基于血清蛋白质组学方法的低级别和高级别膀胱癌评估。

Low- and high-grade bladder cancer appraisal via serum-based proteomics approach.

机构信息

Department of Urology, King George's Medical University, Lucknow, India.

Department of Metabolomics, Centre of Biomedical Research, SGPGIMS Campus, Lucknow, India.

出版信息

Clin Chim Acta. 2014 Sep 25;436:97-103. doi: 10.1016/j.cca.2014.05.012. Epub 2014 May 27.

DOI:10.1016/j.cca.2014.05.012

PMID:24875752

Abstract

OBJECTIVE

To address the shortcomings of urine cytology and cystoscopy for screening and grading of urinary bladder cancer (BC) we applied a serum-based proteomics approach as a surrogate tactic for rapid BC probing.

METHODS

This study was performed on 90 sera samples comprising of low-grade (LG, n=33) and high-grade (HG, n=32) BC, and healthy controls (HC, n=25). Two-dimensional gel electrophoresis (2DE) tactic was executed to describe serum proteome. MALDI-TOF-MS (MS) was used to identify the characteristics of aberrantly expressed proteins in 2DE and validated using Western blot (WB) and ELISA approach. Receiver operating characteristics (ROC) curve analysis was also performed to determine the clinical usefulness of these proteins to discriminate among LG, HG and HC cohorts.

RESULTS

This comprehensive approach of 2DE, MS, WB and ELISA reveals five differentially expressed proteins. Among them two biomarkers (S100A8 and S100A9) were able to accurately (ROC, 0.946) distinguish 81% of BC (LG+HG) cases compared to HC with highest sensitivity and specificity. With a comparable tactic, two biomarkers (S100A8 and S100A4) were able to precisely (ROC, 0.941) discriminate 92% of LG cases from HG with utmost sensitivity and specificity.

CONCLUSIONS

Serum proteomics probing appears to be an encouraging and least-invasive tactic for screening and grading of BC.

摘要

目的

为了解决尿细胞学和膀胱镜检查在膀胱癌（BC）筛查和分级方面的不足，我们应用了一种基于血清的蛋白质组学方法作为快速 BC 探测的替代策略。

方法

本研究共纳入 90 例血清样本，包括低级别（LG，n=33）和高级别（HG，n=32）BC 患者以及健康对照者（HC，n=25）。采用二维凝胶电泳（2DE）技术描述血清蛋白质组。MALDI-TOF-MS（MS）用于鉴定 2DE 中异常表达蛋白的特征，并通过 Western blot（WB）和 ELISA 方法进行验证。还进行了受试者工作特征（ROC）曲线分析，以确定这些蛋白在区分 LG、HG 和 HC 队列中的临床应用价值。

结果

本研究采用 2DE、MS、WB 和 ELISA 等综合方法，共发现了 5 种差异表达蛋白。其中，两种生物标志物（S100A8 和 S100A9）能够准确地区分 81%的 BC（LG+HG）病例与 HC，具有最高的敏感性和特异性（ROC，0.946）。采用类似的方法，两种生物标志物（S100A8 和 S100A4）能够准确地区分 92%的 LG 病例与 HG，具有最高的敏感性和特异性（ROC，0.941）。