obtain mechanistic insights about disease pathogenesis and identify grade specific protein signatures. In silico functional analysis revealed modulation of different vital physiological pathways including complement and coagulation cascades, metabolism of lipids and lipoproteins, immune signaling, cell growth and apoptosis and integrin signaling in meningiomas. ROC curve analysis demonstrated apolipoprotein E and A-I and hemopexin as efficient predictors for meningiomas. Identified proteins like vimentin, alpha-2-macroglobulin, apolipoprotein B and A-I and antithrombin-III, which exhibited a sequential increase in different malignancy grades of meningiomas, could serve as potential predictive markers.

摘要

脑膜瘤是脑和脊柱最常见的非神经胶质肿瘤。由于其不寻常的形态特征和位置,脑膜瘤的病理生理学和明确的组织学分级常常具有欺骗性。在此,我们首次报告通过使用多种基于定量蛋白质组学和免疫测定的方法,对不同分级的脑膜瘤进行全面的血清蛋白质组分析,以获得有关疾病发病机制的机制性见解,并识别分级特异性蛋白质特征。计算机功能分析揭示了脑膜瘤中不同重要生理途径的调节,包括补体和凝血级联反应、脂质和脂蛋白代谢、免疫信号传导、细胞生长和凋亡以及整合素信号传导。ROC曲线分析表明,载脂蛋白E、A-I和血红素结合蛋白是脑膜瘤的有效预测指标。在脑膜瘤不同恶性分级中呈序贯增加的波形蛋白、α-2-巨球蛋白、载脂蛋白B和A-I以及抗凝血酶III等已鉴定蛋白质,可作为潜在的预测标志物。

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