Context-specific microRNA analysis: identification of functional microRNAs and their mRNA targets

Small RNA Targets: Advances in Prediction Tools and High-Throughput Profiling

MicroRNAs (miRNAs) are an abundant class of small non-coding RNAs that regulate gene expression at the post-transcriptional level. They are suggested to be involved in most biological processes of the cell primarily by targeting messenger RNAs (mRNAs) for cleavage or translational repression. Their binding to their target sites is mediated by the Argonaute (AGO) family of proteins. Thus, miRNA target prediction is pivotal for research and clinical applications. Moreover, transfer-RNA-derived fragments (tRFs) and other types of small RNAs have been found to be potent regulators of Ago-mediated gene expression. Their role in mRNA regulation is still to be fully elucidated, and advancements in the computational prediction of their targets are in their infancy. To shed light on these complex RNA-RNA interactions, the availability of good quality high-throughput data and reliable computational methods is of utmost importance. Even though the arsenal of computational approaches in the field has been enriched in the last decade, there is still a degree of discrepancy between the results they yield. This review offers an overview of the relevant advancements in the field of bioinformatics and machine learning and summarizes the key strategies utilized for small RNA target prediction. Furthermore, we report the recent development of high-throughput sequencing technologies, and explore the role of non-miRNA AGO driver sequences.

Aberrant transcriptional and post-transcriptional regulation of SPAG5, a YAP-TAZ-TEAD downstream effector, fuels breast cancer cell proliferation

Sperm-associated antigen 5 (SPAG5) is an important driver of the cell mitotic spindle required for chromosome segregation and progression into anaphase. SPAG5 has been identified as an important proliferation marker and chemotherapy-sensitivity predictor, especially in estrogen receptor-negative breast cancer subtypes. Here, we report that SPAG5 is a direct target of miR-10b-3p, and its aberrantly high expression associates with poor disease-free survival in two large cohorts of breast cancer patients. SPAG5 depletion strongly impaired cancer cell cycle progression, proliferation, and migration. Interestingly, high expression of SPAG5 pairs with a YAP/TAZ-activated signature in breast cancer patients. Reassuringly, the depletion of YAP, TAZ, and TEAD strongly reduced SPAG5 expression and diminished its oncogenic effects. YAP, TAZ coactivators, and TEAD transcription factors are key components of the Hippo signaling pathway involved in tumor initiation, progression, and metastasis. Furthermore, we report that SPAG5 is a direct transcriptional target of TEAD/YAP/TAZ, and pharmacological targeting of YAP and TAZ severely reduces SPAG5 expression. Collectively, our data uncover an oncogenic feedback loop, comprising miR-10b-3p, SPAG5, and YAP/TAZ/TEAD, which fuels the aberrant proliferation of breast cancer.

Integrative approaches for analysis of mRNA and microRNA high-throughput data

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Review on tools and databases linking miRNA and its mRNA targetome.

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Databases show little overlap in miRNA targetome predictions suggesting strong contextual effects.

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Deconvolution and deep learning approaches are promising new approaches to improve miRNA targetome predictions.

Advanced sequencing technologies such as RNASeq provide the means for production of massive amounts of data, including transcriptome-wide expression levels of coding RNAs (mRNAs) and non-coding RNAs such as miRNAs, lncRNAs, piRNAs and many other RNA species. In silico analysis of datasets, representing only one RNA species is well established and a variety of tools and pipelines are available. However, attaining a more systematic view of how different players come together to regulate the expression of a gene or a group of genes requires a more intricate approach to data analysis. To fully understand complex transcriptional networks, datasets representing different RNA species need to be integrated. In this review, we will focus on miRNAs as key post-transcriptional regulators summarizing current computational approaches for miRNA:target gene prediction as well as new data-driven methods to tackle the problem of comprehensively and accurately dissecting miRNome-targetome interactions.

Elucidation of molecular links between obesity and cancer through microRNA regulation

Background

Obesity contributes to high cancer risk in humans and the mechanistic links between these two pathologies are not yet understood. Recent emerging evidence has associated obesity and cancer with metabolic abnormalities and inflammation where microRNA regulation has a strong implication.

Methods

In this study, we have developed an integrated framework to unravel obesity-cancer linkage from a microRNA regulation perspective. Different from traditional means of identifying static microRNA targets based on sequence and structure properties, our approach focused on the discovery of context-dependent microRNA-mRNA interactions that are potentially associated with disease progression via large-scale genomic analysis. Specifically, a meta-regression analysis and the integration of multi-omics information from obesity and cancers were presented to investigate the microRNA regulation in a dynamic and systematic manner.

Results

Our analysis has identified a total number of 2,143 unique microRNA-gene interactions in obesity and seven types of cancer. Common interactions in obesity and obesity-associated cancers are found to regulate genes in key metabolic processes such as fatty acid and arachidonic acid metabolism and various signaling pathways related to cell growth and inflammation. Additionally, modulated co-regulations among microRNAs targeting the same functional processes were reflected through the analysis.

Conclusion

We demonstrated the statistical modeling of microRNA-mediated gene regulation can facilitate the association study between obesity and cancer. The entire framework provides a powerful tool to understand multifaceted gene regulation in complex human diseases that can be generalized in other biomedical applications.

Supplementary Information

The online version contains supplementary material available at (10.1186/s12920-020-00797-8).

