Targeting microRNAs with small molecules: from dream to reality

Small molecules targeting microRNAs: new opportunities and challenges in precision cancer therapy

Noncoding RNAs, especially miRNAs, play a pivotal role in cancer initiation and metastasis, underscoring their susceptibility to precise modulation via small molecule inhibitors. This review examines the innovative strategy of targeting oncogenic miRNAs with small drug-like molecules, an approach that can reshape the cancer treatment landscape. We review the current understanding of the multifaceted roles of miRNAs in oncogenesis, highlighting emerging therapeutic paradigms that have the potential to expand cancer treatment options. As research on small molecule inhibitors of miRNA is still in its early stages, ongoing investigative efforts and the development of new technologies and chemical matter are essential to fulfill the significant potential of this innovative approach to cancer treatment.

MHESMMR: a multilevel model for predicting the regulation of miRNAs expression by small molecules

According to the expression of miRNA in pathological processes, miRNAs can be divided into oncogenes or tumor suppressors. Prediction of the regulation relations between miRNAs and small molecules (SMs) becomes a vital goal for miRNA-target therapy. But traditional biological approaches are laborious and expensive. Thus, there is an urgent need to develop a computational model. In this study, we proposed a computational model to predict whether the regulatory relationship between miRNAs and SMs is up-regulated or down-regulated. Specifically, we first use the Large-scale Information Network Embedding (LINE) algorithm to construct the node features from the self-similarity networks, then use the General Attributed Multiplex Heterogeneous Network Embedding (GATNE) algorithm to extract the topological information from the attribute network, and finally utilize the Light Gradient Boosting Machine (LightGBM) algorithm to predict the regulatory relationship between miRNAs and SMs. In the fivefold cross-validation experiment, the average accuracies of the proposed model on the SM2miR dataset reached 79.59% and 80.37% for up-regulation pairs and down-regulation pairs, respectively. In addition, we compared our model with another published model. Moreover, in the case study for 5-FU, 7 of 10 candidate miRNAs are confirmed by related literature. Therefore, we believe that our model can promote the research of miRNA-targeted therapy.

miRNA-Based Technologies in Cancer Therapy

The discovery of therapeutic miRNAs is one of the most exciting challenges for pharmaceutical companies. Since the first miRNA was discovered in 1993, our knowledge of miRNA biology has grown considerably. Many studies have demonstrated that miRNA expression is dysregulated in many diseases, making them appealing tools for novel therapeutic approaches. This review aims to discuss miRNA biogenesis and function, as well as highlight strategies for delivering miRNA agents, presenting viral, non-viral, and exosomic delivery as therapeutic approaches for different cancer types. We also consider the therapeutic role of microRNA-mediated drug repurposing in cancer therapy.

Neighborhood-based inference and restricted Boltzmann machine for small molecule-miRNA associations prediction

Background

A growing number of experiments have shown that microRNAs (miRNAs) can be used as target of small molecules (SMs) to regulate gene expression for treating diseases. Therefore, identifying SM-related miRNAs is helpful for the treatment of diseases in the domain of medical investigation.

Methods

This article presents a new computational model, called NIRBMSMMA (neighborhood-based inference (NI) and restricted Boltzmann machine (RBM)), which we developed to identify potential small molecule-miRNA associations (NIRBMSMMA). First, grounded on known SM-miRNAs associations, SM similarity and miRNA similarity, NI was used to predict score of an unknown SM-miRNA pair by reckoning the sum of known associations between neighbors of the SM (miRNA) and the miRNA (SM). Second, utilizing a two-layered generative stochastic artificial neural network, RBM was used to predict SM-miRNA association by learning potential probability distribution from known SM-miRNA associations. At last, an ensemble learning model was conducted to combine NI and RBM for identifying potential SM-miRNA associations.

Results

Furthermore, we conducted global leave one out cross validation (LOOCV), miRNA-fixed LOOCV, SM-fixed LOOCV and five-fold cross validation to assess performance of NIRBMSMMA based on three datasets. Results showed that NIRBMSMMA obtained areas under the curve (AUC) of 0.9912, 0.9875, 0.8376 and 0.9898 ± 0.0009 under global LOOCV, miRNA-fixed LOOCV, SM-fixed LOOCV and five-fold cross validation based on dataset 1, respectively. For dataset 2, the AUCs are 0.8645, 0.8720, 0.7066 and 0.8547 ± 0.0046 in turn. For dataset 3, the AUCs are 0.9884, 0.9802, 0.8239 and 0.9870 ± 0.0015 in turn. Also, we conducted case studies to further assess the predictive performance of NIRBMSMMA. These results illustrated the proposed model is a useful tool in predicting potential SM-miRNA associations.

Predicting potential small molecule-miRNA associations utilizing truncated schatten p-norm

MicroRNAs (miRNAs) have significant implications in diverse human diseases and have proven to be effectively targeted by small molecules (SMs) for therapeutic interventions. However, current SM-miRNA association prediction models do not adequately capture SM/miRNA similarity. Matrix completion is an effective method for association prediction, but existing models use nuclear norm instead of rank function, which has some drawbacks. Therefore, we proposed a new approach for predicting SM-miRNA associations by utilizing the truncated schatten p-norm (TSPN). First, the SM/miRNA similarity was preprocessed by incorporating the Gaussian interaction profile kernel similarity method. This identified more SM/miRNA similarities and significantly improved the SM-miRNA prediction accuracy. Next, we constructed a heterogeneous SM-miRNA network by combining biological information from three matrices and represented the network with its adjacency matrix. Finally, we constructed the prediction model by minimizing the truncated schatten p-norm of this adjacency matrix and we developed an efficient iterative algorithmic framework to solve the model. In this framework, we also used a weighted singular value shrinkage algorithm to avoid the problem of excessive singular value shrinkage. The truncated schatten p-norm approximates the rank function more closely than the nuclear norm, so the predictions are more accurate. We performed four different cross-validation experiments on two separate datasets, and TSPN outperformed various most advanced methods. In addition, public literature confirms a large number of predictive associations of TSPN in four case studies. Therefore, TSPN is a reliable model for SM-miRNA association prediction.

Small molecule-mediated targeting of microRNAs for drug discovery: Experiments, computational techniques, and disease implications

Small molecules have been providing medical breakthroughs for human diseases for more than a century. Recently, identifying small molecule inhibitors that target microRNAs (miRNAs) has gained importance, despite the challenges posed by labour-intensive screening experiments and the significant efforts required for medicinal chemistry optimization. Numerous experimentally-verified cases have demonstrated the potential of miRNA-targeted small molecule inhibitors for disease treatment. This new approach is grounded in their posttranscriptional regulation of the expression of disease-associated genes. Reversing dysregulated gene expression using this mechanism may help control dysfunctional pathways. Furthermore, the ongoing improvement of algorithms has allowed for the integration of computational strategies built on top of laboratory-based data, facilitating a more precise and rational design and discovery of lead compounds. To complement the use of extensive pharmacogenomics data in prioritising potential drugs, our previous work introduced a computational approach based on only molecular sequences. Moreover, various computational tools for predicting molecular interactions in biological networks using similarity-based inference techniques have been accumulated in established studies. However, there are a limited number of comprehensive reviews covering both computational and experimental drug discovery processes. In this review, we outline a cohesive overview of both biological and computational applications in miRNA-targeted drug discovery, along with their disease implications and clinical significance. Finally, utilizing drug-target interaction (DTIs) data from DrugBank, we showcase the effectiveness of deep learning for obtaining the physicochemical characterization of DTIs.

