TargetMiner: microRNA target prediction with systematic identification of tissue-specific negative examples

Enhanced expression of miR-20a driven by nanog exacerbated the degradation of extracellular matrix in thoracic aortic dissection

Thoracic aortic dissection (TAD) is a life-threatening vascular disease manifested as intramural bleeding in the medial layers of the thoracic aorta. The key histopathologic feature of TAD is medial degeneration, characterized by depletion of vascular smooth muscle cells (VSMCs) and degradation of extracellular matrix (ECM). MicroRNA, as essential epigenetic regulators, can inhibit the protein expression of target genes without modifying the sequences. This study aimed to elucidate the role and underlying mechanism of miR-20a, a member of the miR-17-92 cluster, in regulating ECM degradation during the pathogenesis of TAD. The expression of the miR-17-92 cluster was significantly increased in synthetic VSMCs derived from TAD lesions compared to contractile VSMCs isolated from normal thoracic aortas. Notably, the expression of miR-20a was increased in VSMCs in response to serum exposure and various stimuli. In TAD lesions, the expression of miR-20a was significantly negatively correlated with that of elastin. Elevated expression of miR-20a was also observed in thoracic aortas of TAD mice induced by β-aminopropionitrile fumarate and angiotensin II. Overexpression of miR-20a via mimic transfection enhanced the growth and invasive capabilities of VSMCs, with no significant impact on their migratory activity or the expression of phenotypic markers (α-SMA, SM22, and OPN). Silencing of miR-20a with inhibitor transfection mitigated the hyperactivation of MMP2 in VSMCs stimulated by PDGF-bb, as evidenced by reduced levels of active-MMP2 and increased levels of pro-MMP2. Subsequently, TIMP2 was identified as a novel target gene of miR-20a. The role of miR-20a in promoting the activation of MMP2 was mediated by the suppression of TIMP2 expression in VSMCs. In addition, the elevated expression of miR-20a was found to be directly driven by Nanog in VSMCs. Collectively, these findings indicate that miR-20a plays a crucial role in maintaining the homeostasis of the thoracic aortic wall during TAD pathogenesis and may represent a potential therapeutic target for TAD.

Identification of a Novel miR-195-5p/PNN Axis in Colorectal Cancer

Pinin (PNN) is a desmosome-associated protein that reinforces the organization of keratin intermediate filaments and stabilizes the anchoring of the cytoskeleton network to the lateral surface of the plasma membrane. The aberrant expression of PNN affects the strength of cell adhesion as well as modifies the intracellular signal transduction pathways leading to the onset of CRC. In our previous studies, we characterized the role of miR-195-5p in the regulation of desmosome junctions and in CRC progression. Here, with the aim of investigating additional mechanisms related to the desmosome complex, we identified PNN as a miR-195-5p putative target. Using a public data repository, we found that PNN was a negative prognostic factor and was overexpressed in colon cancer tissues from stage 1 of the disease. Then, we assessed PNN expression in CRC tissue specimens, confirming the overexpression of PNN in tumor sections. The increase in intracellular levels of miR-195-5p revealed a significant decrease in PNN at the mRNA and protein levels. As a consequence of PNN regulation by miR-195-5p, the expression of KRT8 and KRT19, closely connected to PNN, was affected. Finally, we investigated the in vivo effect of miR-195-5p on PNN expression in the colon of AOM/DSS-treated mice. In conclusion, we have revealed a new mechanism driven by miR-195-5p in the regulation of desmosome components, suggesting a potential pharmacological target for CRC therapy.