A Traditional Korean Diet Alters the Expression of Circulating MicroRNAs Linked to Diabetes Mellitus in a Pilot Trial

The traditional Korean diet (K-diet) is considered to be healthy and circulating microRNAs (miRs) have been proposed as useful markers or targets in diet therapy. We, therefore, investigated the metabolic influence of the K-diet by evaluating the expression of plasma and salivary miRs. Ten women aged 50 to 60 years were divided into either a K-diet or control diet (a Westernized Korean diet) group. Subjects were housed in a metabolic unit-like condition during the two-week dietary intervention. Blood and saliva samples were collected before and after the intervention, and changes in circulating miRs were screened by an miR array and validated by individual RT-qPCRs. In the K-diet group, eight plasma miRs were down-regulated by array (p < 0.05), out of which two miRs linked to diabetes mellitus, hsa-miR26a-5p and hsa-miR126-3p, were validated (p < 0.05). Among five down-regulated salivary miRs, hsa-miR-92-3p and hsa-miR-122a-5p were validated, which are associated with diabetes mellitus, acute coronary syndrome and non-alcoholic fatty liver disease. In the control diet group, validated were down-regulated plasma hsa-miR-25-3p and salivary hsa-miR-31-5p, which are associated with diabetes mellitus, adipogenesis and obesity. The K-diet may influence the metabolic conditions associated with diabetes mellitus, as evidenced by changes in circulating miRs, putative biomarkers for K-diet.

Model-Based Integration Analysis Revealed Presence of Novel Prognostic miRNA Targets and Important Cancer Driver Genes in Triple-Negative Breast Cancers

Background: miRNAs (microRNAs) play a key role in triple-negative breast cancer (TNBC) progression, and its heterogeneity at the expression, pathological and clinical levels. Stratification of breast cancer subtypes on the basis of genomics and transcriptomics profiling, along with the known biomarkers’ receptor status, has revealed the existence of subgroups known to have diverse clinical outcomes. Recently, several studies have analysed expression profiles of matched mRNA and miRNA to investigate the underlying heterogeneity of TNBC and the potential role of miRNA as a biomarker within cancers. However, the miRNA-mRNA regulatory network within TNBC has yet to be understood. Results and Findings: We performed model-based integrated analysis of miRNA and mRNA expression profiles on breast cancer, primarily focusing on triple-negative, to identify subtype-specific signatures involved in oncogenic pathways and their potential role in patient survival outcome. Using univariate and multivariate Cox analysis, we identified 25 unique miRNAs associated with the prognosis of overall survival (OS) and distant metastases-free survival (DMFS) with “risky” and “protective” outcomes. The association of these prognostic miRNAs with subtype-specific mRNA genes was established to investigate their potential regulatory role in the canonical pathways using anti-correlation analysis. The analysis showed that miRNAs contribute to the positive regulation of known breast cancer driver genes as well as the activation of respective oncogenic pathway during disease formation. Further analysis on the “risk associated” miRNAs group revealed significant regulation of critical pathways such as cell growth, voltage-gated ion channel function, ion transport and cell-to-cell signalling. Conclusion: The study findings provide new insights into the potential role of miRNAs in TNBC disease progression through the activation of key oncogenic pathways. The results showed previously unreported subtype-specific prognostic miRNAs associated with clinical outcome that may be used for further clinical evaluation.

Integrated Analysis of microRNA and mRNA Expression Profiles: An Attempt to Disentangle the Complex Interaction Network in Attention Deficit Hyperactivity Disorder

Attention Deficit Hyperactivity Disorder (ADHD) is a childhood-onset neurodevelopmental disorder, whose etiology and pathogenesis are still largely unknown. In order to uncover novel regulatory networks and molecular pathways possibly related to ADHD, we performed an integrated miRNA and mRNA expression profiling analysis in peripheral blood samples of children with ADHD and age-matched typically developing (TD) children. The expression levels of 13 miRNAs were evaluated with microfluidic qPCR, and differentially expressed (DE) mRNAs were detected on an Illumina HiSeq 2500 genome analyzer. The miRNA targetome was identified using an integrated approach of validated and predicted interaction data extracted from seven different bioinformatic tools. Gene Ontology (GO) and pathway enrichment analyses were carried out. Results showed that six miRNAs (miR-652-3p, miR-942-5p, let-7b-5p, miR-181a-5p, miR-320a, and miR-148b-3p) and 560 genes were significantly DE in children with ADHD compared to TD subjects. After correction for multiple testing, only three miRNAs (miR-652-3p, miR-148b-3p, and miR-942-5p) remained significant. Genes known to be associated with ADHD (e.g., B4GALT2, SLC6A9 TLE1, ANK3, TRIO, TAF1, and SYNE1) were confirmed to be significantly DE in our study. Integrated miRNA and mRNA expression data identified critical key hubs involved in ADHD. Finally, the GO and pathway enrichment analyses of all DE genes showed their deep involvement in immune functions, reinforcing the hypothesis that an immune imbalance might contribute to the ADHD etiology. Despite the relatively small sample size, in this study we were able to build a complex miRNA-target interaction network in children with ADHD that might help in deciphering the disease pathogenesis. Validation in larger samples should be performed in order to possibly suggest novel therapeutic strategies for treating this complex disease.

