An integrative -omics approach to identify functional sub-networks in human colorectal cancer.
An integrative -omics approach to identify functional sub-networks in human colorectal cancer.
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DOI：
10.1371/journal.pcbi.1000639
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发表时间：
2010-01-15
影响因子：
4.3
通讯作者：
Chance MR
中科院分区：
文献类型：
--
作者：
Nibbe RK;Koyutürk M;Chance MR
新出现的证据表明，与人类癌症有关的基因产物经常聚集在蛋白质-蛋白质相互作用（PPI）网络的“热点”中。此外，PPI网络内的小型子网络，表现出协同差异表达相对于致瘤表型最近被证明是更准确的疾病进展的分类相比，通过传统的方法确定的单一目标。然而，这些研究中的许多完全依赖于mRNA表达数据，这是一种有用但有限的细胞活性测量方法。蛋白质组分析实验提供翻译后水平的信息，但它们通常只筛选有限的蛋白质组部分。在这里，我们证明了这些互补的数据源与“蛋白质组学优先”的方法相结合，可以增强癌症中候选子网络的发现，这些子网络非常适合疾病的机制验证。我们提出，在蛋白质枢纽附近的多个基因的mRNA表达的微小变化可以协同地与该蛋白质及其网络邻居的活性的显著变化相关。此外，我们假设在表型和对照之间具有显著倍数变化的蛋白质组靶标可用于“播种”与这些靶标功能相关的小PPI子网络的搜索。为了验证这一假设，我们从两个独立的基于2-D凝胶的筛选中选择在人类结直肠癌（CRC）中具有显著表达变化的蛋白质组学靶点。然后，我们使用基于随机游走的网络串扰模型，并开发新的参考模型，以确定在统计上显着的子网络与这些蛋白质组目标的功能关联。随后，使用信息理论的措施，我们评估协同变化的基础上确定的子网络的活性在CRC的mRNA表达的全基因组筛选。预测疾病类别的交叉分类实验仅使用少数子网络就显示出优异的性能，从而保证了所提出的方法在发现相关和可重复的子网络方面的优势。对癌症的深入研究使人们了解了许多可能对肿瘤的发生和发展至关重要的单个基因。然而，由于癌症是一种进行性疾病，其由可能协同作用的多个突变的积累引起，因此单个标记物只能对肿瘤发生的细胞机制提供有限的见解。因此，最近的研究集中在“子网络标记”的鉴定上，即在癌症进展期间表现出分子表达协调变化的功能相关基因。然而，基因的表达最常在mRNA水平上被询问，其仅在有限程度上捕获基因的功能活性。另一方面，蛋白质表达的筛选提供了关于功能基因产物丰度的信息，但与mRNA表达的筛选相比，其规模往往有限。在这篇文章中，我们开发了一种蛋白质组学驱动的计算方法，搜索人类结直肠癌中的子网络标记，基于蛋白质组学筛选鉴定的差异表达蛋白的种子。我们的研究结果表明，这些蛋白质的表达中的显着变化可能与其产物与这些蛋白质功能相关的基因的表达中的协调变化有关。这种分析导致新的见解的协同过程，肿瘤发生的基础。
Emerging evidence indicates that gene products implicated in human cancers often cluster together in “hot spots” in protein-protein interaction (PPI) networks. Additionally, small sub-networks within PPI networks that demonstrate synergistic differential expression with respect to tumorigenic phenotypes were recently shown to be more accurate classifiers of disease progression when compared to single targets identified by traditional approaches. However, many of these studies rely exclusively on mRNA expression data, a useful but limited measure of cellular activity. Proteomic profiling experiments provide information at the post-translational level, yet they generally screen only a limited fraction of the proteome. Here, we demonstrate that integration of these complementary data sources with a “proteomics-first” approach can enhance the discovery of candidate sub-networks in cancer that are well-suited for mechanistic validation in disease. We propose that small changes in the mRNA expression of multiple genes in the neighborhood of a protein-hub can be synergistically associated with significant changes in the activity of that protein and its network neighbors. Further, we hypothesize that proteomic targets with significant fold change between phenotype and control may be used to “seed” a search for small PPI sub-networks that are functionally associated with these targets. To test this hypothesis, we select proteomic targets having significant expression changes in human colorectal cancer (CRC) from two independent 2-D gel-based screens. Then, we use random walk based models of network crosstalk and develop novel reference models to identify sub-networks that are statistically significant in terms of their functional association with these proteomic targets. Subsequently, using an information-theoretic measure, we evaluate synergistic changes in the activity of identified sub-networks based on genome-wide screens of mRNA expression in CRC. Cross-classification experiments to predict disease class show excellent performance using only a few sub-networks, underwriting the strength of the proposed approach in discovering relevant and reproducible sub-networks. Intensive research on cancer has led to an understanding of many individual genes that may be important for the initiation and progression of tumors. However, since cancer is a progressive disease that results from accumulation of multiple mutations likely acting in concert, individual markers can only provide limited insights into cellular mechanisms that underlie tumorigenesis. For this reason, recent studies focus on identification of “sub-network markers”, that is, functionally associated genes that exhibit coordinate changes in molecular expression during cancer progression. However, expression of genes is most frequently interrogated at the mRNA level, which captures functional activity of genes only to a limited extent. Screening of protein expression, on the other hand, provides information on the abundance of functional gene products, but its scale is often limited compared to screening of mRNA expression. In this article, we develop a proteomics-driven computational method that searches for sub-network markers in human colorectal cancer, based on a seed of differentially expressed proteins identified by proteomic screening. Our results show that significant changes in the expression of these proteins is likely to be associated with coordinate changes in the expression of the genes whose products are functionally associated with these proteins. This analysis leads to novel insights in the synergistic processes that underlie tumorigenesis.
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影响因子：
5.4
作者：
de Silva, Eric;Thorne, Thomas;Ingram, Piers;Agrafioti, Ino;Swire, Jonathan;Wiuf, Carsten;Stumpf, Michael P. H.
通讯作者：
Stumpf, Michael P. H.
影响因子：
7
作者：
Nibbe, Rod K.;Markowitz, Sanford;Chance, Mark R.
通讯作者：
Chance, Mark R.
影响因子：
5.3
作者：
Liu, XQ;Lin, CY;Erikson, RL
通讯作者：
Erikson, RL
影响因子：
9.9
作者：
通讯作者：
--
影响因子：
3.4
作者：
Friedman, DB;Hill, S;Caprioli, RM
通讯作者：
Caprioli, RM