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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.
AuthorsWei Tang, Zhijun Liao, Quan Zou
JournalOncotarget (Oncotarget) Vol. 7 Issue 51 Pg. 85613-85623 (Dec 20 2016) ISSN: 1949-2553 [Electronic] United States
PMID27784000 (Publication Type: Comparative Study, Journal Article)
Chemical References
  • Biomarkers, Tumor
  • MicroRNAs
Topics
  • Area Under Curve
  • Biomarkers, Tumor (genetics)
  • Computational Biology (statistics & numerical data)
  • Data Interpretation, Statistical
  • Databases, Genetic (statistics & numerical data)
  • Gene Expression Profiling (statistics & numerical data)
  • Gene Expression Regulation, Neoplastic
  • Gene Regulatory Networks
  • Humans
  • MicroRNAs (genetics)
  • Models, Statistical
  • Neoplasms (genetics, pathology, therapy)
  • Predictive Value of Tests
  • Prognosis
  • ROC Curve
  • Signal Transduction
  • Transcriptome

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