Investigating the Role of MicroRNA and Transcription Factor Co-regulatory Networks in Multiple Sclerosis Pathogenesis

MicroRNAs (miRNAs) and transcription factors (TFs) play key roles in complex multifactorial diseases like multiple sclerosis (MS). Starting from the miRNomic profile previously associated with a cohort of pediatric MS (PedMS) patients, we applied a combined molecular and computational approach in order to verify published data in patients with adult-onset MS (AOMS). Six out of the 13 selected miRNAs (miR-320a, miR-125a-5p, miR-652-3p, miR-185-5p, miR-942-5p, miR-25-3p) were significantly upregulated in PedMS and AOMS patients, suggesting that they may be considered circulating biomarkers distinctive of the disease independently from age. A computational and unbiased miRNA-based screening of target genes not necessarily associated to MS was then performed in order to provide an extensive view of the genetic mechanisms underlying the disease. A comprehensive MS-specific miRNA-TF co-regulatory network was hypothesized; among others, SP1, RELA, NF-κB, TP53, AR, MYC, HDAC1, and STAT3 regulated the transcription of 61 targets. Interestingly, NF-κB and STAT3 cooperatively regulate the expression of immune response genes and control the cross-talk between inflammatory and immune cells. Further functional analysis will be performed on the identified critical hubs. Above all, in our view, this approach supports the need of multidisciplinary strategies for shedding light into the pathogenesis of MS.

miR-671-5p inhibits gastric cancer cell proliferation and promotes cell apoptosis by targeting URGCP

Various studies have demonstrated that microRNA (miRNA) serves an important role in the development of gastric cancer. However, the expression level, clinical significance and the biological function of miRNA in gastric cancer remain largely unknown. The present study investigated the exact roles of miR-671-5p in gastric cancer, confirmed its target and explored its mechanism. Initially, the low expression levels of miR-671-5p in gastric cancer cells were confirmed by reverse transcription-quantitative polymerase chain reaction. TargetScan and MiRanda databases were utilized to forecast the target genes of miR-671-5p, and the prediction was verified by dual-luciferase reporter assay and western blot analysis. Cell Counting Kit-8 was used for cell proliferation detection. An annexin V-fluorescein isothiocyanate kit was used for cell apoptosis determination. Western blot analysis was adopted to measure the protein expression levels in different groups. The results of the present study revealed that there were lower expression levels of miR-671-5p in gastric cancer cells than in normal gastric cells. Upregulator of cell proliferation (URGCP) is a direct target of miR-671-5p and it may be negatively regulated by miR-671-5p. miR-671-5p mimics induced reduction of MKN28 cell proliferation. miR-671-5p mimics caused upregulation of MKN28 cell apoptosis. In addition, western blotting results indicated that the ratio of B-cell lymphoma 2 (Bcl-2)/Bcl-2-associated X protein was significantly decreased in the miR-671-5p mimic group compared with the negative control group (P<0.01). These results suggested that miR-671-5p had a protective role in gastric cancer through inhibiting gastric cancer cell proliferation and promoting cell apoptosis by targeting URGCP. Therefore, miR-671-5p may be an effective therapeutic target for gastric cancer.

miRTarVis+: Web-based interactive visual analytics tool for microRNA target predictions

In this paper, we present miRTarVis+, a Web-based interactive visual analytics tool for miRNA target predictions and integrative analyses of multiple prediction results. Various microRNA (miRNA) target prediction algorithms have been developed to improve sequence-based miRNA target prediction by exploiting miRNA-mRNA expression profile data. There are also a few analytics tools to help researchers predict targets of miRNAs. However, there still is a need for improving the performance for miRNA prediction algorithms and more importantly for interactive visualization tools for an integrative analysis of multiple prediction results. miRTarVis+ has an intuitive interface to support the analysis pipeline of load, filter, predict, and visualize. It can predict targets of miRNA by adopting Bayesian inference and maximal information-based nonparametric exploration (MINE) analyses as well as conventional correlation and mutual information analyses. miRTarVis+ supports an integrative analysis of multiple prediction results by providing an overview of multiple prediction results and then allowing users to examine a selected miRNA-mRNA network in an interactive treemap and node-link diagram. To evaluate the effectiveness of miRTarVis+, we conducted two case studies using miRNA-mRNA expression profile data of asthma and breast cancer patients and demonstrated that miRTarVis+ helps users more comprehensively analyze targets of miRNA from miRNA-mRNA expression profile data. miRTarVis+ is available at http://hcil.snu.ac.kr/research/mirtarvisplus.

Literature-based condition-specific miRNA-mRNA target prediction

miRNAs are small non-coding RNAs that regulate gene expression by binding to the 3'-UTR of genes. Many recent studies have reported that miRNAs play important biological roles by regulating specific mRNAs or genes. Many sequence-based target prediction algorithms have been developed to predict miRNA targets. However, these methods are not designed for condition-specific target predictions and produce many false positives; thus, expression-based target prediction algorithms have been developed for condition-specific target predictions. A typical strategy to utilize expression data is to leverage the negative control roles of miRNAs on genes. To control false positives, a stringent cutoff value is typically set, but in this case, these methods tend to reject many true target relationships, i.e., false negatives. To overcome these limitations, additional information should be utilized. The literature is probably the best resource that we can utilize. Recent literature mining systems compile millions of articles with experiments designed for specific biological questions, and the systems provide a function to search for specific information. To utilize the literature information, we used a literature mining system, BEST, that automatically extracts information from the literature in PubMed and that allows the user to perform searches of the literature with any English words. By integrating omics data analysis methods and BEST, we developed Context-MMIA, a miRNA-mRNA target prediction method that combines expression data analysis results and the literature information extracted based on the user-specified context. In the pathway enrichment analysis using genes included in the top 200 miRNA-targets, Context-MMIA outperformed the four existing target prediction methods that we tested. In another test on whether prediction methods can re-produce experimentally validated target relationships, Context-MMIA outperformed the four existing target prediction methods. In summary, Context-MMIA allows the user to specify a context of the experimental data to predict miRNA targets, and we believe that Context-MMIA is very useful for predicting condition-specific miRNA targets.

