MicroRNAs (miRNAs) are endogenous ∼23 nt RNAs that play important gene-regulatory roles in animals and plants by pairing to the mRNAs of protein-coding genes to direct their posttranscriptional repression. This review outlines the current understanding of miRNA target recognition in animals and discusses the widespread impact of miRNAs on both the expression and evolution of protein-coding genes.

MicroRNAs: Target Recognition and Regulatory Functions

Large-Scale Simultaneous Hypothesis Testing: The Choice of a Null Hypothesis

BackgroundMicroRNAs (miRNAs) are a novel class of non-coding small RNAs. In mammalian cells, miRNAs repress the translation of messenger RNAs (mRNAs) or degrade mRNAs. miRNAs play important roles in development and differentiation, and they are also implicated in aging, and oncogenesis. Predictions of targets of miRNAs suggest that they may regulate more than one-third of all genes. The overall functions of mammalian miRNAs remain unclear. Combinatorial regulation by transcription factors alone or miRNAs alone offers a wide range of regulatory programs. However, joining transcriptional and post-transcriptional regulatory mechanisms enables higher complexity regulatory programs that in turn could give cells evolutionary advantages. Investigating coordinated regulation of genes by miRNAs and transcription factors (TFs) from a statistical standpoint is a first step that may elucidate some of their roles in various biological processes.ResultsHere, we studied the nature and scope of coordination among regulators from the transcriptional and miRNA regulatory layers in the human genome. Our findings are based on genome wide statistical assessment of regulatory associations ("interactions") among the sets of predicted targets of miRNAs and sets of putative targets of transcription factors. We found that combinatorial regulation by transcription factor pairs and miRNA pairs is much more abundant than the combinatorial regulation by TF-miRNA pairs. In addition, many of the strongly interacting TF-miRNA pairs involve a subset of master TF regulators that co-regulate genes in coordination with almost any miRNA. Application of standard measures for evaluating the degree of interaction between pairs of regulators show that strongly interacting TF-miRNA, TF-TF or miRNA-miRNA pairs tend to include TFs or miRNAs that regulate very large numbers of genes. To correct for this potential bias we introduced an additional Bayesian measure that incorporates not only how significant an interaction is but also how strong it is. Putative pairs of regulators selected by this procedure are more likely to have biological coordination. Importantly, we found that the probability of a TF-miRNA pair forming feed forward loops with its common target genes (where the miRNA simultaneously suppresses the TF and many of its targets) is increased for strongly interacting TF-miRNA pairs.ConclusionGenes are more likely to be co-regulated by pairs of TFs or pairs of miRNAs than by pairs of TF-miRNA, perhaps due to higher probability of evolutionary duplication events of shorter DNA sequences. Nevertheless, many gene sets are reciprocally regulated by strongly interacting pairs of TF-miRNA, which suggests an effective mechanism to suppress functionally related proteins. Moreover, the particular type of feed forward loop (with two opposing modes where the TF activates its target genes or the miRNA simultaneously suppresses this TF and the TF-miRNA joint target genes) is more prevalent among strongly interacting TF-miRNA pairs. This may be attributed to a process that prevents waste of cellular resources or a mechanism to accelerate mRNA degradation.

Inter- and Intra-Combinatorial Regulation by Transcriptions Factors and MicroRNAs.

Ovarian cancer has the highest mortality rate among gynecological cancers. Early clinical signs are missing and there is an urgent need to establish early diagnosis biomarkers. MicroRNAs are promising biomarkers in this respect. In this paper, we review the most recent advances regarding the alterations of microRNAs in ovarian cancer. We have briefly described the contribution of miRNAs in the mechanisms of ovarian cancer invasion, metastasis, and chemotherapy sensitivity. We have also summarized the alterations underwent by microRNAs in solid ovarian tumors, in animal models for ovarian cancer, and in various ovarian cancer cell lines as compared to previous reviews that were only focused the circulating microRNAs as biomarkers. In this context, we consider that the biomarker screening should not be limited to circulating microRNAs per se, but rather to the simultaneous detection of the same microRNA alteration in solid tumors, in order to understand the differences between the detection of nucleic acids in early vs. late stages of cancer. Moreover, in vitro and in vivo models should also validate these microRNAs, which could be very helpful as preclinical testing platforms for pharmacological and/or molecular genetic approaches targeting microRNAs. The enormous quantity of data produced by preclinical and clinical studies regarding the role of microRNAs that act synergistically in tumorigenesis mechanisms that are associated with ovarian cancer subtypes, should be gathered, integrated, and compared by adequate methods, including molecular clustering. In this respect, molecular clustering analysis should contribute to the discovery of best biomarkers-based microRNAs assays that will enable rapid, efficient, and cost-effective detection of ovarian cancer in early stages. In conclusion, identifying the appropriate microRNAs as clinical biomarkers in ovarian cancer might improve the life quality of patients.

https://www.mdpi.com/2073-4409/9/1/169/pdf

Role of microRNAs as Clinical Cancer Biomarkers for Ovarian Cancer: A Short Overview.

