title: Which statistical significance test best detects oncomiRNAs in cancer tissues? An exploratory analysis.
abstract: MicroRNAs(miRNAs) often exert their oncogenic and tumor suppressor functions by suppressing protein-coding genes expressions in cancers and thus have a strong association with cancers' generation, development and metastasis. Through comprehensively understanding differentially expressed miRNAs (oncomiRNA) in tumor tissues, we can elucidate the underlying molecular mechanisms in tumorigenesis and develop novel strategies for cancer diagnosis and treatment. The differential expression of miRNAs can now be analyzed through numerous statistical significance tests based on different principles, which are also available in various R packages. However, the results can be notably different. In this study, we compared miRNAs obtained from 6 common significance tests/R packages (t-test, Limma, DESeq, edgeR, LRT and MARS) with the miRNAs archived in two databases; HMDD 2.0 database, which collects experimentally validated differentially expressed miRNAs, and Infer microRNA-disease association database, which contains the potential disease-associated miRNAs by network forecasting. Finally, we sought the MARS method in DEGseq package more effectively searched out differentially expressed miRNAs than other common methods.
authors: Department of Biological Engineering, School of Chemical Engineering, Tianjin University, Tianjin, China.
authors: Wei
authors: W
authors: Tang
authors: Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China.|School of Computer Science and Technology, Tianjin University, Tianjin, China.
authors: Zhijun
authors: Z
authors: Liao
authors: School of Computer Science and Technology, Tianjin University, Tianjin, China.|State Key Laboratory of Medicinal Chemical Biology, Nankai University, Tianjin, China.
authors: Quan
authors: Q
authors: Zou
mesh_terms: D019540:Area Under Curve; D014408:Biomarkers, Tumor / Q000235:genetics*; D019295:Computational Biology / Q000706:statistics & numerical data; D003627:Data Interpretation, Statistical; D030541:Databases, Genetic / Q000706:statistics & numerical data; D020869:Gene Expression Profiling / Q000706:statistics & numerical data*; D015972:Gene Expression Regulation, Neoplastic; D053263:Gene Regulatory Networks; D006801:Humans; D035683:MicroRNAs / Q000235:genetics*; D015233:Models, Statistical; D009369:Neoplasms / Q000235:genetics* / Q000473:pathology / Q000628:therapy; D011237:Predictive Value of Tests; D011379:Prognosis; D012372:ROC Curve; D015398:Signal Transduction; D059467:Transcriptome
chemical_list: D014408:Biomarkers, Tumor; D035683:MicroRNAs
keywords: MARS; differential expression; microRNA; oncomiRNA; statistical significance test