Yu Liang, Su Ruidan, Wang Bingbo, Zhang Long, Zou Yapeng, Zhang Jing, Gao Lin
IEEE/ACM Trans Comput Biol Bioinform. 2017 Jul-Aug;14(4):966-977. doi: 10.1109/TCBB.2016.2550453. Epub 2016 Apr 5.
Computational approaches for predicting drug-disease associations by integrating gene expression and biological network provide great insights to the complex relationships among drugs, targets, disease genes, and diseases at a system level. Hepatocellular carcinoma (HCC) is one of the most common malignant tumors with a high rate of morbidity and mortality. We provide an integrative framework to predict novel d rugs for HCC based on multi-source random walk (PD-MRW). Firstly, based on gene expression and protein interaction network, we construct a gene-gene weighted i nteraction network (GWIN). Then, based on multi-source random walk in GWIN, we build a drug-drug similarity network. Finally, based on the known drugs for HCC, we score all drugs in the drug-drug similarity network. The robustness of our predictions, their overlap with those reported in Comparative Toxicogenomics Database (CTD) and literatures, and their enriched KEGG pathway demonstrate our approach can effectively identify new drug indications. Specifically, regorafenib (Rank = 9 in top-20 list) is proven to be effective in Phase I and II clinical trials of HCC, and the Phase III trial is ongoing. And, it has 11 overlapping pathways with HCC with lower p-values. Focusing on a particular disease, we believe our approach is more accurate and possesses better scalability.
通过整合基因表达和生物网络来预测药物-疾病关联的计算方法，在系统层面上为药物、靶点、疾病基因和疾病之间的复杂关系提供了深刻见解。肝细胞癌（HCC）是最常见的恶性肿瘤之一，发病率和死亡率都很高。我们提供了一个基于多源随机游走的肝细胞癌新型药物预测整合框架（PD-MRW）。首先，基于基因表达和蛋白质相互作用网络，构建基因-基因加权相互作用网络（GWIN）。然后，基于GWIN中的多源随机游走，构建药物-药物相似性网络。最后，基于肝细胞癌的已知药物，对药物-药物相似性网络中的所有药物进行评分。我们预测的稳健性、与比较毒理基因组学数据库（CTD）和文献中报道的预测结果的重叠性以及富集的KEGG通路，都表明我们的方法能够有效地识别新的药物适应症。具体来说，瑞戈非尼（在前20名列表中排名第9）在肝细胞癌的I期和II期临床试验中已被证明有效，III期试验正在进行。并且，它与肝细胞癌有11条重叠通路，p值较低。针对特定疾病，我们相信我们的方法更准确且具有更好的可扩展性。
