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采用 LC/MS-MS 和无标记定量法进行膀胱癌检测的尿糖蛋白生物标志物的发现。

Urinary glycoprotein biomarker discovery for bladder cancer detection using LC/MS-MS and label-free quantification.

机构信息

Department of Surgery, University of Michigan Medical Center, University of Michigan, Ann Arbor, Michigan, USA.

出版信息

Clin Cancer Res. 2011 May 15;17(10):3349-59. doi: 10.1158/1078-0432.CCR-10-3121. Epub 2011 Apr 1.

Abstract

BACKGROUND

Cancers of the urinary bladder are the fifth most commonly diagnosed malignancy in the United States. Early clinical diagnosis of bladder cancer remains a major challenge, and the development of noninvasive methods for detection and surveillance is desirable for both patients and health care providers.

APPROACH

To identify urinary proteins with potential clinical utility, we enriched and profiled the glycoprotein component of urine samples by using a dual-lectin affinity chromatography and liquid chromatography/tandem mass spectrometry platform.

RESULTS

From a primary sample set obtained from 54 cancer patients and 46 controls, a total of 265 distinct glycoproteins were identified with high confidence, and changes in glycoprotein abundance between groups were quantified by a label-free spectral counting method. Validation of candidate biomarker alpha-1-antitrypsin (A1AT) for disease association was done on an independent set of 70 samples (35 cancer cases) by using an ELISA. Increased levels of urinary A1AT glycoprotein were indicative of the presence of bladder cancer (P < 0.0001) and augmented voided urine cytology results. A1AT detection classified bladder cancer patients with a sensitivity of 74% and specificity of 80%.

SUMMARY

The described strategy can enable higher resolution profiling of the proteome in biological fluids by reducing complexity. Application of glycoprotein enrichment provided novel candidates for further investigation as biomarkers for the noninvasive detection of bladder cancer.

摘要

背景

膀胱癌是美国第五大常见恶性肿瘤。早期临床诊断膀胱癌仍然是一个主要挑战,开发用于检测和监测的非侵入性方法,既有利于患者,也有利于医疗保健提供者。

方法

为了鉴定具有潜在临床应用价值的尿蛋白,我们使用双凝集素亲和层析和液相色谱/串联质谱平台对尿液样本的糖蛋白成分进行了富集和分析。

结果

从 54 名癌症患者和 46 名对照者的原始样本集中,共鉴定出 265 种具有高可信度的独特糖蛋白,并通过无标记谱计数方法定量了组间糖蛋白丰度的变化。使用 ELISA 对 70 个样本(35 个癌症病例)的独立样本集进行了候选生物标志物α-1-抗胰蛋白酶(A1AT)与疾病相关性的验证。尿 A1AT 糖蛋白水平升高表明存在膀胱癌(P<0.0001),并增强了尿液细胞学检查结果。A1AT 检测对膀胱癌患者的灵敏度为 74%,特异性为 80%。

总结

所描述的策略通过降低复杂性,能够实现对生物流体中蛋白质组的更高分辨率分析。糖蛋白富集的应用为进一步作为膀胱癌无创检测的生物标志物提供了新的候选物。

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