Machine Learning Strategies in MicroRNA Research: Bridging Genome to Phenome

MicroRNAs (miRNAs) have emerged as a prominent layer of regulation of gene expression. This article offers the salient and current aspects of machine learning (ML) tools and approaches from genome to phenome in miRNA research. First, we underline that the complexity in the analysis of miRNA function ranges from their modes of biogenesis to the target diversity in diverse biological conditions. Therefore, it is imperative to first ascertain the miRNA coding potential of genomes and understand the regulatory mechanisms of their expression. This knowledge enables the efficient classification of miRNA precursors and the identification of their mature forms and respective target genes. Second, and because one miRNA can target multiple mRNAs and vice versa, another challenge is the assessment of the miRNA-mRNA target interaction network. Furthermore, long-noncoding RNA (lncRNA)and circular RNAs (circRNAs) also contribute to this complexity. ML has been used to tackle these challenges at the high-dimensional data level. The present expert review covers more than 100 tools adopting various ML approaches pertaining to, for example, (1) miRNA promoter prediction, (2) precursor classification, (3) mature miRNA prediction, (4) miRNA target prediction, (5) miRNA- lncRNA and miRNA-circRNA interactions, (6) miRNA-mRNA expression profiling, (7) miRNA regulatory module detection, (8) miRNA-disease association, and (9) miRNA essentiality prediction. Taken together, we unpack, critically examine, and highlight the cutting-edge synergy of ML approaches and miRNA research so as to develop a dynamic and microlevel understanding of human health and diseases.

Gra-CRC-miRTar: The pre-trained nucleotide-to-graph neural networks to identify potential miRNA targets in colorectal cancer

Colorectal cancer (CRC) is the third most diagnosed cancer and the second deadliest cancer worldwide representing a major public health problem. In recent years, increasing evidence has shown that microRNA (miRNA) can control the expression of targeted human messenger RNA (mRNA) by reducing their abundance or translation, acting as oncogenes or tumor suppressors in various cancers, including CRC. Due to the significant up-regulation of oncogenic miRNAs in CRC, elucidating the underlying mechanism and identifying dysregulated miRNA targets may provide a basis for improving current therapeutic interventions. In this paper, we proposed Gra-CRC-miRTar, a pre-trained nucleotide-to-graph neural network framework, for identifying potential miRNA targets in CRC. Different from previous studies, we constructed two pre-trained models to encode RNA sequences and transformed them into de Bruijn graphs. We employed different graph neural networks to learn the latent representations. The embeddings generated from de Bruijn graphs were then fed into a Multilayer Perceptron (MLP) to perform the prediction tasks. Our extensive experiments show that Gra-CRC-miRTar achieves better performance than other deep learning algorithms and existing predictors. In addition, our analyses also successfully revealed 172 out of 201 functional interactions through experimentally validated miRNA-mRNA pairs in CRC. Collectively, our effort provides an accurate and efficient framework to identify potential miRNA targets in CRC, which can also be used to reveal miRNA target interactions in other malignancies, facilitating the development of novel therapeutics.

Correlation analysis of disulfidptosis-related gene signatures with clinical prognosis and immunotherapy response in sarcoma

Disulfidptosis, a newly discovered type of programmed cell death, could be a mechanism of cell death controlled by SLC7A11. This could be closely associated with tumor development and advancement. Nevertheless, the biological mechanism behind disulfidptosis-related genes (DRGs) in sarcoma (SARC) is uncertain. This study identified three valuable genes (SLC7A11, RPN1, GYS1) associated with disulfidptosis in sarcoma (SARC) and developed a prognostic model. The multiple databases and RT-qPCR data confirmed the upregulated expression of prognostic DRGs in SARC. The TCGA internal and ICGC external validation cohorts were utilized to validate the predictive model capacity. Our analysis of DRG riskscores revealed that the low-risk group exhibited a more favorable prognosis than the high-risk group. Furthermore, we observed a significant association between DRG riskscores and different clinical features, immune cell infiltration, immune therapeutic sensitivity, drug sensitivity, and RNA modification regulators. In addition, two external independent immunetherapy datasets and clinical tissue samples were collected, validating the value of the DRGs risk model in predicting immunotherapy response. Finally, the SLC7A11/hsa-miR-29c-3p/LINC00511, and RPN1/hsa-miR-143-3p/LINC00511 regulatory axes were constructed. This study provided DRG riskscore signatures to predict prognosis and response to immunotherapy in SARC, guiding personalized treatment decisions.

