A miRNA-driven inference model to construct potential drug-disease associations for drug repositioning

MicroRNA-Mediated Drug Repurposing Unveiled Potential Candidate Drugs for Prolactinoma Treatment

INTRODUCTION

Prolactinomas, also called lactotroph adenomas, are the most encountered type of hormone-secreting pituitary neuroendocrine tumors in the clinic. The preferred first-line therapy is a medical treatment with dopamine agonists (DAs), mainly cabergoline, to reduce serum prolactin levels, tumor volume, and mass effect. However, in some cases, patients have displayed DA resistance with aggressive tumor behavior or are faced with recurrence after drug withdrawal. Also, currently used therapeutics have notorious side effects and impair the life quality of the patients.

METHODS

Since the amalgamation of clinical and laboratory data besides tumor histopathogenesis and transcriptional regulatory features of the tumor emerges to exhibit essential roles in the behavior and progression of prolactinomas; in this work, we integrated mRNA- and microRNA (miRNA)-level transcriptome data that exploit disease-specific signatures in addition to biological and pharmacological data to elucidate a rational prioritization of pathways and drugs in prolactinoma.

RESULTS

We identified 8 drug candidates through drug repurposing based on mRNA-miRNA-level data integration and evaluated their potential through in vitro assays in the MMQ cell line. Seven repurposed drugs including 5-fluorocytosine, nortriptyline, neratinib, puromycin, taxifolin, vorinostat, and zileuton were proposed as potential drug candidates for the treatment of prolactinoma. We further hypothesized possible mechanisms of drug action on MMQ cell viability through analyzing the PI3K/Akt signaling pathway and cell cycle arrest via flow cytometry and Western blotting.

DISCUSSION

We presented the transcriptomic landscape of prolactinoma through miRNA and mRNA-level data integration and proposed repurposed drug candidates based on this integration. We validated our findings through testing cell viability, cell cycle phases, and PI3K/Akt protein expressions. Effects of the drugs on cell cycle phases and inhibition of the PI3K/Akt pathway by all drugs gave us promising output for further studies using these drugs in the treatment of prolactinoma. This is the first study that reports miRNA-mediated repurposed drugs for prolactinoma treatment via in vitro experiments.

A Fast Linear Neighborhood Similarity-Based Network Link Inference Method to Predict MicroRNA-Disease Associations

Increasing evidences revealed that microRNAs (miRNAs) play critical roles in important biological processes. The identification of disease-related miRNAs is critical to understand the molecular mechanisms of human diseases. Most existing computational methods require diverse features to predict miRNA-disease associations. However, diverse features are not available for all miRNAs or diseases. In addition, most methods can't predict links for miRNAs or diseases without association information. In this paper, we propose a fast linear neighborhood similarity-based network link inference method, named FLNSNLI, to predict miRNA-disease associations. First, known miRNA-disease associations are formulated as a bipartite network, and miRNAs (or diseases) are expressed as association profiles. Second, miRNA-miRNA similarity and disease-disease similarity are calculated by fast linear neighborhood similarity measure and association profiles. Third, the label propagation algorithm is respectively implemented on two sides to score candidate miRNA-disease associations. Finally, FLNSNLI adopts the weighted average strategy and makes predictions. Moreover, we develop a link complementing approach, and extend FLNSNLI to predict links for miRNAs (or diseases) without known associations. In computational experiments, FLNSNLI produces high-accuracy performances, and outperforms other state-of-the-art methods. More importantly, FLNSNLI requires less information but performs well. Case studies on three popular diseases show that FLNSNLI is useful for the microRNA-disease association prediction.

In silico drug repositioning based on the integration of chemical, genomic and pharmacological spaces

BACKGROUND

Drug repositioning refers to the identification of new indications for existing drugs. Drug-based inference methods for drug repositioning apply some unique features of drugs for new indication prediction. Complementary information is provided by these different features. It is therefore necessary to integrate these features for more accurate in silico drug repositioning.

RESULTS

In this study, we collect 3 different types of drug features (i.e., chemical, genomic and pharmacological spaces) from public databases. Similarities between drugs are separately calculated based on each of the features. We further develop a fusion method to combine the 3 similarity measurements. We test the inference abilities of the 4 similarity datasets in drug repositioning under the guilt-by-association principle. Leave-one-out cross-validations show the integrated similarity measurement IntegratedSim receives the best prediction performance, with the highest AUC value of 0.8451 and the highest AUPR value of 0.2201. Case studies demonstrate IntegratedSim produces the largest numbers of confirmed predictions in most cases. Moreover, we compare our integration method with 3 other similarity-fusion methods using the datasets in our study. Cross-validation results suggest our method improves the prediction accuracy in terms of AUC and AUPR values.

CONCLUSIONS

Our study suggests that the 3 drug features used in our manuscript are valuable information for drug repositioning. The comparative results indicate that integration of the 3 drug features would improve drug-disease association prediction. Our study provides a strategy for the fusion of different drug features for in silico drug repositioning.

