title: MiRSEA: Discovering the pathways regulated by dysfunctional MicroRNAs.
abstract: Recent studies have shown that dysfunctional microRNAs (miRNAs) are involved in the progression of various cancers. Dysfunctional miRNAs may jointly regulate their target genes and further alter the activities of canonical biological pathways. Identification of the pathways regulated by a group of dysfunctional miRNAs could help uncover the pathogenic mechanisms of cancer and facilitate development of new drug targets. Current miRNA-pathway analyses mainly use differentially-expressed miRNAs to predict the shared pathways on which they act. However, these methods fail to consider the level of differential expression level, which could improve our understanding of miRNA function. We propose a novel computational method, MicroRNA Set Enrichment Analysis (MiRSEA), to identify the pathways regulated by dysfunctional miRNAs. MiRSEA integrates the differential expression levels of miRNAs with the strength of miRNA pathway associations to perform direct enrichment analysis using miRNA expression data. We describe the MiRSEA methodology and illustrate its effectiveness through analysis of data from hepatocellular cancer, gastric cancer and lung cancer. With these analyses, we show that MiRSEA can successfully detect latent biological pathways regulated by dysfunctional miRNAs. We have implemented MiRSEA as a freely available R-based package on CRAN (https://cran.r-project.org/web/packages/MiRSEA/).
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, PR China.
authors.forename: Junwei
authors.initials: J
authors.lastname: Han
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, PR China.
authors.forename: Siyao
authors.initials: S
authors.lastname: Liu
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, PR China.
authors.forename: Yunpeng
authors.initials: Y
authors.lastname: Zhang
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, PR China.
authors.forename: Yanjun
authors.initials: Y
authors.lastname: Xu
authors.affiliation: College of Basic Medical Science, Heilongjiang University of Chinese Medicine, Harbin 150040, PR China.
authors.forename: Ying
authors.initials: Y
authors.lastname: Jiang
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, PR China.
authors.forename: Chunlong
authors.initials: C
authors.lastname: Zhang
authors.affiliation: School of Medical Informatics, Daqing Campus, Harbin Medical University, Harbin, 150081, PR China.
authors.forename: Chunquan
authors.initials: C
authors.lastname: Li
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, PR China.
authors.forename: Xia
authors.initials: X
authors.lastname: Li
mesh_terms: D000465:Algorithms; D006528:Carcinoma, Hepatocellular / Q000235:genetics; D002289:Carcinoma, Non-Small-Cell Lung / Q000235:genetics; D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods*; D015972:Gene Expression Regulation, Neoplastic; D053263:Gene Regulatory Networks; D006801:Humans; D020407:Internet; D008175:Lung Neoplasms / Q000235:genetics; D035683:MicroRNAs / Q000235:genetics*; D015203:Reproducibility of Results; D015398:Signal Transduction / Q000235:genetics*; D013274:Stomach Neoplasms / Q000235:genetics
chemical_list: D035683:MicroRNAs
keywords: cancer; enrichment analysis; mRNA; microRNA; pathway