Abstract
MicroRNAs (miRNAs), a class of endogenous small regulatory RNAs, play important roles in many biological and physiological processes. The perturbations of some miRNAs, which are usually called as onco-microRNAs (onco-miRs), are significantly associated with multiple stages of cancer. Although hundreds of miRNAs have been discovered, the perturbed miRNA regulatory networks and their functions are still poorly understood in cancer. Analyzing the expression patterns of miRNA target genes is a very useful strategy to infer the perturbed miRNA networks. However, due to the complexity of cancer transcriptome, current methods often encounter low sensitivity and report few onco-miR candidates. Here, we developed a new method, named miRHiC (enrichment analysis of miRNA targets in Hierarchical gene Co-expression signatures), to infer the perturbed miRNA regulatory networks by using the hierarchical co-expression signatures in large-scale cancer gene expression datasets. The method can infer onco-miR candidates and their target networks which are only linked to sub-clusters of the differentially expressed genes at fine scales of the co-expression hierarchy. On two real datasets of lung cancer and hepatocellular cancer, miRHiC uncovered several known onco-miRs and their target genes (such as miR-26, miR-29, miR-124, miR-125 and miR-200) and also identified many new candidates (such as miR-149, which is inferred in both types of cancers). Using hierarchical gene co-expression signatures, miRHiC can greatly increase the sensitivity for inferring the perturbed miRNA regulatory networks in cancer. All Perl scripts of miRHiC and the detailed documents are freely available on the web at http://bioinfo.au.tsinghua.edu.cn/member/jgu/miRHiC/.
Highlights
MicroRNAs are a class of small (,22 nt) regulatory RNAs, which play important roles in many essential biological and physiological processes, such as embryo development, cancer progression and immune response
Results showed that the empirical p-values are uniformly distributed (Figure 2). Another possible bias affecting empirical p-value is caused by different sizes of miRNA target gene sets: some miRNAs have more than 1,000 target genes while some only have less than 50 target genes
Analyzing miRNA target gene set enrichment in differentially expressed genes of large-scale gene expression profiles can greatly advance our understandings of the perturbed miRNA regulations
Summary
MicroRNAs (miRNAs) are a class of small (,22 nt) regulatory RNAs, which play important roles in many essential biological and physiological processes, such as embryo development, cancer progression and immune response. The perturbations of some miRNAs, usually called as onco-microRNAs (onco-miRs, including both oncogenic and tumor suppressive miRNAs in this study), have been reported to be significantly associated with multiple stages of cancer. To infer the perturbed miRNA regulatory network, one popular strategy is to analyze miRNA target gene set enrichments in differentially expressed gene signatures. This includes many developed methods, such as gene set analysis by hyper-geometric test (HG-test, or Fisher’s exact test); GSEA (gene set enrichment analysis) [4,5]; FAME (functional assignment of miRNAs via enrichment) [6]; and miRBridge [7], which assume that the target gene set enrichments reflect the perturbations of their upstream miRNA regulation strengths. Due to the complexity of cancer transcriptome, these methods usually show low sensitivity of inferring onco-miR candidates (here, the ‘‘sensitivity’’ mainly means the number of inferred onco-miR candidates under a given statistical significance level)
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