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.