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首页> 外文期刊>BMC Medical Genomics >Modeling microRNA-mRNA Interactions Using PLS Regression in Human Colon Cancer
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Modeling microRNA-mRNA Interactions Using PLS Regression in Human Colon Cancer

机译：在人类结肠癌中使用PLS回归建模microRNA-mRNA相互作用

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Background Changes in microRNA (miRNA) expression patterns have been extensively characterized in several cancers, including human colon cancer. However, how these miRNAs and their putative mRNA targets contribute to the etiology of cancer is poorly understood. In this work, a bioinformatics computational approach with miRNA and mRNA expression data was used to identify the putative targets of miRNAs and to construct association networks between miRNAs and mRNAs to gain some insights into the underlined molecular mechanisms of human colon cancer. Method The miRNA and mRNA microarray expression profiles from the same tissues including 7 human colon tumor tissues and 4 normal tissues, collected by the Broad Institute, were used to identify significant associations between miRNA and mRNA. We applied the partial least square (PLS) regression method and bootstrap based statistical tests to the joint expression profiles of differentially expressed miRNAs and mRNAs. From this analysis, we predicted putative miRNA targets and association networks between miRNAs and mRNAs. Pathway analysis was employed to identify biological processes related to these miRNAs and their associated predicted mRNA targets. Results Most significantly associated up-regulated mRNAs with a down-regulated miRNA identified by the proposed methodology were considered to be the miRNA targets. On average, approximately 16.5% and 11.0% of targets predicted by this approach were also predicted as targets by the common prediction algorithms TargetScan and miRanda, respectively. We demonstrated that our method detects more targets than a simple correlation based association. Integrative mRNA:miRNA predictive networks from our analysis were constructed with the aid of Cytoscape software. Pathway analysis validated the miRNAs through their predicted targets that may be involved in cancer-associated biological networks. Conclusion We have identified an alternative bioinformatics approach for predicting miRNA targets in human colon cancer and for reverse engineering the miRNA:mRNA network using inversely related mRNA and miRNA joint expression profiles. We demonstrated the superiority of our predictive method compared to the correlation based target prediction algorithm through a simulation study. We anticipate that the unique miRNA targets predicted by the proposed method will advance the understanding of the molecular mechanism of colon cancer and will suggest novel therapeutic targets after further experimental validations.
机译：背景技术已经在包括人类结肠癌在内的几种癌症中广泛表征了microRNA（miRNA）表达模式的变化。然而，人们对这些miRNA及其推定的mRNA靶标如何促成癌症的病因知之甚少。在这项工作中，使用了具有miRNA和mRNA表达数据的生物信息学计算方法来确定miRNA的假定靶标，并构建miRNA和mRNA之间的关联网络，以深入了解人类结肠癌的分子机制。方法利用Broad Institute收集的来自7个人结肠肿瘤组织和4个正常组织的相同组织的miRNA和mRNA微阵列表达谱，鉴定miRNA与mRNA之间的显着相关性。我们将偏最小二乘（PLS）回归方法和基于bootstrap的统计检验应用于差异表达的miRNA和mRNA的联合表达谱。通过该分析，我们预测了假定的miRNA靶标以及miRNA和mRNA之间的关联网络。途径分析被用于鉴定与这些miRNA及其相关的预测的mRNA靶标有关的生物学过程。结果通过拟议的方法鉴定出的与下调的miRNA最显着相关的上调mRNA被认为是miRNA靶标。普通预测算法TargetScan和miRanda还分别平均预测了通过此方法预测的目标的16.5％和11.0％。我们证明，与简单的基于关联的关联相比，我们的方法可检测更多目标。借助Cytoscape软件，构建了我们分析中的整合mRNA：miRNA预测网络。途径分析通过其可能参与癌症相关生物网络的预测靶标验证了miRNA。结论我们已经确定了一种替代的生物信息学方法，用于预测人类结肠癌中的miRNA靶标，并使用与mRNA和miRNA反向相关的反向表达谱逆向工程miRNA：mRNA网络。通过仿真研究，我们证明了与基于相关的目标预测算法相比，我们的预测方法的优越性。我们预期通过所提出的方法预测的独特的miRNA靶标将促进对结肠癌分子机制的理解，并在进一步的实验验证后将提出新的治疗靶标。

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