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Computational and Mathematical Methods in Medicine
Volume 2014 (2014), Article ID 746979, 8 pages
Research Article

Establishing Reliable miRNA-Cancer Association Network Based on Text-Mining Method

1Hubei Bioinformatics and Molecular Imaging Key Laboratory, Huazhong University of Science and Technology, Wuhan 430074, China
2Biomedical Engineering Department, College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
3Department of Statistics, The University of Akron, Akron, OH 44325, USA
4Department of Family & Community Medicine, Northeast Ohio Medical University, Rootstown, OH 44272, USA
5Guizhou Provincial Key Laboratory of Computational Nano-Material Science, Guizhou Normal College, Guiyang 550018, China

Received 24 January 2014; Revised 5 March 2014; Accepted 6 March 2014; Published 10 April 2014

Academic Editor: Xiao-Qin Xia

Copyright © 2014 Lun Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Associating microRNAs (miRNAs) with cancers is an important step of understanding the mechanisms of cancer pathogenesis and finding novel biomarkers for cancer therapies. In this study, we constructed a miRNA-cancer association network (miCancerna) based on more than 1,000 miRNA-cancer associations detected from millions of abstracts with the text-mining method, including 226 miRNA families and 20 common cancers. We further prioritized cancer-related miRNAs at the network level with the random-walk algorithm, achieving a relatively higher performance than previous miRNA disease networks. Finally, we examined the top 5 candidate miRNAs for each kind of cancer and found that 71% of them are confirmed experimentally. miCancerna would be an alternative resource for the cancer-related miRNA identification.