Improving microRNA target prediction with gene expression profiles

BACKGROUND

Mammalian genomes encode for thousands of microRNAs, which can potentially regulate the majority of protein-coding genes. They have been implicated in development and disease, leading to great interest in understanding their function, with computational methods being widely used to predict their targets. Most computational methods rely on sequence features, thermodynamics, and conservation filters; essentially scanning the whole transcriptome to predict one set of targets for each microRNA. This has the limitation of not considering that the same microRNA could have different sets of targets, and thus different functions, when expressed in different types of cells.

RESULTS

To address this problem, we combine popular target prediction methods with expression profiles, via machine learning, to produce a new predictor: TargetExpress. Using independent data from microarrays and high-throughput sequencing, we show that TargetExpress outperforms existing methods, and that our predictions are enriched in functions that are coherent with the added expression profile and literature reports.

CONCLUSIONS

Our method should be particularly useful for anyone studying the functions and targets of miRNAs in specific tissues or cells. TargetExpress is available at: http://targetexpress.ceiabreulab.org/ .

Tumor Evolution Inferred by Patterns of microRNA Expression through the Course of Disease, Therapy, and Recurrence in Breast Cancer

PURPOSE

Molecular evolution of tumors during progression, therapy, and metastasis is a major clinical challenge and the main reason for resistance to therapy. We hypothesized that microRNAs (miRNAs) that exhibit similar variation of expression through the course of disease in several patients have a significant function in the tumorigenic process.

EXPERIMENTAL DESIGN

Exploration of evolving disease by profiling 800 miRNA expression from serial samples of individual breast cancer patients at several time points: pretreatment, posttreatment, lymph nodes, and recurrence sites when available (58 unique samples from 19 patients). Using a dynamic approach for analysis, we identified expression modulation patterns and classified varying miRNAs into one of the eight possible temporal expression patterns.

RESULTS

The various patterns were found to be associated with different tumorigenic pathways. The dominant pattern identified an miRNA set that significantly differentiated between disease stages, and its pattern in each patient was also associated with response to therapy. These miRNAs were related to tumor proliferation and to the cell-cycle pathway, and their mRNA targets showed anticorrelated expression. Interestingly, the level of these miRNAs was lowest in matched recurrent samples from distant metastasis, indicating a gradual increase in proliferative potential through the course of disease. Finally, the average expression level of these miRNAs in the pretreatment biopsy was significantly different comparing patients experiencing recurrence to recurrence-free patients.

CONCLUSIONS

Serial tumor sampling combined with analysis of temporal expression patterns enabled to pinpoint significant signatures characterizing breast cancer progression, associated with response to therapy and with risk of recurrence. Clin Cancer Res; 22(14); 3651-62. ©2016 AACR.

Integrating genetics and epigenetics in breast cancer: biological insights, experimental, computational methods and therapeutic potential

BACKGROUND

Development of human cancer can proceed through the accumulation of different genetic changes affecting the structure and function of the genome. Combined analyses of molecular data at multiple levels, such as DNA copy-number alteration, mRNA and miRNA expression, can clarify biological functions and pathways deregulated in cancer. The integrative methods that are used to investigate these data involve different fields, including biology, bioinformatics, and statistics.

RESULTS

These methodologies are presented in this review, and their implementation in breast cancer is discussed with a focus on integration strategies. We report current applications, recent studies and interesting results leading to the identification of candidate biomarkers for diagnosis, prognosis, and therapy in breast cancer by using both individual and combined analyses.

CONCLUSION

This review presents a state of art of the role of different technologies in breast cancer based on the integration of genetics and epigenetics, and shares some issues related to the new opportunities and challenges offered by the application of such integrative approaches.

Polysome shift assay for direct measurement of miRNA inhibition by anti-miRNA drugs

Anti-miRNA (anti-miR) oligonucleotide drugs are being developed to inhibit overactive miRNAs linked to disease. To help facilitate the transition from concept to clinic, new research tools are required. Here we report a novel method--miRNA Polysome Shift Assay (miPSA)--for direct measurement of miRNA engagement by anti-miR, which is more robust than conventional pharmacodynamics using downstream target gene derepression. The method takes advantage of size differences between active and inhibited miRNA complexes. Active miRNAs bind target mRNAs in high molecular weight polysome complexes, while inhibited miRNAs are sterically blocked by anti-miRs from forming this interaction. These two states can be assessed by fractionating tissue or cell lysates using differential ultracentrifugation through sucrose gradients. Accordingly, anti-miR treatment causes a specific shift of cognate miRNA from heavy to light density fractions. The magnitude of this shift is dose-responsive and maintains a linear relationship with downstream target gene derepression while providing a substantially higher dynamic window for aiding drug discovery. In contrast, we found that the commonly used 'RT-interference' approach, which assumes that inhibited miRNA is undetectable by RT-qPCR, can yield unreliable results that poorly reflect the binding stoichiometry of anti-miR to miRNA. We also demonstrate that the miPSA has additional utility in assessing anti-miR cross-reactivity with miRNAs sharing similar seed sequences.

