Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY 40202, USA.
BMC Med Genomics. 2011 May 19;4:44. doi: 10.1186/1755-8794-4-44.
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.
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.
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.
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.
微小 RNA（miRNA）表达模式的变化已在多种癌症中得到广泛研究，包括人类结肠癌。然而，这些 miRNA 及其潜在的 mRNA 靶标如何促成癌症的发生仍知之甚少。在这项工作中，使用 miRNA 和 mRNA 表达数据的生物信息学计算方法来识别 miRNA 的潜在靶标，并构建 miRNA 和 mRNA 之间的关联网络，以深入了解人类结肠癌的潜在分子机制。
Broad 研究所收集的相同组织（包括 7 个人结肠癌组织和 4 个正常组织）的 miRNA 和 mRNA 微阵列表达谱，用于识别 miRNA 和 mRNA 之间的显著关联。我们应用偏最小二乘（PLS）回归方法和基于 bootstrap 的统计检验方法对差异表达 miRNA 和 mRNAs 的联合表达谱进行分析。通过该分析，我们预测了 miRNA 靶标和 miRNA 和 mRNA 之间的关联网络。采用通路分析识别与这些 miRNA 及其相关预测 mRNA 靶标相关的生物学过程。
通过所提出的方法确定的与下调 miRNA 显著相关的上调 mRNA ，被认为是 miRNA 靶标。平均而言，通过该方法预测的靶标约有 16.5%和 11.0%也被共同的预测算法 TargetScan 和 miRanda 预测为靶标。我们证明，我们的方法比简单的基于相关性的关联检测到更多的靶标。通过 Cytoscape 软件构建了我们分析中整合的 mRNA:miRNA 预测网络。通路分析通过其预测的靶标验证了 miRNAs，这些靶标可能参与癌症相关的生物网络。
我们已经确定了一种在人类结肠癌中预测 miRNA 靶标以及使用相反的相关 mRNA 和 miRNA 联合表达谱反向工程 miRNA:mRNA 网络的替代生物信息学方法。通过模拟研究，我们证明了我们的预测方法优于基于相关性的靶标预测算法。我们预计，所提出的方法预测的独特 miRNA 靶标将有助于深入了解结肠癌的分子机制，并在进一步的实验验证后提出新的治疗靶标。