AMCSMMA: Predicting Small Molecule-miRNA Potential Associations Based on Accurate Matrix Completion

Exploring potential associations between small molecule drugs (SMs) and microRNAs (miRNAs) is significant for drug development and disease treatment. Since biological experiments are expensive and time-consuming, we propose a computational model based on accurate matrix completion for predicting potential SM-miRNA associations (AMCSMMA). Initially, a heterogeneous SM-miRNA network is constructed, and its adjacency matrix is taken as the target matrix. An optimization framework is then proposed to recover the target matrix with the missing values by minimizing its truncated nuclear norm, an accurate, robust, and efficient approximation to the rank function. Finally, we design an effective two-step iterative algorithm to solve the optimization problem and obtain the prediction scores. After determining the optimal parameters, we conduct four kinds of cross-validation experiments based on two datasets, and the results demonstrate that AMCSMMA is superior to the state-of-the-art methods. In addition, we implement another validation experiment, in which more evaluation metrics in addition to the AUC are introduced and finally achieve great results. In two types of case studies, a large number of SM-miRNA pairs with high predictive scores are confirmed by the published experimental literature. In summary, AMCSMMA has superior performance in predicting potential SM-miRNA associations, which can provide guidance for biological experiments and accelerate the discovery of new SM-miRNA associations.

DeepsmirUD: Prediction of Regulatory Effects on microRNA Expression Mediated by Small Molecules Using Deep Learning

Aberrant miRNA expression has been associated with a large number of human diseases. Therefore, targeting miRNAs to regulate their expression levels has become an important therapy against diseases that stem from the dysfunction of pathways regulated by miRNAs. In recent years, small molecules have demonstrated enormous potential as drugs to regulate miRNA expression (i.e., SM-miR). A clear understanding of the mechanism of action of small molecules on the upregulation and downregulation of miRNA expression allows precise diagnosis and treatment of oncogenic pathways. However, outside of a slow and costly process of experimental determination, computational strategies to assist this on an ad hoc basis have yet to be formulated. In this work, we developed, to the best of our knowledge, the first cross-platform prediction tool, DeepsmirUD, to infer small-molecule-mediated regulatory effects on miRNA expression (i.e., upregulation or downregulation). This method is powered by 12 cutting-edge deep-learning frameworks and achieved AUC values of 0.843/0.984 and AUCPR values of 0.866/0.992 on two independent test datasets. With a complementarily constructed network inference approach based on similarity, we report a significantly improved accuracy of 0.813 in determining the regulatory effects of nearly 650 associated SM-miR relations, each formed with either novel small molecule or novel miRNA. By further integrating miRNA-cancer relationships, we established a database of potential pharmaceutical drugs from 1343 small molecules for 107 cancer diseases to understand the drug mechanisms of action and offer novel insight into drug repositioning. Furthermore, we have employed DeepsmirUD to predict the regulatory effects of a large number of high-confidence associated SM-miR relations. Taken together, our method shows promise to accelerate the development of potential miRNA targets and small molecule drugs.

Transcriptional Inhibition of MicroRNA miR-122 by Small Molecules Reduces Hepatitis C Virus Replication in Liver Cells

MicroRNAs (miRNAs) are noncoding RNA molecules of 22-24 nucleotides that are estimated to regulate thousands of genes in humans, and their dysregulation has been implicated in many diseases. MicroRNA-122 (miR-122) is the most abundant miRNA in the liver and has been linked to the development of hepatocellular carcinoma and hepatitis C virus (HCV) infection. Its role in these diseases renders miR-122 a potential target for small-molecule therapeutics. Here, we report the discovery of a new sulfonamide class of small-molecule miR-122 inhibitors from a high-throughput screen using a luciferase-based reporter assay. Structure-activity relationship (SAR) studies and secondary assays led to the development of potent and selective miR-122 inhibitors. Preliminary mechanism-of-action studies suggest a role in the promoter-specific transcriptional inhibition of miR-122 expression through direct binding to the liver-enriched transcription factor hepatocyte nuclear factor 4α. Importantly, the developed inhibitors significantly reduce HCV replication in human liver cells.

Glucocorticoid-induced microRNA-378 signaling mediates the progression of pancreatic cancer by enhancing autophagy

Glucocorticoids (GCs) are widely used in tumor therapy to reduce tumor growth, inflammation, edema, and other side effects. Controversially, GCs may also cause the progression of highly aggressive pancreatic ductal adenocarcinoma (PDAC). Because microRNA (miR) and autophagy signaling support the invasive growth of PDAC, we asked whether these mechanisms may be targeted by GCs. Six established human PDAC cell lines, tissue from patients who received GC medication (n = 35) prior to surgery, or not (n = 35), and tumor xenografts were examined by RT‒qPCR, transmission electron microscopy (TEM), monodansylcadaverine (MDC) staining, immunohistochemistry, in situ hybridization, gene array and Kaplan‒Meier analysis with bioinformatics, and MTT, western blot, colony, spheroid, migration, and invasion assays. We found that various GCs, including dexamethasone (DEX), induced typical features of macroautophagy with the appearance of autolysosomes, enhanced LC3-II, decreased SQSTM1/p62 expression and induced epithelial-mesenchymal transition (EMT) and gemcitabine resistance. The GC receptor (GR) antagonist mifepristone (RU486) counteracted DEX-induced autophagy features, suggesting that the GC-GR complex is involved in the induction of autophagy. The autophagy-related miR-378i and miR-378a-3p were selected as the top upregulated candidates, and their high expression in PDAC patient tissue correlated with low survival. siRNA-mediated downregulation of miR-378 inhibited DEX-induced autophagy, and tumor progression. Bioinformatics confirmed the contribution of miR-378 to the regulation of signaling networks involved in GC-induced autophagy and tumor progression. The construction of a molecular docking model revealed stable binding of miR-378 to the DEX-GR complex, suggesting direct regulation. These substantial, novel, in-depth data reveal that GCs favor autophagy-mediated cancer progression by inducing miR-378 and GR binding and implicate GR and miR-378 as new therapeutic targets.

Reciprocal interplays between MicroRNAs and pluripotency transcription factors in dictating stemness features in human cancers

The interplay between microRNAs (miRNAs) and pluripotency transcription factors (TFs) orchestrates the acquisition of cancer stem cell (CSC) features during the course of malignant transformation, rendering them essential cancer cell dependencies and therapeutic vulnerabilities. In this review, we discuss emerging themes in tumor heterogeneity, including the clonal evolution and the CSC models and their implications in resistance to cancer therapies, and then provide thorough coverage on the roles played by key TFs in maintaining normal and malignant stem cell pluripotency and plasticity. In addition, we discuss the reciprocal interactions between miRNAs and MYC, OCT4, NANOG, SOX2, and KLF4 pluripotency TFs and their contributions to tumorigenesis. We provide our view on the potential to interfere with key miRNA-TF networks through the use of RNA-based therapeutics as single agents or in combination with other therapeutic strategies, to abrogate the CSC state and render tumor cells more responsive to standard and targeted therapies.

