Discovery and Scoring of Protein Interaction Subnetworks Discriminative of Late Stage Human Colon Cancer
Discovery and Scoring of Protein Interaction Subnetworks Discriminative of Late Stage Human Colon Cancer
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DOI：
10.1074/mcp.m800428-mcp200
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发表时间：
2009-04-01
影响因子：
7
通讯作者：
Chance, Mark R.
中科院分区：
文献类型：
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作者：
Nibbe, Rod K.;Markowitz, Sanford;Chance, Mark R.
我们使用系统生物学方法来识别和评分蛋白质相互作用子网络，其活动模式可区分晚期人类结直肠癌（CRC）与结肠组织中的对照。我们进行了两项基于凝胶的蛋白质组学实验，以确定从足够规模的人类患者群体中获得的正常和晚期肿瘤组织之间显着变化的蛋白质。这些实验总共鉴定出 67 种蛋白质，用于寻找蛋白质-蛋白质相互作用子网络。开发了一种基于互信息的评分方案，使用基因表达数据作为子网活动的代理进行计算，以对子网中的目标进行评分。基于此评分，对子网络进行修剪，以确定对晚期癌症与对照有显着区别的特定蛋白质组合。仅使用蛋白质组学数据或仅通过基因表达数据聚类无法发现这些组合。然后，我们分析了所得的修剪子网络与人类 CRC 的生物学相关性。这些较小子网络中的许多蛋白质与 CRC 的进展（CSNK2A2、PLK1 和 IGFBP3）或转移潜力（PDGFRB）相关。其他最近被确定为 CRC 的潜在标志物 (IFITM1)，而其他标志物在这种疾病中的作用很大程度上未知（CCT3、CCT5、CCT7 和 GNA12）。这些特征所代表的功能相互作用提供了新的实验假设，值得对该疾病的生物学意义进行后续验证。总体而言，该方法概述了一种定量方法，用于整合蛋白质组学数据、基因表达数据和大量积累的遗留实验数据，以发现特定于疾病的重要蛋白质子网络。分子与细胞蛋白质组学 8：827-845，2009。
We used a systems biology approach to identify and score protein interaction subnetworks whose activity patterns are discriminative of late stage human colorectal cancer (CRC) versus control in colonic tissue. We conducted two gel-based proteomics experiments to identify significantly changing proteins between normal and late stage tumor tissues obtained from an adequately sized cohort of human patients. A total of 67 proteins identified by these experiments was used to seed a search for protein-protein interaction subnetworks. A scoring scheme based on mutual information, calculated using gene expression data as a proxy for subnetwork activity, was developed to score the targets in the subnetworks. Based on this scoring, the subnetwork was pruned to identify the specific protein combinations that were significantly discriminative of late stage cancer versus control. These combinations could not be discovered using only proteomics data or by merely clustering the gene expression data. We then analyzed the resultant pruned subnetwork for biological relevance to human CRC. A number of the proteins in these smaller subnetworks have been associated with the progression (CSNK2A2, PLK1, and IGFBP3) or metastatic potential (PDGFRB) of CRC. Others have been recently identified as potential markers of CRC (IFITM1), and the role of others is largely unknown in this disease (CCT3, CCT5, CCT7, and GNA12). The functional interactions represented by these signatures provide new experimental hypotheses that merit follow-on validation for biological significance in this disease. Overall the method outlines a quantitative approach for integrating proteomics data, gene expression data, and the wealth of accumulated legacy experimental data to discover significant protein subnetworks specific to disease. Molecular & Cellular Proteomics 8:827-845, 2009.