Wu Ming, Chan Christina
Department of Computer Science and Engineering, Department of Chemical Engineering and Materials Science and Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA.
Department of Computer Science and Engineering, Department of Chemical Engineering and Materials Science and Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA Department of Computer Science and Engineering, Department of Chemical Engineering and Materials Science and Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA Department of Computer Science and Engineering, Department of Chemical Engineering and Materials Science and Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, USA.
Bioinformatics. 2014 Apr 15;30(8):1163-1171. doi: 10.1093/bioinformatics/btt751. Epub 2014 Jan 7.
MicroRNA (miRNA) expression has been found to be deregulated in human cancer, contributing, in part, to the interest of the research community in using miRNAs as alternative therapeutic targets. Although miRNAs could be potential targets, identifying which miRNAs to target for a particular type of cancer has been difficult due to the limited knowledge on their regulatory roles in cancer. We address this challenge by integrating miRNA-target prediction, metabolic modeling and context-specific gene expression data to predict therapeutic miRNAs that could reduce the growth of cancer.
We developed a novel approach to simulate a condition-specific metabolic system for human hepatocellular carcinoma (HCC) wherein overexpression of each miRNA was simulated to predict their ability to reduce cancer cell growth. Our approach achieved >80% accuracy in predicting the miRNAs that could suppress metastasis and progression of liver cancer based on various experimental evidences in the literature. This condition-specific metabolic system provides a framework to explore the mechanisms by which miRNAs modulate metabolic functions to affect cancer growth. To the best of our knowledge, this is the first computational approach implemented to predict therapeutic miRNAs for human cancer based on their functional role in cancer metabolism. Analyzing the metabolic functions altered by the miRNA-identified metabolic genes essential for cell growth and proliferation that are targeted by the miRNAs.
See supplementary protocols and http://www.egr.msu.edu/changroup/Protocols%20Index.html CONTACT: krischan@egr.msu.edu Supplementary information: Supplementary data are available at Bioinformatics online.
已发现微小RNA（miRNA）表达在人类癌症中失调，这在一定程度上引发了研究界将miRNA用作替代治疗靶点的兴趣。尽管miRNA可能是潜在的靶点，但由于对其在癌症中的调控作用了解有限，确定针对特定类型癌症的miRNA靶点一直很困难。我们通过整合miRNA靶点预测、代谢建模和特定背景下的基因表达数据来应对这一挑战，以预测可降低癌症生长的治疗性miRNA。
我们开发了一种新颖的方法来模拟人类肝细胞癌（HCC）的特定条件下的代谢系统，其中模拟了每个miRNA的过表达以预测其降低癌细胞生长的能力。基于文献中的各种实验证据，我们的方法在预测可抑制肝癌转移和进展的miRNA方面准确率超过80%。这种特定条件下的代谢系统提供了一个框架，用于探索miRNA调节代谢功能以影响癌症生长的机制。据我们所知，这是第一种基于miRNA在癌症代谢中的功能作用来预测人类癌症治疗性miRNA的计算方法。分析了由miRNA识别的对细胞生长和增殖至关重要的代谢基因所改变的代谢功能，这些基因是miRNA的靶点。
见补充协议和http://www.egr.msu.edu/changroup/Protocols%20Index.html 联系方式：krischan@egr.msu.edu 补充信息：补充数据可在《生物信息学》在线获取。