DAESTB: inferring associations of small molecule-miRNA via a scalable tree boosting model based on deep autoencoder

MicroRNAs (miRNAs) are closely related to a variety of human diseases, not only regulating gene expression, but also having an important role in human life activities and being viable targets of small molecule drugs for disease treatment. Current computational techniques to predict the potential associations between small molecule and miRNA are not that accurate. Here, we proposed a new computational method based on a deep autoencoder and a scalable tree boosting model (DAESTB), to predict associations between small molecule and miRNA. First, we constructed a high-dimensional feature matrix by integrating small molecule-small molecule similarity, miRNA-miRNA similarity and known small molecule-miRNA associations. Second, we reduced feature dimensionality on the integrated matrix using a deep autoencoder to obtain the potential feature representation of each small molecule-miRNA pair. Finally, a scalable tree boosting model is used to predict small molecule and miRNA potential associations. The experiments on two datasets demonstrated the superiority of DAESTB over various state-of-the-art methods. DAESTB achieved the best AUC value. Furthermore, in three case studies, a large number of predicted associations by DAESTB are confirmed with the public accessed literature. We envision that DAESTB could serve as a useful biological model for predicting potential small molecule-miRNA associations.

A Slow Dynamic RNA Switch Regulates Processing of microRNA-21

The microRNAs are non-coding RNAs which post-transcriptionally regulate the expression of many eukaryotic genes, and whose dysregulation is a driver of human disease. Here we report the discovery of a very slow (0.1 s-1) conformational rearrangement at the Dicer cleavage site of pre-miR-21, which regulates the relative concentration of readily- and inefficiently-processed RNA structural states. We show that this dynamic switch is affected by single nucleotide mutations and can be biased by small molecule and peptide ligands, which can direct the microRNA to occupy the inefficiently processed state and reduce processing efficiency. This result reveals a new mechanism of RNA regulation and suggests a chemical approach to suppressing or activating pathogenic microRNAs by selective stabilization of their unprocessed or processed states.

MicroRNA as a Potential Therapeutic Molecule in Cancer

Small noncoding RNAs, as post-translational regulators of many target genes, are not only markers of neoplastic disease initiation and progression, but also markers of response to anticancer therapy. Hundreds of miRNAs have been identified as biomarkers of drug resistance, and many have demonstrated the potential to sensitize cancer cells to therapy. Their properties of modulating the response of cells to therapy have made them a promising target for overcoming drug resistance. Several methods have been developed for the delivery of miRNAs to cancer cells, including introducing synthetic miRNA mimics, DNA plasmids containing miRNAs, and small molecules that epigenetically alter endogenous miRNA expression. The results of studies in animal models and preclinical studies for solid cancers and hematological malignancies have confirmed the effectiveness of treatment protocols using microRNA. Nevertheless, the use of miRNAs in anticancer therapy is not without limitations, including the development of a stable nanoconstruct, delivery method choices, and biodistribution. The aim of this review was to summarize the role of miRNAs in cancer treatment and to present new therapeutic concepts for these molecules. Supporting anticancer therapy with microRNA molecules has been verified in numerous clinical trials, which shows great potential in the treatment of cancer.

In silico drug repositioning based on integrated drug targets and canonical correlation analysis

Background

Besides binding to proteins, the most recent advances in pharmacogenomics indicate drugs can regulate the expression of non-coding RNAs (ncRNAs). The polypharmacological feature in drugs enables us to find new uses for existing drugs (namely drug repositioning). However, current computational methods for drug repositioning mainly consider proteins as drug targets. Meanwhile, these methods identify only statistical relationships between drugs and diseases. They provide little information about how drug-disease associations are formed at the molecular target level.

Methods

Herein, we first comprehensively collect proteins and two categories of ncRNAs as drug targets from public databases to construct drug–target interactions. Experimentally confirmed drug-disease associations are downloaded from an established database. A canonical correlation analysis (CCA) based method is then applied to the two datasets to extract correlated sets of targets and diseases. The correlated sets are regarded as canonical components, and they are used to investigate drug’s mechanism of actions. We finally develop a strategy to predict novel drug-disease associations for drug repositioning by combining all the extracted correlated sets.

Results

We receive 400 canonical components which correlate targets with diseases in our study. We select 4 components for analysis and find some top-ranking diseases in an extracted set might be treated by drugs interfacing with the top-ranking targets in the same set. Experimental results from 10-fold cross-validations show integrating different categories of target information results in better prediction performance than only using proteins or ncRNAs as targets. When compared with 3 state-of-the-art approaches, our method receives the highest AUC value 0.8576. We use our method to predict new indications for 789 drugs and confirm 24 predictions in the top 1 predictions.

Conclusions

To the best of our knowledge, this is the first computational effort which combines both proteins and ncRNAs as drug targets for drug repositioning. Our study provides a biologically relevant interpretation regarding the forming of drug-disease associations, which is useful for guiding future biomedical tests.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-022-01203-1.

Dual-Network Collaborative Matrix Factorization for predicting small molecule-miRNA associations

MicroRNAs (miRNAs) play crucial roles in multiple biological processes and human diseases and can be considered as therapeutic targets of small molecules (SMs). Because biological experiments used to verify SM-miRNA associations are time-consuming and expensive, it is urgent to propose new computational models to predict new SM-miRNA associations. Here, we proposed a novel method called Dual-network Collaborative Matrix Factorization (DCMF) for predicting the potential SM-miRNA associations. Firstly, we utilized the Weighted K Nearest Known Neighbors (WKNKN) method to preprocess SM-miRNA association matrix. Then, we constructed matrix factorization model to obtain two feature matrices containing latent features of SM and miRNA, respectively. Finally, the predicted SM-miRNA association score matrix was obtained by calculating the inner product of two feature matrices. The main innovations of this method were that the use of WKNKN method can preprocess the missing values of association matrix and the introduction of dual network can integrate more diverse similarity information into DCMF. For evaluating the validity of DCMF, we implemented four different cross validations (CVs) based on two distinct datasets and two different case studies. Finally, based on dataset 1 (dataset 2), DCMF achieved Area Under receiver operating characteristic Curves (AUC) of 0.9868 (0.8770), 0.9833 (0.8836), 0.8377 (0.7591) and 0.9836 ± 0.0030 (0.8632 ± 0.0042) in global Leave-One-Out Cross Validation (LOOCV), miRNA-fixed local LOOCV, SM-fixed local LOOCV and 5-fold CV, respectively. For case studies, plenty of predicted associations have been confirmed by published experimental literature. Therefore, DCMF is an effective tool to predict potential SM-miRNA associations.

