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本文引用的文献

1
Combined analysis of transcriptome and proteome data as a tool for the identification of candidate biomarkers in renal cell carcinoma.转录组和蛋白质组数据的联合分析作为肾细胞癌中候选生物标志物鉴定的工具
Proteomics. 2009 Mar;9(6):1567-81. doi: 10.1002/pmic.200700288.
2
Grade-dependent proteomics characterization of kidney cancer.肾癌的分级依赖性蛋白质组学特征
Mol Cell Proteomics. 2009 May;8(5):971-85. doi: 10.1074/mcp.M800252-MCP200. Epub 2009 Jan 21.
3
Altered detoxification status and increased resistance to oxidative stress by K-ras transformation.K-ras 转化导致解毒状态改变及对氧化应激的抗性增加。
Cancer Res. 2008 Dec 15;68(24):10086-93. doi: 10.1158/0008-5472.CAN-08-0360.
4
Quantitative proteomic analysis to discover potential diagnostic markers and therapeutic targets in human renal cell carcinoma.定量蛋白质组学分析以发现人类肾细胞癌中的潜在诊断标志物和治疗靶点。
Proteomics. 2008 Aug;8(15):3194-203. doi: 10.1002/pmic.200700619.
5
Understanding and managing renal cell carcinoma: can proteomic studies contribute to clinical practice?
Contrib Nephrol. 2008;160:88-106. doi: 10.1159/000125936.
6
Reverse transcriptase inhibitors induce cell differentiation and enhance the immunogenic phenotype in human renal clear-cell carcinoma.
Int J Cancer. 2008 Jun 15;122(12):2842-50. doi: 10.1002/ijc.23197.
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Candidate biomarkers in renal cell carcinoma.肾细胞癌中的候选生物标志物。
Proteomics. 2007 Dec;7(24):4601-12. doi: 10.1002/pmic.200700415.
8
Key clinical issues in renal cancer: a challenge for proteomics.肾癌的关键临床问题:蛋白质组学面临的挑战
World J Urol. 2007 Dec;25(6):537-56. doi: 10.1007/s00345-007-0199-y. Epub 2007 Aug 25.
9
Integrated therapy of kidney cancer.肾癌的综合治疗
Ann Oncol. 2007 Jun;18 Suppl 6:vi141-8. doi: 10.1093/annonc/mdm244.
10
Vascular endothelial growth factor-targeted therapy in renal cell carcinoma: current status and future directions.肾细胞癌中血管内皮生长因子靶向治疗:现状与未来方向
Clin Cancer Res. 2007 Feb 15;13(4):1098-106. doi: 10.1158/1078-0432.CCR-06-1989.

基于二维凝胶电泳分离组织标本的透明细胞肾细胞癌系统比较蛋白质表达谱分析:一项初步研究。

Systematic comparative protein expression profiling of clear cell renal cell carcinoma: a pilot study based on the separation of tissue specimens by two-dimensional gel electrophoresis.

机构信息

Institute of Medical Immunology, Martin Luther University Halle-Wittenberg, 06112 Halle/Saale, Germany.

出版信息

Mol Cell Proteomics. 2009 Dec;8(12):2827-42. doi: 10.1074/mcp.M900168-MCP200. Epub 2009 Sep 14.

DOI:10.1074/mcp.M900168-MCP200
PMID:19752005
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC2816012/
Abstract

Proteome-based technologies represent powerful tools for the analysis of protein expression profiles, including the identification of potential cancer candidate biomarkers. Thus, here we provide a comprehensive protein expression map for clear cell renal cell carcinoma established by systematic comparative two-dimensional gel electrophoresis-based protein expression profiling of 16 paired tissue systems comprising clear cell renal cell carcinoma lesions and corresponding tumor-adjacent renal epithelium using overlapping narrow pH gradients. This approach led to the mapping of 348 distinct spots corresponding to 248 different protein identities. By implementing restriction criteria concerning their detection frequency and overall regulation mode, 28 up- and 56 down-regulated single target spots were considered as potential candidate biomarkers. Based on their gene ontology information, these differentially expressed proteins were classified into distinct functional groups and according to their cellular distribution. Moreover, three representative members of this group, namely calbindin, gelsolin, and heart fatty acid-binding protein, were selected, and their expression pattern was analyzed by immunohistochemistry using tissue microarrays. Thus, this pilot study provides a significant update of the current renal cell carcinoma map and defines a number of differentially expressed proteins, but both their potential as candidate biomarkers and clinical relevance has to be further explored in tissues and for body fluids like serum and urine.

摘要

基于蛋白质组学的技术代表了分析蛋白质表达谱的强大工具,包括鉴定潜在的癌症候选生物标志物。因此,在这里我们提供了一个全面的蛋白质表达图谱,用于分析透明细胞肾细胞癌,该图谱是通过系统比较二维凝胶电泳的蛋白质表达谱分析建立的,包括 16 对组织系统,其中包含透明细胞肾细胞癌病变和相应的肿瘤旁肾上皮组织,使用重叠的窄 pH 梯度。这种方法映射了 348 个不同的斑点,对应于 248 种不同的蛋白质身份。通过实施关于其检测频率和整体调节模式的限制标准,28 个上调和 56 个下调的单一靶标斑点被认为是潜在的候选生物标志物。基于它们的基因本体信息,这些差异表达的蛋白质被分类为不同的功能组,并根据它们的细胞分布进行分类。此外,选择了该组的三个代表性成员,即钙结合蛋白、凝胶蛋白和心脏脂肪酸结合蛋白,并使用组织微阵列通过免疫组织化学分析其表达模式。因此,这项初步研究提供了当前肾细胞癌图谱的重要更新,并定义了一些差异表达的蛋白质,但它们作为候选生物标志物的潜力及其临床相关性都需要在组织中以及在血清和尿液等体液中进一步探索。