Subpathway-GMir: identifying miRNA-mediated metabolic subpathways by integrating condition-specific genes,microRNAs, and pathway topologies |
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| Authors: | Li Feng Yanjun Xu Yunpeng Zhang Zeguo Sun Junwei Han Chunlong Zhang Haixiu Yang Desi Shang Fei Su Xinrui Shi Shang Li Chunquan Li Xia Li |
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| Affiliation: | 1. College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China;2. Department of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing, 163319, China |
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| Abstract: | MicroRNAs (miRNAs) regulate disease-relevant metabolic pathways. However, most current pathway identification methods fail to consider miRNAs in addition to genes when analyzing pathways. We developed a powerful method called Subpathway-GMir to construct miRNA-regulated metabolic pathways and to identify miRNA-mediated subpathways by considering condition-specific genes, miRNAs, and pathway topologies. We used Subpathway-GMir to analyze two liver hepatocellular carcinomas (LIHC), one stomach adenocarcinoma (STAD), and one type 2 diabetes (T2D) data sets. Results indicate that Subpathway-GMir is more effective in identifying phenotype-associated metabolic pathways than other methods and our results are reproducible and robust. Subpathway-GMir provides a flexible platform for identifying abnormal metabolic subpathways mediated by miRNAs, and may help to clarify the roles that miRNAs play in a variety of diseases. The Subpathway-GMir method has been implemented as a freely available R package. |
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| Keywords: | microRNA mRNA cancer pathway identification regulation |
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