Ensemble of kernel ridge regression-based small molecule-miRNA association prediction in human disease

MicroRNAs (miRNAs) play crucial roles in human disease and can be targeted by small molecule (SM) drugs according to numerous studies, which shows that identifying SM-miRNA associations in human disease is important for drug development and disease treatment. We proposed the method of Ensemble of Kernel Ridge Regression-based Small Molecule-MiRNA Association prediction (EKRRSMMA) to uncover potential SM-miRNA associations by combing feature dimensionality reduction and ensemble learning. First, we constructed different feature subsets for both SMs and miRNAs. Then, we trained homogeneous base learners based on distinct feature subsets and took the average of scores obtained from these base learners as SM-miRNA association score. In EKRRSMMA, feature dimensionality reduction technology was employed in the process of construction of feature subsets to reduce the influence of noisy data. Besides, the base learner, namely KRR_avg, was the combination of two classifiers constructed under SM space and miRNA space, which could make full use of the information of SM and miRNA. To assess the prediction performance of EKRRSMMA, we conducted Leave-One-Out Cross-Validation (LOOCV), SM-fixed local LOOCV, miRNA-fixed local LOOCV and 5-fold CV based on two datasets. For Dataset 1 (Dataset 2), EKRRSMMA got the Area Under receiver operating characteristic Curves (AUCs) of 0.9793 (0.8871), 0.8071 (0.7705), 0.9732 (0.8586) and 0.9767 ± 0.0014 (0.8560 ± 0.0027). Besides, we conducted four case studies. As a result, 32 (5-Fluorouracil), 19 (17β-Estradiol), 26 (5-Aza-2'-deoxycytidine) and 11 (cyclophosphamide) out of top 50 predicted potentially associated miRNAs were confirmed by database or experimental literature. Above evaluation results demonstrated that EKRRSMMA is reliable for predicting SM-miRNA associations.

Small-Molecule Inhibitors Overcome Epigenetic Reprogramming for Cancer Therapy

Cancer treatment is a significant challenge for the global health system, although various pharmacological and therapeutic discoveries have been made. It has been widely established that cancer is associated with epigenetic modification, which is reversible and becomes an attractive target for drug development. Adding chemical groups to the DNA backbone and modifying histone proteins impart distinct characteristics on chromatin architecture. This process is mediated by various enzymes modifying chromatin structures to achieve the diversity of epigenetic space and the intricacy in gene expression files. After decades of effort, epigenetic modification has represented the hallmarks of different cancer types, and the enzymes involved in this process have provided novel targets for antitumor therapy development. Epigenetic drugs show significant effects on both preclinical and clinical studies in which the target development and research offer a promising direction for cancer therapy. Here, we summarize the different types of epigenetic enzymes which target corresponding protein domains, emphasize DNA methylation, histone modifications, and microRNA-mediated cooperation with epigenetic modification, and highlight recent achievements in developing targets for epigenetic inhibitor therapy. This article reviews current anticancer small-molecule inhibitors targeting epigenetic modified enzymes and displays their performances in different stages of clinical trials. Future studies are further needed to address their off-target effects and cytotoxicity to improve their clinical translation.

Predicting potential small molecule-miRNA associations based on bounded nuclear norm regularization

Mounting evidence has demonstrated the significance of taking microRNAs (miRNAs) as the target of small molecule (SM) drugs for disease treatment. Given the fact that exploring new SM-miRNA associations through biological experiments is extremely expensive, several computing models have been constructed to reveal the possible SM-miRNA associations. Here, we built a computing model of Bounded Nuclear Norm Regularization for SM-miRNA Associations prediction (BNNRSMMA). Specifically, we first constructed a heterogeneous SM-miRNA network utilizing miRNA similarity, SM similarity, confirmed SM-miRNA associations and defined a matrix to represent the heterogeneous network. Then, we constructed a model to complete this matrix by minimizing its nuclear norm. The Alternating Direction Method of Multipliers was adopted to minimize the nuclear norm and obtain predicted scores. The main innovation lies in two aspects. During completion, we limited all elements of the matrix within the interval of (0,1) to make sure they have practical significance. Besides, instead of strictly fitting all known elements, a regularization term was incorporated to tolerate the noise in integrated similarities. Furthermore, four kinds of cross-validations on two datasets and two types of case studies were performed to evaluate the predictive performance of BNNRSMMA. Finally, BNNRSMMA attained areas under the curve of 0.9822 (0.8433), 0.9793 (0.8852), 0.8253 (0.7350) and 0.9758 ± 0.0029 (0.8759 ± 0.0041) under global leave-one-out cross-validation (LOOCV), miRNA-fixed LOOCV, SM-fixed LOOCV and 5-fold cross-validation based on Dataset 1(Dataset 2), respectively. With regard to case studies, plenty of predicted associations have been verified by experimental literatures. All these results confirmed that BNNRSMMA is a reliable tool for inferring associations.

Noncoding RNA therapeutics - challenges and potential solutions

Therapeutic targeting of noncoding RNAs (ncRNAs), such as microRNAs (miRNAs) and long noncoding RNAs (lncRNAs), represents an attractive approach for the treatment of cancers, as well as many other diseases. Over the past decade, substantial effort has been made towards the clinical application of RNA-based therapeutics, employing mostly antisense oligonucleotides and small interfering RNAs, with several gaining FDA approval. However, trial results have so far been ambivalent, with some studies reporting potent effects whereas others demonstrated limited efficacy or toxicity. Alternative entities such as antimiRNAs are undergoing clinical testing, and lncRNA-based therapeutics are gaining interest. In this Perspective, we discuss key challenges facing ncRNA therapeutics - including issues associated with specificity, delivery and tolerability - and focus on promising emerging approaches that aim to boost their success.

Novel Potential Small Molecule-MiRNA-Cancer Associations Prediction Model Based on Fingerprint, Sequence, and Clinical Symptoms

As an important biomarker in organisms, miRNA is closely related to various small molecules and diseases. Research on small molecule-miRNA-cancer associations is helpful for the development of cancer treatment drugs and the discovery of pathogenesis. It is very urgent to develop theoretical methods for identifying potential small molecular-miRNA-cancer associations, because experimental approaches are usually time-consuming, laborious, and expensive. To overcome this problem, we developed a new computational method, in which features derived from structure, sequence, and symptoms were utilized to characterize small molecule, miRNA, and cancer, respectively. A feature vector was construct to characterize small molecule-miRNA-cancer association by concatenating these features, and a random forest algorithm was utilized to construct a model for recognizing potential association. Based on the 5-fold cross-validation and benchmark data set, the model achieved an accuracy of 93.20 ± 0.52%, a precision of 93.22 ± 0.51%, a recall of 93.20 ± 0.53%, and an F1-measure of 93.20 ± 0.52%. The areas under the receiver operating characteristic curve and precision recall curve were 0.9873 and 0.9870. The real prediction ability and application performance of the developed method have also been further evaluated and verified through an independent data set test and case study. Some potential small molecules and miRNAs related to cancer have been identified and are worthy of further experimental research. It is anticipated that our model could be regarded as a useful high-throughput virtual screening tool for drug research and development. All source codes can be downloaded from https://github.com/LeeKamlong/Multi-class-SMMCA.

