潜在前列腺癌生物标志物的计算机模拟验证及平行反应监测预验证
In silico verification and parallel reaction monitoring prevalidation of potential prostate cancer biomarkers.
作者信息
Adeola Henry A, Calder Bridget, Soares Nelson C, Kaestner Lisa, Blackburn Jonathan M, Zerbini Luiz F
机构信息
International Centre for Genetic Engineering & Biotechnology, Cape Town, South Africa.
Institute of Infectious Diseases & Molecular Medicine, Faculty of Health Sciences, University of Cape Town, South Africa.
出版信息
Future Oncol. 2016 Jan;12(1):43-57. doi: 10.2217/fon.15.296. Epub 2015 Nov 30.
PURPOSE
Targeted proteomics of potential biomarkers is often challenging. Hence, we developed an intermediate workflow to streamline potential urinary biomarkers of prostate cancer (PCa).
MATERIALS & METHODS: Using previously discovered potential PCa biomarkers; we selected proteotypic peptides for targeted validation. Preliminary in silico immunohistochemical and single reaction monitoring (SRM) verification was performed. Successful PTPs were then prevalidated using parallel reaction monitoring (PRM) and reconfirmed in 15 publicly available databases.
RESULTS
Stringency-based targetable potential biomarkers were shortlisted following in silico screening. PRM reveals top 12 potential biomarkers including the top ranking seven in silico verification-based biomarkers. Database reconfirmation showed differential expression between PCa and benign/normal prostatic urine samples.
CONCLUSION
The pragmatic penultimate screening step, described herein, would immensely improve targeted proteomics validation of potential disease biomarkers.
目的
对潜在生物标志物进行靶向蛋白质组学研究往往具有挑战性。因此，我们开发了一种中间工作流程，以简化前列腺癌（PCa）潜在尿液生物标志物的研究。
材料与方法
利用先前发现的潜在PCa生物标志物；我们选择了蛋白质型肽段进行靶向验证。进行了初步的计算机免疫组织化学和单反应监测（SRM）验证。然后使用平行反应监测（PRM）对成功的蛋白质型肽段进行预验证，并在15个公开可用的数据库中进行重新确认。
结果
基于严格性的可靶向潜在生物标志物在计算机筛选后入围。PRM揭示了前12种潜在生物标志物，包括基于计算机验证排名前七的生物标志物。数据库重新确认显示PCa与良性/正常前列腺尿液样本之间存在差异表达。
结论
本文所述的实用的倒数第二步筛选步骤将极大地改善对潜在疾病生物标志物的靶向蛋白质组学验证。
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