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MicroRNAs (miRNAs) are a class of small non-coding RNA molecules that regulate gene expression at post-transcriptional level. Increasing evidences show aberrant expression of miRNAs in varieties of diseases. Targeting the dysregulated miRNAs with small molecule drugs has become a novel therapy for many human diseases, especially cancer. Here, we proposed a novel computational approach to identify associations between small molecules and miRNAs based on functional similarity of differentially expressed genes. At the significance level of p < 0.01, we constructed the small molecule and miRNA functional similarity network involving 111 small molecules and 20 miRNAs. Moreover, we also predicted associations between drugs and diseases through integrating our identified small molecule-miRNA associations with experimentally validated disease related miRNAs. As a result, we identified 2265 associations between FDA approved drugs and diseases, in which ~35% associations have been validated by comprehensive literature reviews. For breast cancer, we identified 19 potential drugs, in which 12 drugs were supported by previous studies. In addition, we performed survival analysis for the patients from TCGA and GEO database, which indicated that the associated miRNAs of 4 drugs might be good prognosis markers in breast cancer. Collectively, this study proposed a novel approach to predict small molecule and miRNA associations based on functional similarity, which may pave a new way for miRNA-targeted therapy and drug repositioning., Competing Interests: Competing financial interests: The authors declare no competing financial interests.

miRNAs are key post-transcriptional regulators of many essential biological processes, and their dysregulation has been validated in almost all human cancers. Restoring aberrantly expressed miRNAs might be a novel therapeutics. Recently, many studies have demonstrated that small molecular compounds can affect miRNA expression. Thus, prediction of associations between small molecules and miRNAs is important for investigation of miRNA-targeted drugs. Here, we analyzed 39 miRNA-perturbed gene expression profiles, and then calculated the similarity of transcription responses between miRNA perturbation and drug treatment to predict drug-miRNA associations. At the significance level of 0.05, we obtained 6501 candidate associations between 1295 small molecules and 25 miRNAs, which included 624 FDA approved drugs. Finally, we constructed the Psmir database to store all potential associations and the related materials. In a word, Psmir served as a valuable resource for dissecting the biological significance in small molecules' effects on miRNA expression, which will facilitate developing novel potential therapeutic targets or treatments for human cancers. Psmir is supported by all major browsers, and is freely available at http://www.bio-bigdata.com/Psmir/.

Motivation: miRNAs play crucial roles in human diseases and newly discovered could be targeted by small molecule (SM) drug compounds. Thus, the identification of small molecule drug compounds (SM) that target dysregulated miRNAs in cancers will provide new insight into cancer biology and accelerate drug discovery for cancer therapy., Results: In this study, we aimed to develop a novel computational method to comprehensively identify associations between SMs and miRNAs. To this end, exploiting multiple molecular interaction databases, we first established an integrated SM-miRNA association network based on 690 561 SM to SM interactions, 291 600 miRNA to miRNA associations, as well as 664 known SM to miRNA targeting pairs. Then, by performing Random Walk with Restart algorithm on the integrated network, we prioritized the miRNAs associated to each of the SMs. By validating our results utilizing an independent dataset we obtained an area under the ROC curve greater than 0.7. Furthermore, comparisons indicated our integrated approach significantly improved the identification performance of those simple modeled methods. This computational framework as well as the prioritized SM-miRNA targeting relationships will promote the further developments of targeted cancer therapies., Contact: yxli@sibs.ac.cn, lixia@hrbmu.edu.cn or jiangwei@hrbmu.edu.cn, Supplementary Information: Supplementary data are available at Bioinformatics online., (© The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

Over the past decades, studies have reported that the combinatorial regulation of transcription factors (TFs) and microRNAs (miRNAs) is essential for the appropriate execution of biological events and developmental processes. Dysregulations of these regulators often cause diseases. However, there are no available resources on the regulatory cascades of TFs and miRNAs in the context of human diseases. To fulfill this vacancy, we established the TMREC database in this study. First, we integrated curated transcriptional and post-transcriptional regulations to construct the TF and miRNA regulatory network. Next, we identified all linear paths using the Breadth First Search traversal method. Finally, we used known disease-related genes and miRNAs to measure the strength of association between cascades and diseases. Currently, TMREC consists of 74,248 cascades and 25,194 cascade clusters, involving in 412 TFs, 266 miRNAs and 545 diseases. With the expanding of experimental support regulation data, we will regularly update the database. TMREC aims to help experimental biologists to comprehensively analyse gene expression regulation, to understand the aetiology and to predict novel therapeutic targets. TMREC is freely available at http://bioinfo.hrbmu.edu.cn/TMREC/.

As two kinds of important gene expression regulators, both epigenetic modification and microRNA (miRNA) can play significant roles in a wide range of human diseases. Recently, many studies have demonstrated that epigenetics and miRNA can affect each other in various ways. In this study, we established the EpimiR database, which collects 1974 regulations between 19 kinds of epigenetic modifications (such as DNA methylation, histone acetylation, H3K4me3, H3S10p) and 617 miRNAs across seven species (including Homo sapiens, Mus musculus, Rattus norvegicus, Gallus gallus, Epstein-Barr virus, Canis familiaris and Arabidopsis thaliana) from >300 references in the literature. These regulations can be divided into two parts: miR2Epi (103 entries describing how miRNA regulates epigenetic modification) and Epi2miR (1871 entries describing how epigenetic modification affects miRNA). Each entry of EpimiR not only contains basic descriptions of the validated experiment (method, species, reference and so on) but also clearly illuminates the regulatory pathway between epigenetics and miRNA. As a supplement to the curated information, the EpimiR extends to gather predicted epigenetic features (such as predicted transcription start site, upstream CpG island) associated with miRNA for users to guide their future biological experiments. Finally, EpimiR offers download and submission pages. Thus, EpimiR provides a fairly comprehensive repository about the mutual regulation between epigenetic modifications and miRNAs, which will promote the research on the regulatory mechanism of epigenetics and miRNA. Database URL: http://bioinfo.hrbmu.edu.cn/EpimiR/.