A miR-137-Related Biological Pathway of Risk for Schizophrenia Is Associated With Human Brain Emotion Processing

BACKGROUND

miR-137 is a microRNA involved in brain development, regulating neurogenesis and neuronal maturation. Genome-wide association studies have implicated miR-137 in schizophrenia risk but do not explain its involvement in brain function and underlying biology. Polygenic risk for schizophrenia mediated by miR-137 targets is associated with working memory, although other evidence points to emotion processing. We characterized the functional brain correlates of miR-137 target genes associated with schizophrenia while disentangling previously reported associations of miR-137 targets with working memory and emotion processing.

METHODS

Using RNA sequencing data from postmortem prefrontal cortex (N = 522), we identified a coexpression gene set enriched for miR-137 targets and schizophrenia risk genes. We validated the relationship of this set to miR-137 in vitro by manipulating miR-137 expression in neuroblastoma cells. We translated this gene set into polygenic scores of coexpression prediction and associated them with functional magnetic resonance imaging activation in healthy volunteers (n1 = 214; n2 = 136; n3 = 2075; n4 = 1800) and with short-term treatment response in patients with schizophrenia (N = 427).

RESULTS

In 4652 human participants, we found that 1) schizophrenia risk genes were coexpressed in a biologically validated set enriched for miR-137 targets; 2) increased expression of miR-137 target risk genes was mediated by low prefrontal miR-137 expression; 3) alleles that predict greater gene set coexpression were associated with greater prefrontal activation during emotion processing in 3 independent healthy cohorts (n1, n2, n3) in interaction with age (n4); and 4) these alleles predicted less improvement in negative symptoms following antipsychotic treatment in patients with schizophrenia.

CONCLUSIONS

The functional translation of miR-137 target gene expression linked with schizophrenia involves the neural substrates of emotion processing.

Exploring testicular miRNA profiles during developmental stages in Dezhou donkeys: A preliminary insight

Testicular development and spermatogenesis are complex phenomena controlled by various genetic factors, including miRNA-based post-transcriptional gene expression regulation. Exploring the miRNA expression patterns during testicular development in Dezhou donkeys would enhance our understanding of equine fertility and spermatogenesis. In this investigation, we examined the testicular miRNA profiles at various stages of development. The experimental animals were divided into three groups based on their developmental stages: 2 months old (juvenile: n = 3), 12 months old (adolescent; n = 3) and 24 months old (adult; n = 3) donkeys. Total RNA was extracted from dissected testicles for miRNA sequencing and analysis. In total, 586 miRNAs, including 451 known miRNAs and 135 novel miRNAs, were identified. Among identified miRNAs, 315 displayed age-dependent expression differences. The levels of miRNA expression in the juvenile group were significantly higher than in the adolescent or adult groups. The MiR-483 exhibited the maximum fold change between juvenile and adolescent groups. Several screened genes, including SLC45A4 and TFCP2L1, have been linked to male reproductive pathways in donkeys. In addition, miR-744 was predicted to regulate SPIN2B, a gene implicated in spermatocyte cell cycle progression and genomic integrity of spermatozoa. These results contribute to our comprehension of microRNA regulation during testicular development and spermatogenesis in Dezhou donkeys. The identified microRNAs and their target genes have the potential to serve as biomarkers for evaluating the reproductive capacity of stud donkeys.

Ovarian cancer classification and prognosis assessment model based on prognostic target genes in key microRNA-target gene networks

BACKGROUND

The present study was designed to screen key microRNA (miRNA)-target gene networks for ovarian cancer (OC) and to classify and construct a risk assessment system for OC based on the target genes.

METHODS

OC sample data of The Cancer Genome Atlas dataset and GSE26193, GSE30161, GSE63885 and GSE9891 datasets were retrospectively collected. Pearson correlation analysis and targeted analysis of miRNA and target gene were performed to screen key miRNA-target gene networks. Target genes associated with the prognosis of OC were screened from key miRNA-target gene networks for consensus clustering and least absolute shrinkage and selection operator-based regression machine learning analysis of OC samples.

