Background

Cardiovascular diseases contribute to the leading cause of deaths (31%) in the world population.

Objective

The objective of this study is to compile non-coding RNA-gene interaction into a core regulatory network whose dysregulation might play an important role in disease progression.

Method

We applied a structured approach to reconstruct the interaction network of lncRNAs, miRNAs and genes involved in cardiovascular diseases. For network construction, we used ‘diseasome to interactome’ and ‘interactome to diseasome’ approaches and developed two regulatory networks in heart disorders. In diseasome to interactome approach, starting from a disease-centric network we, expanded the data into an interaction network. However in interactome to diseasome, we used a set of guide genes, miRNAs and lncRNAs to arrive at the common diseases. The disease-centric network in combination with the interaction network will shed light on the interconnected components in a huge diseasome network implicated in heart disorders and manifested through small sub-networks while progressing. Using the above networks we created a sub-networks consisting only of hub genes, miRNAs and lncRNAs on both approaches. The dysregulation of any one of the hubs can lead to a disease condition.

Results

The top ranking hubs common in both the sub-networks were found to be VEGFA, MALAT1, HOTAIR, H19 and hsa-miR-15a. Our network based study reveals an entanglement of regulatory sub-network of miRNAs, lncRNAs and genes in multiple conditions.

Conclusion

The identification of hubs in the core triple node network of elements in disease development and progression demonstrates a promising role for network based approaches in targeting critical molecules for drug development.

中文翻译：

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mRNA，miRNA和lncRNA的双向网络构建和分析揭示了心血管疾病的关键调节因子和调节模块。

背景

心血管疾病是导致世界人口死亡的主要原因（31％）。

目的

这项研究的目的是将非编码RNA基因相互作用编入核心调控网络，其失调可能在疾病进展中起重要作用。

方法

我们应用了一种结构化方法来重建与心血管疾病有关的lncRNA，miRNA和基因的相互作用网络。对于网络建设，我们使用了“ diseasome到交互组”和“ interactome到令人讨厌的”方法，并开发了心脏病的两个调节网络。在不受欢迎的交互组方法中，我们从以疾病为中心的网络开始，将数据扩展为交互网络。然而，在使人不适的相互作用组中，我们使用了一组指导基因，miRNA和lncRNA来达到常见的疾病。以疾病为中心的网络与交互网络的结合将揭示出巨大的，令人不适的网络中相互关联的组件，这些网络牵涉到心脏病，并在发展过程中通过小的子网络体现出来。使用以上网络，我们在两种方法上创建了仅由集线器基因，miRNA和lncRNA组成的子网。任何一个集线器的失调都可能导致疾病。

结果

在这两个子网中常见的排名最高的中心是VEGFA，MALAT1，HOTAIR，H19和hsa-miR-15a。我们基于网络的研究揭示了多种条件下miRNA，lncRNA和基因的调控子网络的纠缠。

结论

在疾病发展和进展中，核心三重节点网络中枢纽的识别证明了基于网络的方法在靶向关键分子以进行药物开发方面的有希望的作用。