sTAM: An Online Tool for the Discovery of miRNA-set Level Disease Biomarkers

Abstract

microRNAs (miRNAs) are one class of important small noncoding RNA molecules, which have shown their excellent ability as biomarkers of various diseases. However, current miRNA biomarkers including those comprised of multiple miRNAs work at a single-miRNA level but not at a miRNA set level. Given the rapidly accumulated miRNA omics data, it is believed that miRNA set level analysis could be an important supplement to the single miRNA level analysis. For doing so, here we presented a computational method for single-sample miRNA set enrichment analysis and developed the sTAM tool (http://mir.rnanut.net/stam). Moreover, we demonstrated the usefulness of sTAM scores in discovering miRNA-set level biomarkers through two case studies. We conducted pan-cancer analysis of the sTAM scores of "tumor suppressor miRNA set" on 15 types of cancers from TCGA and 14 types of cancers from GEO, finding that the scores show a good performance in distinguishing the cancers from the controls. Moreover, we revealed that the sTAM score of "brain development" miRNA set can effectively predict cerebrovascular disorder (CVD). Finally, we believe sTAM can be used in discovering disease-related biomarkers at a miRNA-set level.