RESULTS

Twenty target genes of 2651 key miRNA-target gene pairs had significant prognostic correlation in each OC cohort, and OC was divided into three clusters. There were differences in prognostic outcome, biological pathways, immune cell abundance and susceptibility to immune checkpoint blockade (ICB) therapy and anti-tumor drugs among the three molecular clusters. S2 exhibited the least advantage in prognosis and immunotherapy response rate in the three molecular clusters, and the pathways regulating immunity, hypoxia, metabolism and promoting malignant progression of cancer, as well as infiltrating immune and stromal cell population abundance, were the highest in this cluster. An eight-target gene prognostic model was created, and the risk index obtained by using this model not only significantly distinguished the immune characteristics of the sample, but also predicted the response of the sample to ICB treatment, and helped to screen 36 potential anti-OC drugs.

CONCLUSIONS

The present study provides a classification strategy for OC based on prognostic target genes in key miRNA-target gene networks, and creates a risk assessment system for predicting prognosis and response to ICB therapy in OC patients, providing molecular basis for prognosis and precise treatment of OC.

BAP31-Mediated miR-206/133b Cluster Promotes Transendothelial Migration and Metastasis of Colorectal Cancer

Dysregulated B cell receptor-associated protein 31 (BAP31) plays a crucial role in tumor progression. This study aimed to investigate the functions and molecular mechanism of BAP31 on the miR-206/133b cluster in colorectal cancer (CRC). qPCR was conducted to detect miRNA and mRNA levels in tissues and cells. Western blot assays were used to assess the levels of biomarkers and targets, as well as the levels of BAP31 and HOXD10. Wound healing, coculture and transwell assays were conducted to assess the transendothelial migration abilities of CRC cells. A luciferase assay was employed to assess miRNA binding effects on targets, as well as the initiating transcription effect of genomic fragments. Tumor growth and lung metastatic models were established through an in vivo animal study. BAP31 overexpression in CRC cells led to a reduction in the expression of the miR-206/133b cluster. The expression of the miR-206/133b cluster was correlated with the transendothelial migration capability of CRC cells. The miR-206/133b cluster was found to directly regulate cell division cycle 42 (CDC42) and actin-related protein 2/3 complex subunit 5 (ARPC5) in the tight junction pathway (hsa04530). Moreover, a potential transcription regulator of the miR-206/133b cluster was also found to be Homeobox D10 (HOXD10). We further elucidated the molecular mechanisms and functional mechanisms of BAP31's regulatory role in the expression levels of the miR-206/133b cluster by inhibiting HOXD10 translocation from the cytoplasm to the nucleus. In conclusion, this study provides valuable insights into how BAP31 regulates the transcription of the miR-206/133b cluster and how BAP31-related lung metastases arise in CRC.

Identification of markers for predicting prognosis and endocrine metabolism in nasopharyngeal carcinoma by miRNA-mRNA network mining and machine learning

Background

Nasopharyngeal cancer (NPC) has a high incidence in Southern China and Asia, and its survival is extremely poor in advanced patients. MiRNAs play critical roles in regulating gene expression and serve as therapeutic targets in cancer. This study sought to disclose key miRNAs and target genes responsible for NPC prognosis and endocrine metabolism.

Materials and methods

Three datasets (GSE32960, GSE70970, and GSE102349) of NPC samples came from Gene Expression Omnibus (GEO). Limma and WGCNA were applied to identify key prognostic miRNAs. There were 12 types of miRNA tools implemented to study potential target genes (mRNAs) of miRNAs. Univariate Cox regression and stepAIC were introduced to construct risk models. Pearson analysis was conducted to analyze the correlation between endocrine metabolism and RiskScore. Single-sample gene set enrichment analysis (ssGSEA), MCP-counter, and ESTIMATE were performed for immune analysis. The response to immunotherapy was predicted by TIDE and SubMap analyses.

Results

Two key miRNAs (miR-142-3p and miR-93) were closely involved in NPC prognosis. The expression of the two miRNAs was dysregulated in NPC cell lines. A total of 125 potential target genes of the key miRNAs were screened, and they were enriched in autophagy and mitophagy pathways. Five target genes (E2F1, KCNJ8, SUCO, HECTD1, and KIF23) were identified to construct a prognostic model, which was used to divide patients into high group and low group. RiskScore was negatively correlated with most endocrine-related genes and pathways. The low-risk group manifested higher immune infiltration, anticancer response, more activated immune-related pathways, and higher response to immunotherapy than the high-risk group.

