Quitadamo Andrew, Tian Lu, Hall Benika, Shi Xinghua
BMC Bioinformatics. 2015;16 Suppl 5(Suppl 5):S5. doi: 10.1186/1471-2105-16-S5-S5. Epub 2015 Mar 18.
Ovarian cancer is a deadly female reproductive cancer. Understanding the biological mechanisms underlying ovarian cancer could help lead to quicker and more accurate diagnosis and more effective treatments. Both changes in microRNA(miRNA) expression and miRNA/mRNA dysregulation have been associated with ovarian cancer. With the availability of whole-genome miRNA and mRNA sequencing we now have new potentials to study these associations. In this study, we performed a comprehensive analysis of miRNA and mRNA expression in ovarian cancer using an integrative network approach combined with association analysis.
We developed an integrative approach to construct a network that illustrates the complex interplay among miRNA and gene expression from a systems perspective. Our method is composed of expanding networks from eQTL associations, building network associations in eQTL analysis, and then combine the networks into an integrated network. This integrated network takes account of miRNA expression quantitative trait loci (eQTL) associations, miRNAs and their targets, protein-protein interactions, co-expressions among miRNAs and genes respectively. Applied to the ovarian cancer data set from The Cancer Genome Atlas (TCGA), we created an integrated network with 167 nodes containing 108 miRNA-target interactions and 145 from protein-protein interactions, starting from 44 initial eQTLs. This integrated network encompassed 26 genes and 14 miRNAs associated with cancer. In particular, 11 genes and 12 miRNAs in the integrated network are associated with ovarian cancer.
We demonstrated an integrated network approach that integrates multiple data sources at a systems level. We applied this approach to the TCGA ovarian cancer dataset, and constructed a network that provided a more inclusive view of miRNA and gene expression in ovarian cancer. This network included four separate types of interactions among miRNAs and genes. Simply analyzing each interaction component in isolation, such as the eQTL associations, the miRNA-target interactions or the protein-protein interactions, would create a much more limited network than the integrated one.
卵巢癌是一种致命的女性生殖系统癌症。了解卵巢癌背后的生物学机制有助于实现更快、更准确的诊断以及更有效的治疗。微小RNA（miRNA）表达的变化以及miRNA/信使核糖核酸（mRNA）失调均与卵巢癌有关。随着全基因组miRNA和mRNA测序技术的出现，我们现在有了研究这些关联的新潜力。在本研究中，我们采用整合网络方法并结合关联分析，对卵巢癌中的miRNA和mRNA表达进行了全面分析。
我们开发了一种整合方法来构建一个网络，该网络从系统角度说明了miRNA与基因表达之间的复杂相互作用。我们的方法包括从表达数量性状位点（eQTL）关联扩展网络、在eQTL分析中建立网络关联，然后将这些网络合并为一个整合网络。这个整合网络分别考虑了miRNA表达数量性状位点（eQTL）关联、miRNA及其靶标、蛋白质-蛋白质相互作用、miRNA与基因之间的共表达。应用于来自癌症基因组图谱（TCGA）的卵巢癌数据集，我们从44个初始eQTL开始，创建了一个包含167个节点的整合网络，其中包含108个miRNA-靶标相互作用和145个蛋白质-蛋白质相互作用。这个整合网络涵盖了26个与癌症相关的基因和14个miRNA。特别是，整合网络中的11个基因和12个miRNA与卵巢癌相关。
我们展示了一种在系统水平上整合多个数据源的整合网络方法。我们将这种方法应用于TCGA卵巢癌数据集，并构建了一个网络，该网络提供了对卵巢癌中miRNA和基因表达更全面的视图。这个网络包括miRNA与基因之间四种不同类型的相互作用。单独分析每个相互作用成分，如eQTL关联、miRNA-靶标相互作用或蛋白质-蛋白质相互作用，所创建的网络将比整合网络有限得多。