The mRNA related ceRNA-ceRNA landscape and significance across 20 major cancer types

Cross-talk between competitive endogenous RNAs (ceRNAs) through shared miRNAs represents a novel layer of gene regulation that plays important roles in the physiology and development of cancers. However, a global view of their system-level properties across various types of cancers is still unknown. Here, we constructed the mRNA related ceRNA–ceRNA interaction landscape across 20 cancer types by systematically analyzing molecular profiles of 5203 tumors and miRNA regulations. Our study highlights the conserved features shared by pan-cancer and higher similarity within similar origin cell type. Moreover, a core ceRNA network was identified. Function analysis identified a common theme of cancer hallmarks, however they exhibit phenotype-specific connectivity patterns. Besides, we found a marked rewiring in the ceRNA program between various cancers, and further revealed conserved and rewired network ceRNA hubs in each cancer, which were tensely competitive interactions to constitute conserved and cancer-specific modules. By providing mechanistic linkage between known cancer miRNAs, their mediated ceRNA–ceRNA interactions, and the associations with known cancer hallmarks, the inferred cancer ceRNA–ceRNA interaction landscape will serve as a powerful public resource for further biological discoveries of tumorigenesis.

MicroRNAs in the axon locally mediate the effects of chondroitin sulfate proteoglycans and cGMP on axonal growth

Axonal miRNAs locally regulate axonal growth by modulating local protein composition. Whether localized miRNAs in the axon mediate the inhibitory effect of Chondroitin sulfate proteoglycans (CSPGs) on the axon remains unknown. We showed that in cultured cortical neurons, axonal application of CSPGs inhibited axonal growth and altered axonal miRNA profiles, whereas elevation of axonal cyclic guanosine monophosphate (cGMP) levels by axonal application of sildenafil reversed the effect of CSPGs on inhibition of axonal growth and on miRNA profiles. Specifically, CSPGs elevated and reduced axonal levels of miR-29c and integrin β1 (ITGB1) proteins, respectively, while elevation of cGMP levels overcame these CSPG effects. Gain-of- and loss-of-function experiments demonstrated that miR-29c in the distal axon mediates axonal growth downstream of CSPGs and cGMP by regulating axonal protein levels of ITGB1, FAK, and RhoA. Together, our data demonstrate that axonal miRNAs play an important role in mediating the inhibitory action of CSPGs on axonal growth and that miR-29c at least partially mediates this process.

MicroRNA regulons in tumor microenvironment

Cancer initiation and progression are defined by the behavior of cancer cells per se and the development of tumor tissues, both of which are modulated by crosstalk between cancer cells and the surrounding microenvironment. Advances in cancer research have highlighted the significance of constant evolution of the tumor microenvironment, leading to tumor formation, metastasis and refractoriness to therapy. MicroRNAs (miRNAs) are small non-coding RNAs that function as major players of posttranscriptional gene regulation in diverse biological processes. They function as both tumor suppressors and promoters in many aspects of the autonomous behavior of cancer cells. Theoretically, dysfunction in the gene regulatory networks of cancer cells is one of the major driving forces for alterations of ostensibly normal surrounding cells. In this context, the core targets of miRNAs, termed miRNA regulons, are currently being expanded to include various modulators of the tumor microenvironment. Recent advances have highlighted two important roles played by miRNAs in the evolution of tumor microenvironments: miRNAs in tumor cells transform the microenvironment via non-cell-autonomous mechanisms, and miRNAs in neighboring cells stabilize cancer hallmark traits. These observations epitomize the distal and proximal functions of miRNAs in tumor microenvironments, respectively. Such regulation by miRNAs affects tumor angiogenesis, immune invasion and tumor-stromal interactions. This review summarizes recent findings on the mechanisms of miRNA-mediated regulation of tumor microenvironments, with a perspective on the design of therapeutic interventions.

MiR-137 inhibits proliferation and angiogenesis of human glioblastoma cells by targeting EZH2

It is suggested that microRNAs play important roles in the development of various cancers. Here, we showed that miR-137 is downregulated in glioblastoma (GBM) cell lines and that low levels of miR-137 are associated with a poor prognostic phenotype of GBM patients. Ectopic expression of miR-137 significantly inhibited GBM cell proliferation and angiogenesis. In addition, ectopic expression of miR-137 inhibited tumor growth and angiogenesis in a SCID mouse xenograft model. EZH2 was identified as a direct target of miR-137 by using luciferase reporter and Western blot assays, and EZH2 overexpression can rescue the inhibitory effect of miR-137 on cell proliferation and angiogenesis. Furthermore, tumor samples from GBM patients showed an inverse relationship between miR-137 and EZH2 levels. Our results suggest that miR-137 may serve as a biomarker in GBM, and the modulation of its activity may represent a novel therapeutic strategy for the treatment of GBM patients.

