Sumathipala Marissa, Maiorino Enrico, Weiss Scott T, Sharma Amitabh
Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States.
Harvard College, Cambridge, MA, United States.
Front Physiol. 2019 Jul 16;10:888. doi: 10.3389/fphys.2019.00888. eCollection 2019.
Recently, long-non-coding RNAs (lncRNAs) have attracted attention because of their emerging role in many important biological mechanisms. The accumulating evidence indicates that the dysregulation of lncRNAs is associated with complex diseases. However, only a few lncRNA-disease associations have been experimentally validated and therefore, predicting potential lncRNAs that are associated with diseases become an important task. Current computational approaches often use known lncRNA-disease associations to predict potential lncRNA-disease links. In this work, we exploited the topology of multi-level networks to propose the ncRNA rankng by Netwrk Diffusio (LION) approach to identify lncRNA-disease associations. The multi-level complex network consisted of lncRNA-protein, protein-protein interactions, and protein-disease associations. We applied the network diffusion algorithm of LION to predict the lncRNA-disease associations within the multi-level network. LION achieved an AUC value of 96.8% for cardiovascular diseases, 91.9% for cancer, and 90.2% for neurological diseases by using experimentally verified lncRNAs associated with diseases. Furthermore, compared to a similar approach (TPGLDA), LION performed better for cardiovascular diseases and cancer. Given the versatile role played by lncRNAs in different biological mechanisms that are perturbed in diseases, LION's accurate prediction of lncRNA-disease associations helps in ranking lncRNAs that could function as potential biomarkers and potential drug targets.
最近，长链非编码RNA（lncRNAs）因其在许多重要生物学机制中发挥的新作用而备受关注。越来越多的证据表明，lncRNAs的失调与复杂疾病有关。然而，只有少数lncRNA与疾病的关联得到了实验验证，因此，预测与疾病相关的潜在lncRNAs成为一项重要任务。当前的计算方法通常利用已知的lncRNA与疾病的关联来预测潜在的lncRNA与疾病的联系。在这项工作中，我们利用多层次网络的拓扑结构，提出了基于网络扩散的ncRNA排名（LION）方法来识别lncRNA与疾病的关联。多层次复杂网络由lncRNA与蛋白质、蛋白质与蛋白质的相互作用以及蛋白质与疾病的关联组成。我们应用LION的网络扩散算法来预测多层次网络内的lncRNA与疾病的关联。通过使用与疾病相关的经实验验证的lncRNAs，LION在心血管疾病方面的AUC值达到了96.8%，在癌症方面为91.9%，在神经系统疾病方面为90.2%。此外，与类似方法（TPGLDA）相比，LION在心血管疾病和癌症方面表现更好。鉴于lncRNAs在疾病中受到干扰的不同生物学机制中发挥的多种作用，LION对lncRNA与疾病关联的准确预测有助于对可能作为潜在生物标志物和潜在药物靶点的lncRNAs进行排名。
