Charles Sona, Natarajan Jeyakumar
Data Mining and Text Mining Laboratory, Department of Bioinformatics, Bharathiar University, Coimbatore, Tamilnadu, India.
Genes Genomics. 2020 Aug;42(8):855-867. doi: 10.1007/s13258-020-00952-6. Epub 2020 May 30.
Cardiovascular diseases contribute to the leading cause of deaths (31%) in the world population.
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.
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.
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.
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.
心血管疾病是全球人口死亡的主要原因（占31%）。
本研究的目的是将非编码RNA-基因相互作用整合到一个核心调控网络中，该网络的失调可能在疾病进展中起重要作用。
我们应用一种结构化方法来重建参与心血管疾病的长链非编码RNA（lncRNA）、微小RNA（miRNA）和基因的相互作用网络。对于网络构建，我们使用了“疾病组到相互作用组”和“相互作用组到疾病组”的方法，并在心脏疾病中开发了两个调控网络。在“疾病组到相互作用组”的方法中，我们从一个以疾病为中心的网络开始，将数据扩展为一个相互作用网络。然而，在“相互作用组到疾病组”的方法中，我们使用一组指导基因、miRNA和lncRNA来确定常见疾病。以疾病为中心的网络与相互作用网络相结合，将揭示一个巨大的疾病组网络中与心脏疾病相关的相互连接的成分，并在进展过程中通过小的子网表现出来。使用上述网络，我们在两种方法上创建了仅由枢纽基因、miRNA和lncRNA组成的子网。任何一个枢纽的失调都可能导致疾病状态。
在两个子网中排名靠前的共同枢纽基因是血管内皮生长因子A（VEGFA）、转移相关肺腺癌转录本1（MALAT1）、HOX转录反义RNA（HOTAIR）、H19和人miR-15a。我们基于网络的研究揭示了miRNA、lncRNA和基因的调控子网在多种情况下的纠缠。
在疾病发生和发展的核心三节点网络中识别枢纽，证明了基于网络的方法在靶向关键分子进行药物开发方面具有广阔前景。
