Department of Biotechnology, Proteomics and Mass Spectrometry Lab, University of Verona, Strada le Grazie 15, 37135, Verona, Italy.
Department of Sciences and Technological Innovation, University of Piemonte Orientale, Viale T. Michel 11, 15121, Alessandria, Italy; ISALIT S.r.l., Novara, Italy.
Semin Cell Dev Biol. 2018 Jun;78:93-101. doi: 10.1016/j.semcdb.2017.06.030. Epub 2017 Jul 3.
In this review, we give an overview of the actual proteomic approaches used in the study of cancer cells secretome. In particular, we describe the proteomic strategies to decipher cancer cell secretome initially focusing on the different aspects of sample preparation. We examine the issues related to the presence of low abundant proteins, the analysis of secreted proteins in the conditioned media with or without the removal of fetal bovine serum and strategies developed to reduce intracellular protein contamination. As regards the identification and quantification of secreted proteins, we described the different proteomic approaches used, i.e. gel-based, MS-based (label-based and label-free), and the antibody and array-based methods, together with some of the most recent applications in the field of cancer research. Moreover, we describe the bioinformatics tools developed for the in silico validation and characterization of cancer cells secretome. We also discuss the most important available tools for protein annotation and for prediction of classical and non-classical secreted proteins. In summary in this review advances, concerns and challenges in the field of cancer secretome analysis are discussed.
在这篇综述中，我们概述了用于研究癌细胞分泌组的实际蛋白质组学方法。特别是，我们描述了破译癌细胞分泌组的蛋白质组学策略，最初侧重于样品制备的不同方面。我们研究了与低丰度蛋白质存在相关的问题、有或没有胎牛血清去除的条件培养基中分泌蛋白的分析以及开发的减少细胞内蛋白质污染的策略。至于分泌蛋白的鉴定和定量，我们描述了使用的不同蛋白质组学方法，即基于凝胶、基于 MS（基于标记和无标记）以及抗体和阵列方法，以及该领域的一些最新应用癌症研究。此外，我们还描述了用于癌细胞分泌组学的计算机验证和特征描述的生物信息学工具。我们还讨论了用于蛋白质注释和预测经典和非经典分泌蛋白的最有效工具。总之，在这篇综述中，讨论了癌症分泌组分析领域的进展、关注点和挑战。