Conclusions

This study revealed two key miRNAs that were highly contributable to NPC prognosis. We delineated the specific links between key miRNAs and prognostic mRNAs with miRNA-mRNA networks. The effectiveness of the five-gene model in predicting NPC prognosis as well as endocrine metabolism provided a guidance for personalized immunotherapy in NPC patients.

In silico methods for predicting functional synonymous variants

Single nucleotide variants (SNVs) contribute to human genomic diversity. Synonymous SNVs are previously considered to be "silent," but mounting evidence has revealed that these variants can cause RNA and protein changes and are implicated in over 85 human diseases and cancers. Recent improvements in computational platforms have led to the development of numerous machine-learning tools, which can be used to advance synonymous SNV research. In this review, we discuss tools that should be used to investigate synonymous variants. We provide supportive examples from seminal studies that demonstrate how these tools have driven new discoveries of functional synonymous SNVs.

A novel circRNA, hsa_circ_0069382, regulates gastric cancer progression

Aberrant expression of circRNAs is closely associated with the progression of gastric cancer; however, the specific mechanisms involved remain unclear. Our aim was to identify new gastric cancer biomarkers and explore the molecular mechanisms of gastric cancer progression. Therefore, we analyzed miRNA and circRNA microarrays of paired early-stage gastric cancer samples. Our study identified a new circRNA called hsa_circ_0069382, that had not been reported before and was expressed at low levels in gastric cancer tissues. Our study also included bioinformatics analyses which determined that the high expression of hsa_circ_0069382 regulated the BTG anti-proliferation factor 2 (BTG2)/ focal adhesion kinase (FAK) axis in gastric cancer lines by sponging for miR-15a-5p. Therefore, proliferation, invasion, and migration of gastric cancer is impacted. miR-15a-5p overexpression partially restored the effects of hsa_circ_0069382. This study provides potential new therapeutic options and a future direction to explore for gastric cancer treatment, and biomarkers.

A passage-dependent network for estimating the in vitro senescence of mesenchymal stromal/stem cells using microarray, bulk and single cell RNA sequencing

Long-term in vitro culture of human mesenchymal stem cells (MSCs) leads to cell lifespan shortening and growth stagnation due to cell senescence. Here, using sequencing data generated in the public domain, we have established a specific regulatory network of "transcription factor (TF)-microRNA (miRNA)-Target" to provide key molecules for evaluating the passage-dependent replicative senescence of mesenchymal stem cells for the quality control and status evaluation of mesenchymal stem cells prepared by different procedures. Short time-series expression miner (STEM) analysis was performed on the RNA-seq and miRNA-seq databases of mesenchymal stem cells from various passages to reveal the dynamic passage-related changes of miRNAs and mRNAs. Potential miRNA targets were predicted using seven miRNA target prediction databases, including TargetScan, miRTarBase, miRDB, miRWalk, RNA22, RNAinter, and TargetMiner. Then use the TransmiR v2.0 database to obtain experimental-supported transcription factor for regulating the selected miRNA. More than ten sequencing data related to mesenchymal stem cells or mesenchymal stem cells reprogramming were used to validate key miRNAs and mRNAs. And gene set variation analysis (GSVA) was performed to calculate the passage-dependent signature. The results showed that during the passage of mesenchymal stem cells, a total of 29 miRNAs were gradually downregulated and 210 mRNA were gradually upregulated. Enrichment analysis showed that the 29 miRNAs acted as multipotent regulatory factors of stem cells and participated in a variety of signaling pathways, including TGF-beta, HIPPO and oxygen related pathways. 210 mRNAs were involved in cell senescence. According to the target prediction results, the targets of these key miRNAs and mRNAs intersect to form a regulatory network of "TF-miRNA-Target" related to replicative senescence of cultured mesenchymal stem cells, across 35 transcription factor, 7 miRNAs (has-mir-454-3p, has-mir-196b-5p, has-mir-130b-5p, has-mir-1271-5p, has-let-7i-5p, has-let-7a-5p, and has-let-7b-5p) and 7 predicted targets (PRUNE2, DIO2, CPA4, PRKAA2, DMD, DDAH1, and GATA6). This network was further validated by analyzing datasets from a variety of mesenchymal stem cells subculture and lineage reprogramming studies, as well as qPCR analysis of early passages mesenchymal stem cells versus mesenchymal stem cells with senescence morphologies (SA-β-Gal⁺). The "TF-miRNA-Target" regulatory network constructed in this study reveals the functional mechanism of miRNAs in promoting the senescence of MSCs during in vitro expansion and provides indicators for monitoring the quality of functional mesenchymal stem cells during the preparation and clinical application.

