Suppr超能文献

膀胱癌相关蛋白,人类膀胱癌潜在的预后生物标志物。

Bladder cancer-associated protein, a potential prognostic biomarker in human bladder cancer.

机构信息

Department of Proteomics in Cancer, Institute of Cancer Biology, Danish Cancer Society, DK-2100 Copenhagen Ø, Denmark.

出版信息

Mol Cell Proteomics. 2010 Jan;9(1):161-77. doi: 10.1074/mcp.M900294-MCP200. Epub 2009 Sep 25.

Abstract

It is becoming increasingly clear that no single marker will have the sensitivity and specificity necessary to be used on its own for diagnosis/prognosis of tumors. Interpatient and intratumor heterogeneity provides overwhelming odds against the existence of such an ideal marker. With this in mind, our laboratory has been applying a long term systematic approach to identify multiple biomarkers that can be used for clinical purposes. As a result of these studies, we have identified and reported several candidate biomarker proteins that are deregulated in bladder cancer. Following the conceptual biomarker development phases proposed by the Early Detection Research Network, we have taken some of the most promising candidate proteins into postdiscovery validation studies, and here we report on the characterization of one such biomarker, the bladder cancer-associated protein (BLCAP), formerly termed Bc10. To characterize BLCAP protein expression and cellular localization patterns in benign bladder urothelium and urothelial carcinomas (UCs), we used two independent sets of samples from different patient cohorts: a reference set consisting of 120 bladder specimens (formalin-fixed as well as frozen biopsies) and a validation set consisting of 2,108 retrospectively collected UCs with long term clinical follow-up. We could categorize the UCs examined into four groups based on levels of expression and subcellular localization of BLCAP protein and showed that loss of BLCAP expression is associated with tumor progression. The results indicated that increased expression of this protein confers an adverse patient outcome, suggesting that categorization of staining patterns for this protein may have prognostic value. Finally, we applied a combinatorial two-marker discriminator using BLCAP and adipocyte-type fatty acid-binding protein, another UC biomarker previously reported by us, and found that the combination of the two markers correlated more closely with grade and/or stage of disease than the individual markers. The implications of these results in biomarker discovery are discussed.

摘要

越来越明显的是,没有任何单一的标志物能够具有足够的敏感性和特异性,单凭其自身就可以用于肿瘤的诊断/预后。患者间和肿瘤内异质性使得这样一个理想标志物的存在几乎不可能。考虑到这一点,我们的实验室一直在应用一种长期的系统方法来识别可用于临床目的的多种生物标志物。作为这些研究的结果,我们已经鉴定并报告了几个在膀胱癌中失调的候选生物标志物蛋白。根据早期检测研究网络提出的概念性生物标志物开发阶段,我们已经将一些最有前途的候选蛋白纳入了发现后验证研究,在这里我们报告了其中一种生物标志物的特征,即膀胱癌相关蛋白(BLCAP),以前称为 Bc10。为了描述在良性膀胱尿路上皮和尿路上皮癌(UCs)中 BLCAP 蛋白表达和细胞定位模式,我们使用了来自不同患者队列的两组独立样本:一组参考样本由 120 个膀胱标本组成(福尔马林固定和冷冻活检),另一组验证样本由 2108 个回顾性收集的具有长期临床随访的 UCs 组成。我们可以根据 BLCAP 蛋白的表达和亚细胞定位将检查的 UCs 分为四组,并表明 BLCAP 表达的丧失与肿瘤进展相关。结果表明,该蛋白的表达增加会导致患者预后不良,这表明该蛋白染色模式的分类可能具有预后价值。最后,我们使用了一种组合的双标志物鉴别器,该鉴别器使用了 BLCAP 和我们之前报道的另一种 UC 标志物脂肪细胞型脂肪酸结合蛋白,发现这两种标志物的组合与疾病的分级和/或分期的相关性比单个标志物更密切。讨论了这些结果在生物标志物发现中的意义。

相似文献

9
Proteomics analysis of bladder cancer exosomes.膀胱癌外泌体的蛋白质组学分析。
Mol Cell Proteomics. 2010 Jun;9(6):1324-38. doi: 10.1074/mcp.M000063-MCP201. Epub 2010 Mar 11.

引用本文的文献

本文引用的文献

1
Biobanking for better healthcare.生物样本库助力更优质医疗。
Mol Oncol. 2008 Oct;2(3):213-22. doi: 10.1016/j.molonc.2008.07.004. Epub 2008 Jul 30.
2
Innovations, challenges and future prospects of oncoproteomics.肿瘤蛋白质组学的创新、挑战与未来前景
Mol Oncol. 2008 Aug;2(2):153-60. doi: 10.1016/j.molonc.2008.05.003. Epub 2008 May 28.
6
Does RNA editing play a role in the development of urinary bladder cancer?RNA 编辑在膀胱癌的发展中起作用吗?
Urol Oncol. 2011 Jan-Feb;29(1):21-6. doi: 10.1016/j.urolonc.2008.11.006. Epub 2009 Jan 31.
7
The global burden of urinary bladder cancer.膀胱癌的全球负担。
Scand J Urol Nephrol Suppl. 2008 Sep(218):12-20. doi: 10.1080/03008880802285032.
8
Current status of prognostic immunohistochemical markers for urothelial bladder cancer.
Tumour Biol. 2008;29(5):311-22. doi: 10.1159/000170878. Epub 2008 Nov 5.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验