Defining disease-related modules based on weighted miRNA synergistic network

The identification of miRNA regulatory modules can help decipher miRNAs combinatorial regulation effects on the pathogenesis underlying complex diseases, especially in cancer. By integrating miRNA/mRNA expression profiles and sequence-based predicted target site information, we develop a novel cluster-based computational method named CoModule for identifying miRNA regulatory modules (MRMs). The ultimate goal of CoModule is to detect the MRMs, in which the miRNAs in each module are expected to present cooperative mechanisms in regulating their targets mRNAs. Here, the co-expression of miRNAs are believed to present cooperative regulatory relationship, therefore, the critical step of CoModule is first to partition the miRNAs with similar expression into a cluster by employing rough set clustering. After gaining credible miRNA clusters, the targets of regulator are naturally added into corresponding clusters to produce the final miRNA regulatory modules. We apply this present method to ovarian cancer datasets and make a comparison with the other two existing prominent approaches. The results indicate that the modules identified by CoModule perform better than the other two methods ranging from the topological aspects to the biological function. Survival analysis detects a number of prognostic modules with statistical significance, which can help reveal the potential diagnostic for ovarian cancer.

A Novel Cluster-Based Computational Method to Identify miRNA Regulatory Modules

It is well known that regulators known as microRNA (miRNA) and transcription factor (TF) have been found to play an important role in gene regulation. However, there are few researches of collaborative regulatory (co-regulatory) mechanism between miRNA and TF on system level (function level). Meanwhile, recent advances in high-throughput genomic technologies have enabled researchers to collect diverse large-scale genomic data, which can be used to study the co-regulatory mechanism between miRNA and TF. In this paper, we propose a novel method called Sparse Network regularized non-negative matrix factorization for co-regulatory modules identification which adopts multiple non-negative matrix factorization framework to identify co-regulatory modules including miRNAs, TFs and genes. This method jointly integrates miRNA, TF and gene expression profiles, and additional priori networks were added in a regularized manner. In addition, to avoid the sparsity of these networks, we employ the sparsity penalties to the variables to achieve modular solutions. The mathematical formulation can be effectively solved by an iterative multiplicative updating algorithm. We apply this method to multiple genomic data including the expression profiles of miRNAs, TFs and genes on breast cancer obtained from TCGA, priori miRNA-gene regulations, TF-gene regulations and gene-gene interactions. The results show that the miRNAs, TFs and genes of the co-regulatory modules are significantly associated and modules have a reasonable size distribution. Furthermore, the co-regulatory modules are significantly enriched in Gene Ontology biological processes and Kyoto Encyclopedia of Genes and Genomes pathways, respectively.

Discovery of microRNAs and Transcription Factors Co-Regulatory Modules by Integrating Multiple Types of Genomic Data

At the co-regulatory network level, functional modules comprising microRNAs (miRNAs), transcription factors (TFs), and common target genes (mRNAs) are found to be widespread in diverse organisms from bacteria to human, suggesting that co-regulatory functional modules (CRMs) serve as basic building blocks of transcription networks. Identification of CRMs would contribute to explore the miRNAs and TFs regulatory mechanism to specific cancer. However, few studies considered the functional modules which contain TFs and overlapping parts now. In this paper, we propose a novel computational framework called overlapping spectral clustering (OSC) to systematically detect overlapping CRMs using miRNA/mRNA/TF expression profiles. First, empirical Bayes theories are used to improve building more exact relation matrix instead of simple Pearson correlation analysis. In order to ensure the adaptivity of the whole framework, the eigenvalue decomposition (Eigengap) is employed to determine the module number automatically. Then considering key regulators which may involve in more function modules, we propose a novel overlapping detection approach to observe whether the edges between modules can be overlapped by other modules. Comparing with existing methods on breast cancer and ovary cancer data sets from The Cancer Genome Atlas, we showed that the CRMs identified by OSC are more functionally enriched. 80% TFs and 90% miRNAs in CRMs are related to corresponding diseases. And the overlapping parts between CRMs were also analyzed and verified by standard databases and previous literatures.

Identifying Functional Modules in Co-Regulatory Networks Through Overlapping Spectral Clustering

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A Novel Cluster-Based Computational Method to Identify miRNA Regulatory Modules

Discovery of microRNAs and Transcription Factors Co-Regulatory Modules by Integrating Multiple Types of Genomic Data

Identifying Functional Modules in Co-Regulatory Networks Through Overlapping Spectral Clustering