运用计算策略预测鼻咽癌的潜在药物。
Using computational strategies to predict potential drugs for nasopharyngeal carcinoma.
作者信息
Lan Ming-Ying, Yang Wu-Lung R, Lin Kuan-Ting, Lin Jin-Ching, Shann Yih-Jyh, Ho Ching-Yin, Huang Chi-Ying F
机构信息
Division of Rhinology, Department of Otolaryngology Head and Neck Surgery, Taipei Veterans General Hospital, Taipei, Taiwan; Institute of Clinical Medicine, National Yang-Ming University, Taipei, Taiwan.
出版信息
Head Neck. 2014 Oct;36(10):1398-407. doi: 10.1002/hed.23464. Epub 2013 Nov 18.
BACKGROUND
Nasopharyngeal carcinoma (NPC) is a unique cancer. Refinement of current therapy by discovering potential drugs may be approached by several computational strategies.
METHODS
We collected NPC genes from published microarray data and the literature. The NPC disease network was constructed via a protein-protein interaction (PPI) network. The Connectivity Map (CMap) was used to predict potential chemicals, and support vector machines (SVMs) were further utilized to classify the effectiveness of tested drugs against NPC using their gene expression from CMap.
RESULTS
A highly interconnected network was obtained. Several chemically sensitive genes were identified and 87 drugs were predicted with the potential for treating NPC by SVM, in which nearly half of them have anticancer effects according to the literature. The 2 top-ranked drugs, thioridazine and vorinostat, were demonstrated to be effective in inhibiting NPC cells.
CONCLUSION
This in silico approach provides a promising strategy for screening potential therapeutic drugs for NPC treatment.
背景
鼻咽癌（NPC）是一种独特的癌症。通过几种计算策略可以发现潜在药物，从而优化当前治疗方法。
方法
我们从已发表的微阵列数据和文献中收集鼻咽癌基因。通过蛋白质-蛋白质相互作用（PPI）网络构建鼻咽癌疾病网络。使用连通性图谱（CMap）预测潜在化学物质，并进一步利用支持向量机（SVM）根据CMap中测试药物的基因表达对其针对鼻咽癌的有效性进行分类。
结果
获得了一个高度互联的网络。鉴定出了几个化学敏感基因，通过支持向量机预测出87种具有治疗鼻咽癌潜力的药物，根据文献，其中近一半具有抗癌作用。排名前两位的药物硫利达嗪和伏立诺他被证明可有效抑制鼻咽癌细胞。
结论
这种计算机模拟方法为筛选用于鼻咽癌治疗的潜在治疗药物提供了一种有前景的策略。
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