Alzheimer's disease (AD) is an incurable neurodegenerative disorder. Much effort has been devoted to developing effective therapeutic agents. Recently, targeting microRNAs (miRNAs) with small molecules has become a novel therapy for human diseases. In this study, we present a systematic computational approach to construct a bioactive Small molecule and miRNA association Network in AD (SmiRN-AD), which is based on the gene expression signatures of bioactive small molecule perturbation and AD-related miRNA regulation. We also performed topological and functional analysis of the SmiRN-AD from multiple perspectives. At the significance level of p ≤ 0.01, 496 small molecule-miRNA associations, including 25 AD-related miRNAs and 275 small molecules, were recognized and used to construct the SmiRN-AD. The drugs that were connected with the same miRNA tended to share common drug targets (p = 1.72 × 10(-4)) and belong to the same therapeutic category (p = 4.22 × 10(-8)). The miRNAs that were linked to the same small molecule regulated more common miRNA targets (p = 6.07 × 10(-3)). Further analysis of the positive connections (quinostatin and miR-148b, amantadine and miR-15a) and the negative connections (melatonin and miR-30e-5p) indicated that our large-scale predictions afforded specific biological insights into AD pathogenesis and therapy. This study proposes a holistic strategy for deciphering the associations between small molecules and miRNAs in AD, which may be helpful for developing a novel effective miRNA-associated therapeutic strategy for AD. A comprehensive database for the SmiRN-AD and the differential expression patterns of the miRNA targets in AD is freely available at http://bioinfo.hrbmu.edu.cn/SmiRN-AD/.

Motivation: Alzheimer's disease (AD) is a severe neurodegenerative disease of the central nervous system that may be caused by perturbation of regulatory pathways rather than the dysfunction of a single gene. However, the pathology of AD has yet to be fully elucidated., Results: In this study, we systematically analyzed AD-related mRNA and miRNA expression profiles as well as curated transcription factor (TF) and miRNA regulation to identify active TF and miRNA regulatory pathways in AD. By mapping differentially expressed genes and miRNAs to the curated TF and miRNA regulatory network as active seed nodes, we obtained a potential active subnetwork in AD. Next, by using the breadth-first-search technique, potential active regulatory pathways, which are the regulatory cascade of TFs, miRNAs and their target genes, were identified. Finally, based on the known AD-related genes and miRNAs, the hypergeometric test was used to identify active pathways in AD. As a result, nine pathways were found to be significantly activated in AD. A comprehensive literature review revealed that eight out of nine genes and miRNAs in these active pathways were associated with AD. In addition, we inferred that the pathway hsa-miR-146a→STAT1→MYC, which is the source of all nine significantly active pathways, may play an important role in AD progression, which should be further validated by biological experiments. Thus, this study provides an effective approach to finding active TF and miRNA regulatory pathways in AD and can be easily applied to other complex diseases.

Unlabelled: The inappropriate expression of microRNAs (miRNAs) is closely related with disease diagnosis, prognosis and therapy response. Recently, many studies have demonstrated that bioactive small molecules (or drugs) can regulate miRNA expression, which indicates that targeting miRNAs with small molecules is a new therapy for human diseases. In this study, we established the SM2miR database, which recorded 2925 relationships between 151 small molecules and 747 miRNAs in 17 species after manual curation from nearly 2000 articles. Each entry contains the detailed information about small molecules, miRNAs and evidences of their relationships, such as species, miRBase Accession number, DrugBank Accession number, PubChem Compound Identifier (CID), expression pattern of miRNA, experimental method, tissues or conditions for detection. SM2miR database has a user-friendly interface to retrieve by miRNA or small molecule. In addition, we offered a submission page. Thus, SM2miR provides a fairly comprehensive repository about the influences of small molecules on miRNA expression, which will promote the development of miRNA therapeutics., Availability: SM2miR is freely available at http://bioinfo.hrbmu.edu.cn/SM2miR/.

The use of small molecules to target miRNAs is a new type of therapy for human diseases, particularly cancers. We proposed a novel high-throughput approach to identify the biological links between small molecules and miRNAs in 23 different cancers and constructed the Small Molecule-MiRNA Network (SMirN) for each cancer to systematically analyze the properties of their associations. In each SMirN, we partitioned small molecules (miRNAs) into modules, in which small molecules (miRNAs) were connected with one miRNA (small molecule). Almost all of the miRNA modules comprised miRNAs that had similar target genes and functions or were members of the same miRNA family. Most of the small molecule modules involved compounds with similar chemical structures, modes of action, or drug interactions. These modules can be used to identify drug candidates and new indications for existing drugs. Therefore, our approach is valuable to drug discovery and cancer therapy.