Comparative analysis of similarity measurements in miRNAs with applications to miRNA-disease association predictions

BACKGROUND

As regulators of gene expression, microRNAs (miRNAs) are increasingly recognized as critical biomarkers of human diseases. Till now, a series of computational methods have been proposed to predict new miRNA-disease associations based on similarity measurements. Different categories of features in miRNAs are applied in these methods for miRNA-miRNA similarity calculation. Benchmarking tests on these miRNA similarity measures are warranted to assess their effectiveness and robustness.

RESULTS

In this study, 5 categories of features, i.e. miRNA sequences, miRNA expression profiles in cell-lines, miRNA expression profiles in tissues, gene ontology (GO) annotations of miRNA target genes and Medical Subject Heading (MeSH) terms of miRNA-associated diseases, are collected and similarity values between miRNAs are quantified based on these feature spaces, respectively. We systematically compare the 5 similarities from multi-statistical views. Furthermore, we adopt a rule-based inference method to test their performance on miRNA-disease association predictions with the similarity measurements. Comprehensive comparison is made based on leave-one-out cross-validations and a case study. Experimental results demonstrate that the similarity measurement using MeSH terms performs best among the 5 measurements. It should be noted that the other 4 measurements can also achieve reliable prediction performance. The best-performed similarity measurement is used for new miRNA-disease association predictions and the inferred results are released for further biomedical screening.

CONCLUSIONS

Our study suggests that all the 5 features, even though some are restricted by data availability, are useful information for inferring novel miRNA-disease associations. However, biased prediction results might be produced in GO- and MeSH-based similarity measurements due to incomplete feature spaces. Similarity fusion may help produce more reliable prediction results. We expect that future studies will provide more detailed information into the 5 feature spaces and widen our understanding about disease pathogenesis.

Prediction and interpretation of miRNA-disease associations based on miRNA target genes using canonical correlation analysis

Background

It has been shown that the deregulation of miRNAs is associated with the development and progression of many human diseases. To reduce time and cost of biological experiments, a number of algorithms have been proposed for predicting miRNA-disease associations. However, the existing methods rarely investigated the cause-and-effect mechanism behind these associations, which hindered further biomedical follow-ups.

Results

In this study, we presented a CCA-based model in which the possible molecular causes of miRNA-disease associations were comprehensively revealed by extracting correlated sets of genes and diseases based on the co-occurrence of miRNAs in target gene profiles and disease profiles. Our method directly suggested the underlying genes involved, which could be used for experimental tests and confirmation. The inference of associated diseases of a new miRNA was made by taking into account the weight vectors of the extracted sets.

We extracted 60 pairs of correlated sets from 404 miRNAs with two profiles for 2796 target genes and 362 diseases. The extracted diseases could be considered as possible outcomes of miRNAs regulating the target genes which appeared in the same set, some of which were supported by independent source of information. Furthermore, we tested our method on the 404 miRNAs under the condition of 5-fold cross validations and received an AUC value of 0.84606. Finally, we extensively inferred miRNA-disease associations for 100 new miRNAs and some interesting prediction results were validated by established databases.

Conclusions

The encouraging results demonstrated that our method could provide a biologically relevant prediction and interpretation of associations between miRNAs and diseases, which were of great usefulness when guiding biological experiments for scientific research.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2998-8) contains supplementary material, which is available to authorized users.

A Computational Bipartite Graph-Based Drug Repurposing Method

We present a bipartite graph-based approach to calculate drug pairwise similarity for identifying potential new indications of approved drugs. Both chemical and molecular features were used in drug similarity calculation. In this paper, we first extracted drug chemical structures and drug-target interactions. Second, we computed chemical structure similarity and drug- target profile similarity. Further, we constructed a bipartite graph model with known relationships between drugs and their target proteins. Finally, we weighted summing drug structure similarity with target profile similarity to derive drug pairwise similarity, so that we can predict potential indication of a drug from its similar drugs. In addition, we summarized some alternative strategies and variations follow-up to each section in the overall analysis.

miRDDCR: a miRNA-based method to comprehensively infer drug-disease causal relationships

Revealing the cause-and-effect mechanism behind drug-disease relationships remains a challenging task. Recent studies suggested that drugs can target microRNAs (miRNAs) and alter their expression levels. In the meanwhile, the inappropriate expression of miRNAs will lead to various diseases. Therefore, targeting specific miRNAs by small-molecule drugs to modulate their activities provides a promising approach to human disease treatment. However, few studies attempt to discover drug-disease causal relationships through the molecular level of miRNAs. Here, we developed a miRNA-based inference method miRDDCR to comprehensively predict drug-disease causal relationships. We first constructed a three-layer drug-miRNA-disease heterogeneous network by combining similarity measurements, existing drug-miRNA associations and miRNA-disease associations. Then, we extended the algorithm of Random Walk to the three-layer heterogeneous network and ranked the potential indications for drugs. Leave-one-out cross-validations and case studies demonstrated that our method miRDDCR can achieve excellent prediction power. Compared with related methods, our causality discovery-based algorithm showed superior prediction ability and highlighted the molecular basis miRNAs, which can be used to assist in the experimental design for drug development and disease treatment. Finally, comprehensively inferred drug-disease causal relationships were released for further studies.