Comparisons of microRNA Set Enrichment Analysis Tools on Cancer De-Regulated miRNAs from TCGA Expression Datasets | Bentham Science

摘要

Background: De-regulation of microRNAs (miRNAs) is closely related to many complexdiseases, including cancers. In The Cancer Genome Atlas (TCGA), hundreds of differentiallyexpressed miRNAs are stored for each type of cancer, which are hard to be intuitively interpreted.To date, several miRNA set enrichment tools have been tailored to predict the potential diseaseassociations and functions of de-regulated miRNAs, including the miRNA Enrichment Analysis andAnnotation tool (miEAA) and Tool for Annotations of human MiRNAs (TAM1.0 &TAM 2.0).However, independent benchmarking of these tools is warranted to assess their effectiveness androbustness, and the relationship between enrichment analysis results and the prognosis significanceof cancers.Methods: Based on differentially expressed miRNAs from expression profiles in TCGA, weperformed a series of tests and a comprehensive comparison of the enrichment analysis results ofmiEAA, TAM 1.0 and TAM 2.0. The work focused on the performance of the three tools, diseasesimilarity based on miRNA-disease associations from the enrichment analysis results, therelationship between the overrepresented miRNAs from enrichment analysis results and theprognosis significance of cancers.Results: The main results show that TAM 2.0 is more likely to identify the regulatory disease’sfunctions of de-regulated miRNA; it is feasible to calculate disease similarity based on enrichmentanalysis results of TAM 2.0; and there is weak positive correlation between the occurrencefrequency of miRNAs in the TAM 2.0 enrichment analysis results and the prognosis significance ofthe cancer miRNAs.Conclusion: Our comparison results not only provide a reference for biomedical researchers tochoose appropriate miRNA set enrichment analysis tools to achieve their purpose but alsodemonstrate that the degree of overrepresentation of miRNAs could be a supplementary indicator ofthe disease similarity and the prognostic effect of cancer miRNAs.