Identification of colorectal cancer related genes with mRMR and shortest path in protein-protein interaction network.
Identification of colorectal cancer related genes with mRMR and shortest path in protein-protein interaction network.
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DOI：
10.1371/journal.pone.0033393
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发表时间：
2012
期刊：
影响因子：
3.7
通讯作者：
Chou KC
中科院分区：
文献类型：
--
作者：
Li BQ;Huang T;Liu L;Cai YD;Chou KC
生物医学和基因组学中最重要，最具挑战性的问题之一是如何识别疾病基因。在这项研究中，我们开发了一种计算方法，以基于（i）基因表达谱和（ii）功能蛋白缔合网络的最短路径分析来鉴定与结直肠癌相关的基因。长期以来，前者被用来选择差异表达的基因作为疾病基因，而后者已被广泛用于研究疾病机理。使用String（相互作用基因检索的搜索工具）现有的蛋白质 - 蛋白质相互作用数据，构建了加权蛋白质关联网络。通过MRMR（最大相关性最小冗余）方法，鉴定了六个基因，可以区分结直肠肿瘤和正常相邻的结肠组织与其基因表达谱。同时，根据最短的路径方法，我们进一步发现了另外35个基因，其中一些基因与结直肠癌有关，有些很可能与之相关。有趣的是，我们从基因表达谱和功能蛋白关联网络中鉴定的基因具有比单独的基因表达谱相比，具有更多的癌症基因。此外，这些基因与报告的结直肠癌基因的功能相似性也比单独从基因表达谱发现的基因具有更大的功能相似性。所有这些表明，本文提出的我们的方法非常有前途。该方法可能成为一种有用的工具，或者至少对现有方法起着互补作用，以识别结直肠癌基因。它并没有逃脱我们的注意到，该方法也可以应用于识别其他疾病的基因。
One of the most important and challenging problems in biomedicine and genomics is how to identify the disease genes. In this study, we developed a computational method to identify colorectal cancer-related genes based on (i) the gene expression profiles, and (ii) the shortest path analysis of functional protein association networks. The former has been used to select differentially expressed genes as disease genes for quite a long time, while the latter has been widely used to study the mechanism of diseases. With the existing protein-protein interaction data from STRING (Search Tool for the Retrieval of Interacting Genes), a weighted functional protein association network was constructed. By means of the mRMR (Maximum Relevance Minimum Redundancy) approach, six genes were identified that can distinguish the colorectal tumors and normal adjacent colonic tissues from their gene expression profiles. Meanwhile, according to the shortest path approach, we further found an additional 35 genes, of which some have been reported to be relevant to colorectal cancer and some are very likely to be relevant to it. Interestingly, the genes we identified from both the gene expression profiles and the functional protein association network have more cancer genes than the genes identified from the gene expression profiles alone. Besides, these genes also had greater functional similarity with the reported colorectal cancer genes than the genes identified from the gene expression profiles alone. All these indicate that our method as presented in this paper is quite promising. The method may become a useful tool, or at least plays a complementary role to the existing method, for identifying colorectal cancer genes. It has not escaped our notice that the method can be applied to identify the genes of other diseases as well.
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