EGF induces microRNAs that target suppressors of cell migration: miR-15b targets MTSS1 in breast cancer

Growth factors promote tumor growth and metastasis. We found that epidermal growth factor (EGF) induced a set of 22 microRNAs (miRNAs) before promoting the migration of mammary cells. These miRNAs were more abundant in human breast tumors relative to the surrounding tissue, and their abundance varied among breast cancer subtypes. One of these miRNAs, miR-15b, targeted the 3' untranslated region of MTSS1 (metastasis suppressor protein 1). Although xenografts in which MTSS1 was knocked down grew more slowly in mice initially, longer-term growth was unaffected. Knocking down MTSS1 increased migration and Matrigel invasion of nontransformed mammary epithelial cells. Overexpressing MTSS1 in an invasive cell line decreased cell migration and invasiveness, decreased the formation of invadopodia and actin stress fibers, and increased the formation of cellular junctions. In tissues from breast cancer patients with the aggressive basal subtype, an inverse correlation occurred with the high expression of miRNA-15b and the low expression of MTSS1. Furthermore, low abundance of MTSS1 correlated with poor patient prognosis. Thus, growth factor-inducible miRNAs mediate mechanisms underlying the progression of cancer.

miR-186 and 326 predict the prognosis of pancreatic ductal adenocarcinoma and affect the proliferation and migration of cancer cells

MicroRNAs can function as key tumor suppressors or oncogenes and act as biomarkers for cancer diagnosis or prognosis. Although high-throughput assays have revealed many miRNA biomarkers for pancreatic ductal adenocarcinoma (PDAC), only a few have been validated in independent populations or investigated for functional significance in PDAC pathogenesis. In this study, we correlated the expression of 36 potentially prognostic miRNAs within PDAC tissue with clinico-pathological features and survival in 151 Chinese patients. We then analyzed the functional roles and target genes of two miRNAs in PDAC development. We found that high expression of miR-186 and miR-326 predict poor and improved survival, respectively. miR-186 was over-expressed in PDAC patients compared with controls, especially in patients with large tumors (>2 cm), lymph node metastasis, or short-term survival (< 24 months). In contrast, miR-326 was down-regulated in patients compared with controls and displayed relatively increased expression in the patients with long-term survival or without venous invasion. Functional experiments revealed that PDAC cell proliferation and migration was decreased following inhibition and enhanced following over-expression of miR-186. In contrast, it was enhanced following inhibition and decreased after over-expression of miR-326. A luciferase assay indicated that miR-186 can bind directly to the 3′-UTR of NR5A2 to repress gene expression. These findings suggest that miR-186 over-expression contributes to the invasive potential of PDAC, likely via suppression of NR5A2, thereby leading to a poor prognosis; high miR-326 expression prolongs survival likely via the decreasing invasive potential of PDAC cells. These two miRNAs can be used as markers for clinical diagnosis and prognosis, and they represent therapeutic targets for PDAC.

A computational approach to identifying gene-microRNA modules in cancer

MicroRNAs (miRNAs) play key roles in the initiation and progression of various cancers by regulating genes. Regulatory interactions between genes and miRNAs are complex, as multiple miRNAs can regulate multiple genes. In addtion, these interactions vary from patient to patient and even among patients with the same cancer type, as cancer development is a heterogeneous process. These relationships are more complicated because transcription factors and other regulatory molecules can also regulate miRNAs and genes. Hence, it is important to identify the complex relationships between genes and miRNAs in cancer.

In this study, we propose a computational approach to constructing modules that represent these relationships by integrating the expression data of genes and miRNAs with gene-gene interaction data. First, we used a biclustering algorithm to construct modules consisting of a subset of genes and a subset of samples to incorporate the heterogeneity of cancer cells. Second, we combined gene-gene interactions to include genes that play important roles in cancer-related pathways. Then, we selected miRNAs that are closely associated with genes in the modules based on a Gaussian Bayesian network and Bayesian Information Criteria. When we applied our approach to ovarian cancer and glioblastoma (GBM) data sets, 33 and 54 modules were constructed, respectively. In these modules, 91% and 94% of ovarian cancer and GBM modules, respectively, were explained either by direct regulation between genes and miRNAs or by indirect relationships via transcription factors. In addition, 48.4% and 74.0% of modules from ovarian cancer and GBM, respectively, were enriched with cancer-related pathways, and 51.7% and 71.7% of miRNAs in modules were ovarian cancer-related miRNAs and GBM-related miRNAs, respectively. Finally, we extensively analyzed significant modules and showed that most genes in these modules were related to ovarian cancer and GBM.

A microRNA (miRNA) is a small RNA molecule that regulates the expression of mRNA genes. A miRNA can regulate multiple genes, and a gene can be regulated by multiple miRNAs. The regulation of genes by miRNAs may vary from patient to patient, even if they suffer from the same type of cancer. In this study, we identify the relationships between genes and miRNAs in cancer patients using expression data. Because these relationships are complicated by the involvement of transcription factors, which are among the most influential regulators of genes, we also attempt to explain the triple relationship among genes, miRNAs, and transcription factors. We constructed modules consisting of a set of genes and miRNAs, in which the expression levels are highly correlated. In most of these modules, genes and miRNAs are related to specific cancer types; their relationships are explained both by direct regulation of genes by miRNAs and by indirect relationships via transcription factors.

Computational Prediction of microRNA Targets

Computational prediction of microRNA (miRNA) targets is a fundamental step towards the characterization of miRNA function and the understanding of their role in disease. A single miRNA can regulate hundreds of different gene transcripts through partial sequence complementarity and a single gene may be regulated by several miRNAs acting cooperatively. The remarkable advances made in recent years have allowed the identification of key features for functional miRNA binding sites. A plethora of prediction tools are now available, but their accuracies remain rather poor, as miRNA target recognition has revealed itself to be a very complex and dynamic mechanism, still only partially understood.In this chapter, the principles of miRNA target prediction in animals are presented, together with the most up-to-date and effective computational approaches and tools available.