The possible effect of microRNA-155 (miR-155) and BACE1 inhibitors in the memory of patients with down syndrome and Alzheimer's disease: Design, synthesis, virtual screening, molecular modeling and biological evaluations

MiR-155 plays main roles in several physiological and pathological mechanisms, such as Down syndrome (DS), immunity and inflammation and potential anti-AD therapeutic target. The miR-155 is one of the overexpressed miRNAs in DS patients that contribute directly and indirectly to the onset or progression of the DS. Since the miR-155 can simultaneously reduce the translation of several genes at post-transcriptional levels, targeting the miR-155 might set the stage for the treatment of DS. One of the rational strategies in providing therapeutic interventions in this respect is to design and develop novel small molecules inhibiting the miR-155 function or biogenesis or maturation. In the present study, we aim to introduce small molecule compounds with the potential to inhibit the generation of the selectively miR-155 processing by employing computational drug design approaches, as well as in vitro studies. We designed and synthesized a novel series of imidazo[1,2-a]pyridines derivatives as new nonpeptic candidates for the treatment of DS with AD. The designed compounds were investigated for their BACE1 and miR-155 binder inhibitory potential in vitro and in cell. In addition, we present a systematic computational approach that includes 3 D modeling, docking-based virtual screening, and molecular dynamics simulation to identify Small - molecule inhibitors of pre-miR-155 maturation. To confirm the inhibitory potential of compound 8k on miR-155 maturation, qRT- PCR was performed. All our results confirm that compound 8k, in addition to being a good inhibitor of BACE1, can also be a good inhibitor of miR-155.Communicated by Ramaswamy H. Sarma.

Identification of miRNAs as the Crosstalk in the Interaction between Neural Stem/Progenitor Cells and Endothelial Cells

AIM

This study is aimed at identifying genetic and epigenetic crosstalk molecules and their target drugs involved in the interaction between neural stem/progenitor cells (NSPCs) and endothelial cells (ECs).

MATERIALS AND METHODS

Datasets pertaining to reciprocal mRNA and noncoding RNA changes induced by the interaction between NSPCs and ECs were obtained from the GEO database. Differential expression analysis (DEA) was applied to identify NSPC-induced EC alterations by comparing the expression profiles between monoculture of ECs and ECs grown in EC/NSPC cocultures. DEA was also utilized to identify EC-induced NSPC alterations by comparing the expression profiles between monoculture of NSPCs and NSPCs grown in EC/NSPC cocultures. The DEGs and DEmiRNAs shared by NSPC-induced EC alterations and EC-induced NSPC alterations were then identified. Furthermore, miRNA crosstalk analysis and functional enrichment analysis were performed, and the relationship between DEmiRNAs and small molecular drug targets/environment chemical compounds was investigated.

RESULTS

One dataset (GSE29759) was included and analyzed in this study. Six genes (i.e., MMP14, TIMP3, LOXL1, CCK, SMAD6, and HSPA2), three miRNAs (i.e., miR-210, miR-230a, and miR-23b), and three pathways (i.e., Akt, ERK1/2, and BMPs) were identified as crosstalk molecules. Six small molecular drugs (i.e., deptropine, fluphenazine, lycorine, quinostatin, resveratrol, and thiamazole) and seven environmental chemical compounds (i.e., folic acid, dexamethasone, choline, doxorubicin, thalidomide, bisphenol A, and titanium dioxide) were identified to be potential target drugs of the identified DEmiRNAs.

CONCLUSION

To conclude, three miRNAs (i.e., miR-210, miR-230a, and miR-23b) were identified to be crosstalks linking the interaction between ECs and NSPCs by implicating in both angiogenesis and neurogenesis. These crosstalk molecules might provide a basis for devising novel strategies for fabricating neurovascular models in stem cell tissue engineering.

Emerging roles for noncoding RNAs in female sex steroids and reproductive disease

The "central dogma" of molecular biology, that is, that DNA blueprints encode messenger RNAs which are destined for translation into protein, has been challenged in recent decades. In actuality, a significant portion of the genome encodes transcripts that are transcribed into functional RNA. These noncoding RNAs (ncRNAs), which are not transcribed into protein, play critical roles in a wide variety of biological processes. A growing body of evidence derived from mouse models and human data demonstrates that ncRNAs are dysregulated in various reproductive pathologies, and that their expression is essential for female gametogenesis and fertility. Yet in many instances it is unclear how dysregulation of ncRNA expression leads to a disease process. In this review, we highlight new observations regarding the roles of ncRNAs in the pathogenesis of disordered female steroid hormone production and disease, with an emphasis on long noncoding RNAs (lncRNAs) and microRNAs (miRNAs). We will focus our discussion in the context of three ovarian disorders which are characterized in part by altered steroid hormone biology - diminished ovarian reserve, premature ovarian insufficiency, and polycystic ovary syndrome. We will also discuss the limitations and challenges faced in studying noncoding RNAs and sex steroid hormone production. An enhanced understanding of the role of ncRNAs in sex hormone regulatory networks is essential in order to advance the development of potential diagnostic markers and therapeutic targets for diseases, including those in reproductive health. Our deepened understanding of ncRNAs has the potential to uncover new applications and therapies; however, in many cases, the next steps will involve distinguishing critical ncRNAs from those which are merely changing in response to a particular disease state, or which are altogether unrelated to disease pathophysiology.

Altered microRNAs expression levels of sperm and seminal plasma in patients with infertile ejaculates compared with normozoospermic males

Approximately 15% of couples are unable to conceive after one year of unprotected intercourse. Because sperm can be accessed with ease, it is reasonable to search for non-invasive biomarkers in semen. MicroRNAs are a family of short single-stranded non-coding RNA molecules that are capable of regulating gene expression and causing mRNA degradation. We studied the most common 11 spermatogenesis-related microRNAs expression levels in sperm and seminal plasma from patients with oligozoospermic or asthenozoospermic ejaculates, and in men with normozoospermic ejaculates. Five of these miRNAs were significantly upregulated and three were downregulated in infertile males compared to men with normozoospermic ejaculates. A statistically significant negative correlation was found between the sperm concentration and several microRNA expression level (let-7a, miR-7-1-3p, miR-141, miR-200a, and miR-429, p < 0.0001) both in sperm and in seminal plasma. We also found positive correlation between sperm concentration and some miRNA expression levels (miR-15b, miR-34b, and miR-122, p < 0.001) in sperm and in seminal plasma. This is the first study to demonstrate differences between sperm and seminal plasma miRNA expression level and to identify a correlation between the sperm concentration and miRNAs expression level. Therefore, these MiRNAs could have the potential be used as non-invasive biomarkers to diagnose males with impaired sperm production.

Modulating microRNA Processing: Enoxacin, the Progenitor of a New Class of Drugs

The RNA interference (RNAi) process encompasses the cellular mechanisms by which short-noncoding RNAs posttranscriptionally modulate gene expression. First discovered in 1998, today RNAi represents the foundation underlying complex biological mechanisms that are dysregulated in many diseases. MicroRNAs are effector molecules of gene silencing in RNAi, and their modulation can lead to a wide response in cells. Enoxacin was reported as the first and unique small-molecule enhancer of microRNA (SMER) maturation. Herein, the biological activity of enoxacin as SMER is discussed to shed light on its innovative mode of action, its potential in treating different diseases, and the feasibility of using enoxacin as a chemical template for inspiring medicinal chemists. We debate its mechanism of action at the molecular level and the possible impact on future ligand and/or structure-guided chemical optimizations, as well as opportunities and drawbacks associated with the development of quinolones such as SMERs.

