Network-based identification of microRNAs as potential pharmacogenomic biomarkers for anticancer drugs
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Jie Li1, Kecheng Lei1, Zengrui Wu1, Weihua Li1, Guixia Liu1, Jianwen Liu1, Feixiong Cheng2,3,4, Yun Tang1
1Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China
2State Key Laboratory of Biotherapy/Collaborative Innovation Center for Biotherapy, West China Hospital, West China Medical School, Sichuan University, Chengdu, China
3Current address: Center for Cancer Systems Biology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, USA
4Current address: Center for Complex Networks Research, Northeastern University, Boston, USA
Yun Tang, email: email@example.com
Feixiong Cheng, email: firstname.lastname@example.org
Jianwen Liu, email: email@example.com
Keywords: pharmacogenomics, miRNA, network-based inference, metformin, breast cancer
Received: January 18, 2016 Accepted: May 28, 2016 Published: June 14, 2016
As the recent development of high-throughput technologies in cancer pharmacogenomics, there is an urgent need to develop new computational approaches for comprehensive identification of new pharmacogenomic biomarkers, such as microRNAs (miRNAs). In this study, a network-based framework, namely the SMiR-NBI model, was developed to prioritize miRNAs as potential biomarkers characterizing treatment responses of anticancer drugs on the basis of a heterogeneous network connecting drugs, miRNAs and genes. A high area under the receiver operating characteristic curve of 0.820 ± 0.013 was yielded during 10-fold cross validation. In addition, high performance was further validated in identifying new anticancer mechanism-of-action for natural products and non-steroidal anti-inflammatory drugs. Finally, the newly predicted miRNAs for tamoxifen and metformin were experimentally validated in MCF-7 and MDA-MB-231 breast cancer cell lines via qRT-PCR assays. High success rates of 60% and 65% were yielded for tamoxifen and metformin, respectively. Specifically, 11 oncomiRNAs (e.g. miR-20a-5p, miR-27a-3p, miR-29a-3p, and miR-146a-5p) from the top 20 predicted miRNAs were experimentally verified as new pharmacogenomic biomarkers for metformin in MCF-7 or MDA-MB-231 cell lines. In summary, the SMiR-NBI model would provide a powerful tool to identify potential pharmacogenomic biomarkers characterized by miRNAs in the emerging field of precision cancer medicine, which is available at http://lmmd.ecust.edu.cn/database/smir-nbi/.
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