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Basic Research
MicroRNA regulatory network-based prediction of multidrug resistance genes in ovarian cancer
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Abstract:
Objective: To predict genes related to multidrug resistance (MDR) in ovarian cancer based on the microRNA (miRNA) regulatory network. Methods: To identify potential genes associated with multidrug resistance in ovarian cancer based on published miRNAs and miRNA-target genes were identified using a comprehensive bioinformatics approaches including text mining and network researching. Results: Eleven miRNAs related to ovarian cancer chemoresistance were identified, including miR-130a, miR-214, let-7i, miR-125b, miR-376c, miR-199a, miR-93, miR-141, miR-130b, miR-193b* and miR-200c. A total of 47 077 putative targets were predicted using the TargetScan algorithm and 1 675 other targets were predicted using the PicTar algorithm. Neuropilins (NRP1) was the most important Hub-gene found in the cancer miRNA regulatory network. Conclusion: It is an effective method to construct a miRNA regulatory network to predict genes related to MDR in ovarian cancer by utilizing the existing information on miRNAs. Using this approach, we predict that NRP1 may play an important role in ovarian cancer chemoresistance and would be a potential therapeutic target for ovarian cancer.
Keywords:
ovarian cancer ; multidrug resistance ; gene ; microRNA regulatory network ; predict ; neuropilins 1 (NRP1)
Project Supported:
Project supported by the National High-Tech R&D Program of China（863 Program）（No. 2012AA02A507），the Natural Science Foundation of the Guangxi Zhuang Autonomous Region（No. 2014-11），and the Science Research and Technology Development Plan of the Guangxi Zhuang Autonomous Region（No.14124004-1-24）