MicroRNAs (miRNAs) and Long Non-Coding RNAs (lncRNAs) as New Tools for Cancer Therapy: First Steps from Bench to Bedside

Non-coding RNAs represent a significant proportion of the human genome. After having been considered as 'junk' for a long time, non-coding RNAs are now well established as playing important roles in maintaining cellular homeostasis and functions. Some non-coding RNAs show cell- and tissue-specific expression patterns and are specifically deregulated under pathological conditions (e.g. cancer). Therefore, non-coding RNAs have been extensively studied as potential biomarkers in the context of different diseases with a focus on microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) for several years. Since their discovery, miRNAs have attracted more attention than lncRNAs in research studies; however, both families of non-coding RNAs have been established to play an important role in gene expression control, either as transcriptional or post-transcriptional regulators. Both miRNAs and lncRNAs can regulate key genes involved in the development of cancer, thus influencing tumour growth, invasion, and metastasis by increasing the activation of oncogenic pathways and limiting the expression of tumour suppressors. Furthermore, miRNAs and lncRNAs are also emerging as important mediators in drug-sensitivity and drug-resistance mechanisms. In the light of these premises, a number of pre-clinical and early clinical studies are exploring the potential of non-coding RNAs as new therapeutics. The aim of this review is to summarise the latest knowledge of the use of miRNAs and lncRNAs as therapeutic tools for cancer treatment.

Identifying Small Molecule-miRNA Associations Based on Credible Negative Sample Selection and Random Walk

Recently, many studies have demonstrated that microRNAs (miRNAs) are new small molecule drug targets. Identifying small molecule-miRNA associations (SMiRs) plays an important role in finding new clues for various human disease therapy. Wet experiments can discover credible SMiR associations; however, this is a costly and time-consuming process. Computational models have therefore been developed to uncover possible SMiR associations. In this study, we designed a new SMiR association prediction model, RWNS. RWNS integrates various biological information, credible negative sample selections, and random walk on a triple-layer heterogeneous network into a unified framework. It includes three procedures: similarity computation, negative sample selection, and SMiR association prediction based on random walk on the constructed small molecule-disease-miRNA association network. To evaluate the performance of RWNS, we used leave-one-out cross-validation (LOOCV) and 5-fold cross validation to compare RWNS with two state-of-the-art SMiR association methods, namely, TLHNSMMA and SMiR-NBI. Experimental results showed that RWNS obtained an AUC value of 0.9829 under LOOCV and 0.9916 under 5-fold cross validation on the SM2miR1 dataset, and it obtained an AUC value of 0.8938 under LOOCV and 0.9899 under 5-fold cross validation on the SM2miR2 dataset. More importantly, RWNS successfully captured 9, 17, and 37 SMiR associations validated by experiments among the predicted top 10, 20, and 50 SMiR candidates with the highest scores, respectively. We inferred that enoxacin and decitabine are associated with mir-21 and mir-155, respectively. Therefore, RWNS can be a powerful tool for SMiR association prediction.

Identification of microRNA‑363‑3p as an essential regulator of chondrocyte apoptosis in osteoarthritis by targeting NRF1 through the p53‑signaling pathway

Osteoarthritis (OA) is a degenerative joint disease that affects the physical, and mental health of middle-aged and elderly people. The aims of the present study were to determine the biological function and molecular mechanisms of miR-363-3p in chondrocyte apoptosis. Exploration of the molecular mechanisms of OA may be helpful in the understand of the causes, and facilitating the prevention and treatment of OA. In the present study, the expression of nuclear respiratory factor1 (NRF1) was downregulated in the articular cartilage of OA rats in vivo and lipopolysaccharide (LPS)-treated chondrocytes in vitro. MicroRNAs (miRNA) are regulators of gene expression in the progression of OA. TargetScan software was used to predict that NRF1 was a potential target for miRNA (miR)-363, and this was confirmed in subsequent experiments. The expression of miR-363-3p was negatively correlated with the expression of NRF1, and its expression was significantly upregulated in OA model rats and in LPS-induced chondrocytes compared with the expression in the respective controls. In addition, the overexpression of miR-363-3p increased the levels of interleukin (IL)-1β, IL-6 and tumor necrosis factor-α in vivo, and was demonstrated to promote chondrocyte injury and apoptosis by Safranin O staining and TUNEL. Moreover, the inhibition of miR-363-3p expression increased the expression of NRF1 and protected chondrocytes from apoptosis in vitro and in vivo, whereas the overexpression of miR-363-3p downregulated NRF1 expression and promoted LPS-induced chondrocyte apoptosis through the p53 pathway in vitro. The results of this study suggested that miR-363-3p-mediated inhibition of NRF1may be associated with chondrocyte apoptosis in OA.

Advances in the discovery of microRNA-based anticancer therapeutics: latest tools and developments

Introduction: MicroRNAs (miRNAs) are small endogenous non-coding RNAs that repress the expression of their target genes by reducing mRNA stability and/or inhibiting translation. miRNAs are known to be aberrantly regulated in cancers. Modulators of miRNA (mimics and antagonists) have emerged as novel therapeutic tools for cancer treatment.Areas covered: This review summarizes the various strategies that have been applied to correct the dysregulated miRNA in cancer cells. The authors also discuss the recent advances in the technical development and preclinical/clinical evaluation of miRNA-based therapeutic agents.Expert opinion: Application of miRNA-based therapeutics for cancer treatment is appealing because they are able to modulate multiple dysregulated genes and/or signaling pathways in cancer cells. Major obstacles hindering their clinical development include drug delivery, off-target effects, efficacious dose determination, and safety. Tumor site-specific delivery of novel miRNA therapeutics may help to minimize off-target effects and toxicity. Combination of miRNA therapeutics with other anticancer treatment modalities could provide a synergistic effect, thus allowing the use of lower dose, minimizing off-target effects, and improving the overall safety profile in cancer patients. It is critical to identify individual miRNAs with cancer type-specific and context-specific regulation of oncogenes and tumor-suppressor genes in order to facilitate the precise use of miRNA anticancer therapeutics.

Decrypting noncoding RNA interactions, structures, and functional networks

The world of noncoding RNAs (ncRNAs) is composed of an enormous and growing number of transcripts, ranging in length from tens of bases to tens of kilobases, involved in all biological processes and altered in expression and/or function in many types of human disorders. The premise of this review is the concept that ncRNAs, like many large proteins, have a multidomain architecture that organizes them spatially and functionally. As ncRNAs are beginning to be imprecisely classified into functional families, we review here how their structural properties might inform their functions with focus on structural architecture–function relationships. We will describe the properties of “interactor elements” (IEs) involved in direct physical interaction with nucleic acids, proteins, or lipids and of “structural elements” (SEs) directing their wiring within the “ncRNA interactor networks” through the emergence of secondary and/or tertiary structures. We suggest that spectrums of “letters” (ncRNA elements) are assembled into “words” (ncRNA domains) that are further organized into “phrases” (complete ncRNA structures) with functional meaning (signaling output) through complex “sentences” (the ncRNA interactor networks). This semiotic analogy can guide the exploitation of ncRNAs as new therapeutic targets through the development of IE-blockers and/or SE-lockers that will change the interactor partners’ spectrum of proteins, RNAs, DNAs, or lipids and consequently influence disease phenotypes.