Non-coding RNAs in human health and disease: potential function as biomarkers and therapeutic targets

Human diseases have been a critical threat from the beginning of human history. Knowing the origin, course of action and treatment of any disease state is essential. A microscopic approach to the molecular field is a more coherent and accurate way to explore the mechanism, progression, and therapy with the introduction and evolution of technology than a macroscopic approach. Non-coding RNAs (ncRNAs) play increasingly important roles in detecting, developing, and treating all abnormalities related to physiology, pathology, genetics, epigenetics, cancer, and developmental diseases. Noncoding RNAs are becoming increasingly crucial as powerful, multipurpose regulators of all biological processes. Parallel to this, a rising amount of scientific information has revealed links between abnormal noncoding RNA expression and human disorders. Numerous non-coding transcripts with unknown functions have been found in addition to advancements in RNA-sequencing methods. Non-coding linear RNAs come in a variety of forms, including circular RNAs with a continuous closed loop (circRNA), long non-coding RNAs (lncRNA), and microRNAs (miRNA). This comprises specific information on their biogenesis, mode of action, physiological function, and significance concerning disease (such as cancer or cardiovascular diseases and others). This study review focuses on non-coding RNA as specific biomarkers and novel therapeutic targets.

Landscape of MicroRNA Regulatory Network Architecture and Functional Rerouting in Cancer

Somatic mutations are a major source of cancer development, and many driver mutations have been identified in protein coding regions. However, the function of mutations located in miRNA and their target binding sites throughout the human genome remains largely unknown. Here, we built detailed cancer-specific miRNA regulatory networks across 30 cancer types to systematically analyze the effect of mutations in miRNAs and their target sites in 3' untranslated region (3' UTR), coding sequence (CDS), and 5' UTR regions. A total of 3,518,261 mutations from 9,819 samples were mapped to miRNA-gene interactions (mGI). Mutations in miRNAs showed a mutually exclusive pattern with mutations in their target genes in almost all cancer types. A linear regression method identified 148 candidate driver mutations that can significantly perturb miRNA regulatory networks. Driver mutations in 3'UTRs played their roles by altering RNA binding energy and the expression of target genes. Finally, mutated driver gene targets in 3' UTRs were significantly downregulated in cancer and functioned as tumor suppressors during cancer progression, suggesting potential miRNA candidates with significant clinical implications. A user-friendly, open-access web portal (mGI-map) was developed to facilitate further use of this data resource. Together, these results will facilitate novel noncoding biomarker identification and therapeutic drug design targeting the miRNA regulatory networks.

SIGNIFICANCE

A detailed miRNA-gene interaction map reveals extensive miRNA-mediated gene regulatory networks with mutation-induced perturbations across multiple cancers, serving as a resource for noncoding biomarker discovery and drug development.

Suppression of exosomal hsa_circ_0001005 eliminates the Vemurafenib resistance of melanoma

OBJECTIVES

More and more evidences show that circular RNAs (circRNAs) can be used as miRNA sponge to regulate the drug resistance of malignancies, including melanoma. However, how exosomal circRNAs participate in the therapeutic resistance of melanoma remains ambiguous.

