University of Grenoble Alpes, INSERM, CEA, IRIG-BGE, U1038, Grenoble, 38000, France.
MaIAGE, INRA, Université Paris-Saclay, Jouy-en-Josas, 78350, France.
Proteomics. 2019 Nov;19(21-22):e1800489. doi: 10.1002/pmic.201800489. Epub 2019 Oct 10.
Secretome proteomics for the discovery of cancer biomarkers holds great potential to improve early cancer diagnosis. A knowledge-based approach relying on mechanistic criteria related to the type of cancer should help to identify candidates from available "omics" information. With the aim of accelerating the discovery process for novel biomarkers, a set of tools is developed and made available via a Galaxy-based instance to assist end-users biologists. These implemented tools proceed by a step-by-step strategy to mine transcriptomics and proteomics databases for information relating to tissue specificity, allow the selection of proteins that are part of the secretome, and combine this information with proteomics datasets to rank the most promising candidate biomarkers for early cancer diagnosis. Using pancreatic cancer as a case study, this strategy produces a list of 24 candidate biomarkers suitable for experimental assessment by MS-based proteomics. Among these proteins, three (SYCN, REG1B, and PRSS2) were previously reported as circulating candidate biomarkers of pancreatic cancer. Here, further refinement of this list allows to prioritize 14 candidate biomarkers along with their associated proteotypic peptides for further investigation, using targeted MS-based proteomics. The bioinformatics tools and the workflow implementing this strategy for the selection of candidate biomarkers are freely accessible at http://www.proteore.org.
分泌组蛋白质组学在发现癌症生物标志物方面具有很大的潜力，可以提高癌症的早期诊断。基于机制标准的知识型方法与癌症类型相关，应有助于从现有“组学”信息中识别候选物。为了加速新生物标志物的发现过程，开发了一组工具，并通过基于 Galaxy 的实例提供，以帮助终端用户生物学家。这些实现的工具通过逐步的策略来挖掘转录组学和蛋白质组学数据库中与组织特异性相关的信息，允许选择属于分泌组的蛋白质，并将这些信息与蛋白质组数据集相结合，对最有希望用于早期癌症诊断的候选生物标志物进行排名。使用胰腺癌作为案例研究，该策略产生了一份适合 MS 基蛋白质组学实验评估的 24 个候选生物标志物列表。在这些蛋白质中，有三种（SYCN、REG1B 和 PRSS2）先前被报道为胰腺癌的循环候选生物标志物。在这里，通过靶向 MS 基蛋白质组学，进一步细化了该列表，优先选择了 14 个候选生物标志物及其相关的特征肽，以进行进一步研究。用于候选生物标志物选择的生物信息学工具和实施该策略的工作流程可在 http://www.proteore.org 免费获取。