Small molecules with big roles in microRNA chemical biology and microRNA-targeted therapeutics

MicroRNAs (miRNAs) are small, non-coding RNAs that post-transcriptionally regulate gene expression. Aberrant miRNA expression or function have close links with various human diseases. Therefore, therapeutic treatments with disease-associated miRNAs as targets are emerging. However, the intracellular miRNA networks are extremely complicated and poorly understood, which thus hinder the development of miRNA-targeted therapeutics. Small molecules that are able to regulate endogenous miRNAs hold great potential in both elucidation of miRNA networks and treatment of miRNA-related diseases. Herein, we summarize current strategies for discovery of small molecule modifiers of miRNAs, and we highlight aspects of miRNA cellular biology elucidated by using these small molecules and miRNA-targeted therapeutics realized by these small molecules. We envision that this area will expand dramatically in the near future and will ultimately contribute to a better understanding of miRNA-involved cellular processes and development of therapeutic agents for miRNA-associated diseases.

Identification of cancer prognosis-associated lncRNAs based on the miRNA-TF co-regulatory motifs and dosage sensitivity

By integrating dosage sensitivity and motif regulation data, we established a framework and identified a total of 33 cancer prognosis-associated lncRNAs.

In Silico Prediction of Small Molecule-miRNA Associations Based on the HeteSim Algorithm

Targeting microRNAs (miRNAs) with drug small molecules (SMs) is a new treatment method for many human complex diseases. Unsurprisingly, identification of potential miRNA-SM associations is helpful for pharmaceutical engineering and disease therapy in the field of medical research. In this paper, we developed a novel computational model of HeteSim-based inference for SM-miRNA Association prediction (HSSMMA) by implementing a path-based measurement method of HeteSim on a heterogeneous network combined with known miRNA-SM associations, integrated miRNA similarity, and integrated SM similarity. Through considering paths from an SM to a miRNA in the heterogeneous network, the model can capture the semantics information under each path and predict potential miRNA-SM associations based on all the considered paths. We performed global, miRNA-fixed local and SM-fixed local leave one out cross validation (LOOCV) as well as 5-fold cross validation based on the dataset of known miRNA-SM associations to evaluate the prediction performance of our approach. The results showed that HSSMMA gained the corresponding areas under the receiver operating characteristic (ROC) curve (AUCs) of 0.9913, 0.9902, 0.7989, and 0.9910 ± 0.0004 based on dataset 1 and AUCs of 0.7401, 0.8466, 0.6149, and 0.7451 ± 0.0054 based on dataset 2, respectively. In case studies, 2 of the top 10 and 13 of the top 50 predicted potential miRNA-SM associations were confirmed by published literature. We further implemented case studies to test whether HSSMMA was effective for new SMs without any known related miRNAs. The results from cross validation and case studies showed that HSSMMA could be a useful prediction tool for the identification of potential miRNA-SM associations.

MicroRNA-small molecule association identification: from experimental results to computational models

Small molecule is a kind of low molecular weight organic compound with variety of biological functions. Studies have indicated that small molecules can inhibit a specific function of a multifunctional protein or disrupt protein-protein interactions and may have beneficial or detrimental effect against diseases. MicroRNAs (miRNAs) play crucial roles in cellular biology, which makes it possible to develop miRNA as diagnostics and therapeutic targets. Several drug-like compound libraries were screened successfully against different miRNAs in cellular assays further demonstrating the possibility of targeting miRNAs with small molecules. In this review, we summarized the concept and functions of small molecule and miRNAs. Especially, five aspects of miRNA functions were exhibited in detail with individual examples. In addition, four disease states that have been linked to miRNA alterations were summed up. Then, small molecules related to four important miRNAs miR-21, 122, 4644 and 27 were selected for introduction. Some important publicly accessible databases and web servers of the experimentally validated or potential small molecule-miRNA associations were discussed. Identifying small molecule targeting miRNAs has become an important goal of biomedical research. Thus, several experimental and computational models have been developed and implemented to identify novel small molecule-miRNA associations. Here, we reviewed four experimental techniques used in the past few years to search for small-molecule inhibitors of miRNAs, as well as three types of models of predicting small molecule-miRNA associations from different perspectives. Finally, we summarized the limitations of existing methods and discussed the future directions for further development of computational models.

Prediction of Potential Small Molecule-Associated MicroRNAs Using Graphlet Interaction

MicroRNAs (miRNAs) have been proved to be targeted by the small molecules recently, which made using small molecules to target miRNAs become a possible therapy for human diseases. Therefore, it is very meaningful to investigate the relationships between small molecules and miRNAs, which is still yet in the newly-developing stage. In this paper, we presented a prediction model of Graphlet Interaction based inference for Small Molecule-MiRNA Association prediction (GISMMA) by combining small molecule similarity network, miRNA similarity network and known small molecule-miRNA association network. This model described the complex relationship between two small molecules or between two miRNAs using graphlet interaction which consists of 28 isomers. The association score between a small molecule and a miRNA was calculated based on counting the numbers of graphlet interaction throughout the small molecule similarity network and the miRNA similarity network, respectively. Global and two types of local leave-one-out cross validation (LOOCV) as well as five-fold cross validation were implemented in two datasets to evaluate GISMMA. For Dataset 1, the AUCs are 0.9291 for global LOOCV, 0.9505, and 0.7702 for two local LOOCVs, 0.9263 ± 0.0026 for five-fold cross validation; for Dataset 2, the AUCs are 0.8203, 0.8640, 0.6591, and 0.8554 ± 0.0063, in turn. In case study for small molecules, 5-Fluorouracil, 17β-Estradiol and 5-Aza-2'-deoxycytidine, the numbers of top 50 miRNAs predicted by GISMMA and validated to be related to these three small molecules by experimental literatures are in turn 30, 29, and 25. Based on the results from cross validations and case studies, it is easy to realize the excellent performance of GISMMA.