Integration analysis of microRNA and mRNA expression profiles in human peripheral blood lymphocytes cultured in modeled microgravity

We analyzed miRNA and mRNA expression profiles in human peripheral blood lymphocytes (PBLs) incubated in microgravity condition, simulated by a ground-based rotating wall vessel (RWV) bioreactor. Our results show that 42 miRNAs were differentially expressed in MMG-incubated PBLs compared with 1 g incubated ones. Among these, miR-9-5p, miR-9-3p, miR-155-5p, miR-150-3p, and miR-378-3p were the most dysregulated. To improve the detection of functional miRNA-mRNA pairs, we performed gene expression profiles on the same samples assayed for miRNA profiling and we integrated miRNA and mRNA expression data. The functional classification of miRNA-correlated genes evidenced significant enrichment in the biological processes of immune/inflammatory response, signal transduction, regulation of response to stress, regulation of programmed cell death, and regulation of cell proliferation. We identified the correlation of miR-9-3p, miR-155-5p, miR-150-3p, and miR-378-3p expression with that of genes involved in immune/inflammatory response (e.g., IFNG and IL17F), apoptosis (e.g., PDCD4 and PTEN), and cell proliferation (e.g., NKX3-1 and GADD45A). Experimental assays of cell viability and apoptosis induction validated the results obtained by bioinformatics analyses demonstrating that in human PBLs the exposure to reduced gravitational force increases the frequency of apoptosis and decreases cell proliferation.

Predicting response to preoperative chemotherapy agents by identifying drug action on modeled microRNA regulation networks

Identifying patients most responsive to specific chemotherapy agents in neoadjuvant settings can help to maximize the benefits of treatment and minimize unnecessary side effects. Metagene approaches that predict response based on gene expression signatures derived from an associative analysis of clinical data can identify chance associations caused by the heterogeneity of a tumor, leading to reproducibility issues in independent validations. In this study, to incorporate information from drug mechanisms of action, we explore the potential of microRNA regulation networks as a new feature space for identifying predictive markers. We introduce a measure we term the CoMi (Context-specific-miRNA-regulation) pattern to represent a descriptive feature of the miRNA regulation network in the transcriptome. We examine whether the modifications to the CoMi pattern on specific biological processes are a useful representation of drug action by predicting the response to neoadjuvant Paclitaxel treatment in breast cancer and show that the drug counteracts the CoMi network dysregulation induced by tumorigenesis. We then generate a quantitative testbed to investigate the ability of the CoMi pattern to distinguish FDA approved breast cancer drugs from other FDA approved drugs not related to breast cancer. We also compare the ability of the CoMi and metagene methods to predict response to neoadjuvant Paclitaxel treatment in clinical cohorts. We find the CoMi method outperforms the metagene method, achieving area under curve (AUC) values of 0.78 and 0.66 respectively. Furthermore, several of the predicted CoMi features highlight the network-based mechanism of drug resistance. Thus, our study suggests that explicitly modeling the drug action using network biology provides a promising approach for predictive marker discovery.

Tumor suppressor microRNAs: a novel non-coding alliance against cancer

Tumor initiation and progression are the outcomes of a stepwise accumulation of genetic alterations. Among these, gene amplification and aberrant expression of oncogenic proteins, as well as deletion or inactivation of tumor suppressor genes, represent hallmark steps. Mounting evidence collected over the last few years has identified different populations of non-coding RNAs as major players in tumor suppression in almost all cancer types. Elucidating the diverse molecular mechanisms underlying the roles of non-coding RNAs in tumor progression might provide illuminating insights, potentially able to assist improved diagnosis, better staging and effective treatments of human cancers. Here we focus on several groups of tumor suppressor microRNAs, whose downregulation exerts a profound oncologic impact and might be harnessed for the benefit of cancer patients.

Widespread context dependency of microRNA-mediated regulation

Gene expression is regulated in a context-dependent, cell-type-specific manner. Condition-specific transcription is dependent on the presence of transcription factors (TFs) that can activate or inhibit its target genes (global context). Additional factors, such as chromatin structure, histone, or DNA modifications, also influence the activity of individual target genes (individual context). The role of the global and individual context for post-transcriptional regulation has not systematically been investigated on a large scale and is poorly understood. Here we show that global and individual context dependency is a pervasive feature of microRNA-mediated regulation. Our comprehensive and highly consistent data set from several high-throughput technologies (PAR-CLIP, RIP-chip, 4sU-tagging, and SILAC) provides strong evidence that context-dependent microRNA target sites (CDTS) are as frequent and functionally relevant as constitutive target sites (CTS). Furthermore, we found the global context to be insufficient to explain the CDTS, and that flanking sequence motifs provide individual context that is an equally important factor. Our results demonstrate that, similar to TF-mediated regulation, global and individual context dependency are prevalent in microRNA-mediated gene regulation, implying a much more complex post-transcriptional regulatory network than is currently known. The necessary tools to unravel post-transcriptional regulations and mechanisms need to be much more involved, and much more data will be needed for particular cell types and cellular conditions in order to understand microRNA-mediated regulation and the context-dependent post-transcriptional regulatory network.

miR-196a targets netrin 4 and regulates cell proliferation and migration of cervical cancer cells

