title: Network-based identification of microRNAs as potential pharmacogenomic biomarkers for anticancer drugs.
abstract: 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/.
authors.affiliation: Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China.
authors.forename: Jie
authors.initials: J
authors.lastname: Li
authors.affiliation: Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China.
authors.forename: Kecheng
authors.initials: K
authors.lastname: Lei
authors.affiliation: Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China.
authors.forename: Zengrui
authors.initials: Z
authors.lastname: Wu
authors.affiliation: Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China.
authors.forename: Weihua
authors.initials: W
authors.lastname: Li
authors.affiliation: Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China.
authors.forename: Guixia
authors.initials: G
authors.lastname: Liu
authors.affiliation: Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China.
authors.forename: Jianwen
authors.initials: J
authors.lastname: Liu
authors.affiliation: State Key Laboratory of Biotherapy/Collaborative Innovation Center for Biotherapy, West China Hospital, West China Medical School, Sichuan University, Chengdu, China.|Current address: Center for Cancer Systems Biology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, USA.|Current address: Center for Complex Networks Research, Northeastern University, Boston, USA.
authors.forename: Feixiong
authors.initials: F
authors.lastname: Cheng
authors.affiliation: Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China.
authors.forename: Yun
authors.initials: Y
authors.lastname: Tang
mesh_terms: D019540:Area Under Curve; D014408:Biomarkers, Tumor / Q000235:genetics*; D004354:Drug Screening Assays, Antitumor / Q000379:methods*; D020869:Gene Expression Profiling; D053263:Gene Regulatory Networks; D006801:Humans; D035683:MicroRNAs / Q000032:analysis*; D000071185:Pharmacogenomic Testing / Q000379:methods*; D057285:Precision Medicine / Q000379:methods; D012372:ROC Curve
chemical_list: D014408:Biomarkers, Tumor; D035683:MicroRNAs
keywords: breast cancer; metformin; miRNA; network-based inference; pharmacogenomics