Molecular biomarkers in gastro-esophageal cancer: recent developments, current trends and future directions

Gastro-esophageal adenocarcinomas (GEA) represent a severe global health burden and despite improvements in the multimodality treatment of these malignancies the prognosis of patients remains poor. HER2 overexpression/amplification has been the first predictive biomarker approved in clinical practice to guide patient selection for targeted treatment with trastuzumab in advanced gastric and gastro-esophageal junction cancers. More recently, immunotherapy has been approved for the treatment of GEA and PD-L1 expression is now a biomarker required for the administration of pembrolizumab in these diseases. Significant progress has been made in recent years in dissecting the genomic makeup of GEA in order to identify distinct molecular subtypes linked to distinct patterns of molecular alterations. GEA have been found to be highly heterogeneous malignances, representing a challenge for biomarkers discovery and targeted treatment development. The current review focuses on an overview of established and novel promising biomarkers in GEA, covering recent molecular classifications from TCGA and ACRG. Main elements of molecular heterogeneity are discussed, as well as emerging mechanisms of primary and secondary resistance to HER2 targeted treatment and recent biomarker-driven trials. Future perspectives on the role of epigenetics, miRNA/lncRNA and liquid biopsy, and patient-derived xenograft models as a new platform for molecular-targeted drug discovery in GEA are presented. Our knowledge on the genomic landscape of GEA continues to evolve, uncovering the high heterogeneity and deep complexity of these tumors. The availability of new technologies and the identification of promising novel biomarker will be critical to optimize targeted treatment development in a setting where therapeutic options are currently lacking. Nevertheless, clinical validation of novel biomarkers and treatment strategies still represents an issue.

B-RCA revealed circulating miR-33a/b associates with serum cholesterol in type 2 diabetes patients at high risk of ASCVD

AIMS

Type 2 diabetes (T2D) is a complex metabolic disease with high incidence throughout the world. Dyslipidemia is the leading cause of atherosclerotic cardiovascular diseases (ASCVD) in T2D patients. hsa-miR-33 (miR-33) serves as a regulator in lipid metabolism. We hypothesized that blood miR-33 associates with serum lipids in T2D patients at high risk of ASCVD events.

METHODS

We developed a branched rolling circle amplification (B-RCA) method and assessed its sensitivity and specificity with miR-33a/b standards by traditional TaqMan assay. Circulating miR-33a/b level was then determined with B-RCA in 30 T2D patients at high risk for developing ASCVD and 33 healthy controls. Pearson correlation coefficient was used to evaluate the correlation between circulating miR-33a/b and serum cholesterol.

RESULTS

Compared with TaqMan assay, B-RCA method showed a similar specificity and a 100-fold higher sensitivity for miR-33a detection. Circulating miR-33a/b level is positively correlated with serum total cholesterol (TC) (r = 0.364, p = 0.048) and low-density lipoprotein cholesterol (LDL-C) (r = 0.383, p = 0.037) in T2D patients at high risk for developing ASCVD.

CONCLUSIONS

Our B-RCA method provided an alternative strategy with specificity and high sensitivity for circulating miRNAs detection, and the results demonstrated that miR-33a/b might play an important role in cholesterol regulation.

Predicting Potential Drugs for Breast Cancer based on miRNA and Tissue Specificity

Network-based computational method, with the emphasis on biomolecular interactions and biological data integration, has succeeded in drug development and created new directions, such as drug repositioning and drug combination. Drug repositioning, that is finding new uses for existing drugs to treat more patients, offers time, cost and efficiency benefits in drug development, especially when in silico techniques are used. MicroRNAs (miRNAs) play important roles in multiple biological processes and have attracted much scientific attention recently. Moreover, cumulative studies demonstrate that the mature miRNAs as well as their precursors can be targeted by small molecular drugs. At the same time, human diseases result from the disordered interplay of tissue- and cell lineage-specific processes. However, few computational researches predict drug-disease potential relationships based on miRNA data and tissue specificity. Therefore, based on miRNA data and the tissue specificity of diseases, we propose a new method named as miTS to predict the potential treatments for diseases. Firstly, based on miRNAs data, target genes and information of FDA (Food and Drug Administration) approved drugs, we evaluate the relationships between miRNAs and drugs in the tissue-specific PPI (protein-protein) network. Then, we construct a tripartite network: drug-miRNA-disease Finally, we obtain the potential drug-disease associations based on the tripartite network. In this paper, we take breast cancer as case study and focus on the top-30 predicted drugs. 25 of them (83.3%) are found having known connections with breast cancer in CTD (Comparative Toxicogenomics Database) benchmark and the other 5 drugs are potential drugs for breast cancer. We further evaluate the 5 newly predicted drugs from clinical records, literature mining, KEGG pathways enrichment analysis and overlapping genes between enriched pathways. For each of the 5 new drugs, strongly supported evidences can be found in three or more aspects. In particular, Regorafenib (DB08896) has 15 overlapping KEGG pathways with breast cancer and their p-values are all very small. In addition, whether in the literature curation or clinical validation, Regorafenib has a strong correlation with breast cancer. All the facts show that Regorafenib is likely to be a truly effective drug, worthy of our further study. It further follows that our method miTS is effective and practical for predicting new drug indications, which will provide potential values for treatments of complex diseases.

Pharmacogenomics in colorectal cancer: current role in clinical practice and future perspectives

The treatment scenario of colorectal cancer (CRC) has been evolving in recent years with the introduction of novel targeted agents and new therapeutic strategies for the metastatic disease. An extensive effort has been directed to the identification of predictive biomarkers to aid patients selection and guide therapeutic choices. Pharmacogenomics represents an irreplaceable tool to individualize patients treatment based on germline and tumor acquired somatic genetic variations able to predict drugs response and risk of toxicities. The growing knowledge of CRC molecular characteristics and complex genomic makeup has played a crucial role in identifying predictive pharmacogenomic biomarkers, while supporting the rationale for the development of new drugs and treatment combinations. Clinical validation of promising biomarkers, however, is often an issue. More recently, a deeper understanding of resistance mechanisms and tumor escape dynamics under treatment pressure and the availability of novel technologies are opening new perspectives in this field. This review aims to present an overview of current pharmacogenomic biomarkers and future perspectives of pharmacogenomics in CRC, in an evolving scenario moving from a single drug-gene interactions approach to a more comprehensive genome-wide approach, comprising genomics and epigenetics.

Identification of associations between small molecule drugs and miRNAs based on functional similarity

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.

MiR-29a Suppresses Spermatogenic Cell Apoptosis in Testicular Ischemia-Reperfusion Injury by Targeting TRPV4 Channels

Background: MicroRNAs (miRNAs) have emerged as gene expression regulators in the progression of ischemia-reperfusion injury (IRI). Accumulating evidences have indicated miR-29a play roles in myocardial and cerebral IRI. However, the role of miR-29a in testicular IRI has not been elucidated.

Methods: Changes in expression of miR-29a and Transient Receptor Potential Vanilloid 4 (TRPV4) in animal samples and GC-1 spermatogenic cells were examined. The effects of miR-29a on spermatogenic cell apoptosis in testicular IRI were analyzed both in vitro and in vivo.

Results: The expression of MiR-29a was negatively correlated with the expression of TRPV4 and significantly downregulated in animal samples and GC-1 cells as testicular IRI progressed. Further studies revealed TRPV4 as a downstream target of miR-29a. Inhibition of miR-29a expression increased the expression of TRPV4 and promoted spermatogenic cell apoptosis, whereas overexpression of miR-29a downregulated TRPV4 expression and suppressed spermatogenic cell apoptosis caused by testicular IRI in vitro and in vivo.

Conclusion: Our results suggest that miR-29a suppresses apoptosis induced by testicular IRI by directly targeting TRPV4.