Recent research has uncovered tumor-suppressive and oncogenic potential of miR-196a in various tumors. However, the expression and mechanism of its function in cervical cancer remains unclear. In this study, we assess relative expression of miR-196a in cervical premalignant lesions, cervical cancer tissues, and four cancer cell lines using quantitative real-time PCR. CaSki and HeLa cells were treated with miR-196a inhibitors, mimics, or pCDNA/miR-196a to investigate the role of miR-196a in cancer cell proliferation and migration. We demonstrated that miR-196a was overexpressed in cervical intraepithelial neoplasia 2-3 and cervical cancer tissue. Moreover, its expression contributes to the proliferation and migration of cervical cancer cells, whereas inhibiting its expression led to a reduction in proliferation and migration. Five candidate targets of miR-196a chosen by computational prediction and Cervical Cancer Gene Database search were measured for their mRNA in both miR-196a-overexpressing and -depleted cancer cells. Only netrin 4 (NTN4) expression displayed an inverse association with miR-196a. Fluorescent reporter assays revealed that miR-196a inhibited NTN4 expression by targeting one binding site in the 3'-untranslated region (3'-UTR) of NTN4 mRNA. Furthermore, qPCR and Western blot assays verified NTN4 expression was downregulated in cervical cancer tissues compared to normal controls, and in vivo mRNA level of NTN4 inversely correlated with miR-196a expression. In summary, our findings provide new insights about the functional role of miR-196a in cervical carcinogenesis and suggested a potential use of miR-196a for clinical diagnosis and as a therapeutic target.

The locus of microRNA-10b: a critical target for breast cancer insurgence and dissemination

Contemporary microRNA research has led to significant advances in our understanding of the process of tumorigenesis. MicroRNAs participate in different events of a cancer cell's life, through their ability to target hundreds of putative transcripts involved in almost every cellular function, including cell cycle, apoptosis, and differentiation. The relevance of these small molecules is even more evident in light of the emerging linkage between their expression and both prognosis and clinical outcome of many types of human cancers. This identifies microRNAs as potential therapeutic modifiers of cancer phenotypes. From this perspective, we overview here the miR-10b locus and its involvement in cancer, focusing on its role in the establishment (miR-10b*) and spreading (miR-10b) of breast cancer. We conclude that targeting the locus of microRNA 10b holds great potential for cancer treatment.

Integrated analysis of microRNA and mRNA expression: adding biological significance to microRNA target predictions

Current microRNA target predictions are based on sequence information and empirically derived rules but do not make use of the expression of microRNAs and their targets. This study aimed to improve microRNA target predictions in a given biological context, using in silico predictions, microRNA and mRNA expression. We used target prediction tools to produce lists of predicted targets and used a gene set test designed to detect consistent effects of microRNAs on the joint expression of multiple targets. In a single test, association between microRNA expression and target gene set expression as well as the contribution of the individual target genes on the association are determined. The strongest negatively associated mRNAs as measured by the test were prioritized. We applied our integration method to a well-defined muscle differentiation model. Validation of our predictions in C2C12 cells confirmed predicted targets of known as well as novel muscle-related microRNAs. We further studied associations between microRNA–mRNA pairs in human prostate cancer, finding some pairs that have been recently experimentally validated by others. Using the same study, we showed the advantages of the global test over Pearson correlation and lasso. We conclude that our integrated approach successfully identifies regulated microRNAs and their targets.

miR-135a targets IRS2 and regulates insulin signaling and glucose uptake in the diabetic gastrocnemius skeletal muscle

Although aberrant miRNA signatures are associated with diabetes, yet, the status and role of altered miRNAs in the diabetic skeletal muscle is currently poorly understood. Here, we report that 41 miRNAs are altered in the diabetic gastrocnemius skeletal muscle and of these, miR-135a that is identified as a critical regulator of myogenesis, is significantly up-regulated. IRS2 is predicted as its potential putative target and its levels are down-regulated in the diabetic gastrocnemius skeletal muscle. In C2C12 cells, while miR-135a levels decreased during differentiation, IRS2 levels were up-regulated. miR-135a significantly reduced IRS2 protein levels and its 3'UTR luciferase reporter activity and these were blunted by the miR-135a inhibitor and mutation in the miR-135a binding site. Knock-down of endogenous miR-135a levels increased IRS2 at the mRNA and protein levels. miR-135a also attenuated insulin stimulated phosphorylation and activation of PI3Kp85α and Akt and glucose uptake. miR-135a levels were also found to be elevated in the human diabetic skeletal muscle. In-vivo silencing of miR-135a alleviated hyperglycemia, improved glucose tolerance and significantly restored the levels of IRS2 and p-Akt in the gastrocnemius skeletal muscle of db/db mice without any effect on their hepatic levels. These suggest that miR-135a targets IRS2 levels by binding to its 3'UTR and this interaction regulates skeletal muscle insulin signaling.

Context-specific microRNA function in developmental complexity

Since their discovery, microRNAs (miRNA) have been implicated in a vast array of biological processes in animals, from fundamental developmental functions including cellular proliferation and differentiation, to more complex and specialized roles such as long-term potentiation and synapse-specific modifications in neurons. This review recounts the history behind this paradigm shift, which has seen small non-coding RNA molecules coming to the forefront of molecular biology, and introduces their role in establishing developmental complexity in animals. The fundamental mechanisms of miRNA biogenesis and function are then considered, leading into a discussion of recent discoveries transforming our understanding of how these molecules regulate gene network behaviour throughout developmental and pathophysiological processes. The emerging complexity of this mechanism is also examined with respect to the influence of cellular context on miRNA function. This discussion highlights the absolute imperative for experimental designs to appreciate the significance of context-specific factors when determining what genes are regulated by a particular miRNA. Moreover, by establishing the timing, location, and mechanism of these regulatory events, we may ultimately understand the true biological function of a specific miRNA in a given cellular environment.