METHODS

Vemurafenib-resistant A375 cells were cultured and then the circRNA profile of exosomes from the parental A375 and A375-resistant cells were sequenced. Transmission electron microscopy (TEM), exogenous nanoparticle tracking analysis (NTA) and Western Blot assays were leveraged to confirm the successful collection of exosomes from A375 and A375R cells. Another five published RNA-seq data and microRNA-seq data, and seven miRNA databases were collected to construct a competing endogenous RNA (ceRNA) network. Comprehensive bioinformatic analysis was adopted to identify key molecules related to the drug resistance, including multiscale embedded gene co-expression network analysis (MEGENA). Then, qRT-PCR, cell viability and colony formation were used to estimate the function of hub circRNAs. The role of has_circ_0001005 in vivo was verified via xenograft assay. The Tumor online Prognostic analyses Platform (ToPP) was leveraged to develop the has_circ_0001005-related prognostic models for melanoma patients based on TCGA data.

RESULTS

Compared with parental cells, hsa_circ_0001005 expression was significantly increased in resistant cells and their exosomes. The elevated level of hsa_circ_0001005 was related to the poor clinical prognosis of melanoma patients. Hsa_circ_0001005 found in melanoma was mainly secreted by drug-resistant cells as exosomes. Exosomal hsa_circ_0001005 activated multiple canonical pathways related to drug resistance through sponging four miRNAs, thus suppressing the drug sensitivity of melanoma. Knocking down hsa_circ_0001005 in vitro, we found that the inhibition of hsa_circ_0001005 could hinder the clone formation of melanoma. Further in vivo animal experiments suggested that suppression of hsa_circ_0001005 can increase the sensitivity to Vemurafenib of melanoma cells. Finally, we also constructed the functional regulatory ceRNA network and prognostic risk models for hsa_circ_0001005, and further survival analysis reveals that the regulatory network and prognostic risk models obviously affected the prognosis of melanoma patients.

CONCLUSIONS

This study confirmed that the level of hsa_circ_0001005 in exosomes is the key factor affecting drug resistance of melanoma, which provides a new potential therapeutic target for melanoma patients.

Overview of Computational and Experimental Methods to Identify Tissue-Specific MicroRNA Targets

As ubiquitous posttranscriptional regulators of gene expression, microRNAs (miRNAs) play key roles in cell physiology and function across taxa. In the last two decades, we have gained a good understanding about miRNA biogenesis pathways, modes of action, and consequences of miRNA-mediated gene regulation. More recently, research has focused on exploring causes for miRNA dysregulation, miRNA-mediated crosstalk between genes and signaling pathways, and the role of miRNAs in disease.This chapter discusses methods for the identification of miRNA-target interactions and causes for tissue-specific miRNA-target regulation. Computational approaches for predicting miRNA target sites and assessing tissue-specific target regulation are discussed. Moreover, there is an emphasis on features that affect miRNA target recognition and how high-throughput sequencing protocols can help in assessing miRNA-mediated gene regulation on a genome-wide scale. In addition, this chapter introduces some experimental approaches for the validation of miRNA targets as well as web-based resources sharing predicted and validated miRNA-target interactions.

miRBind: A Deep Learning Method for miRNA Binding Classification

The binding of microRNAs (miRNAs) to their target sites is a complex process, mediated by the Argonaute (Ago) family of proteins. The prediction of miRNA:target site binding is an important first step for any miRNA target prediction algorithm. To date, the potential for miRNA:target site binding is evaluated using either co-folding free energy measures or heuristic approaches, based on the identification of binding 'seeds', i.e., continuous stretches of binding corresponding to specific parts of the miRNA. The limitations of both these families of methods have produced generations of miRNA target prediction algorithms that are primarily focused on 'canonical' seed targets, even though unbiased experimental methods have shown that only approximately half of in vivo miRNA targets are 'canonical'. Herein, we present miRBind, a deep learning method and web server that can be used to accurately predict the potential of miRNA:target site binding. We trained our method using seed-agnostic experimental data and show that our method outperforms both seed-based approaches and co-fold free energy approaches. The full code for the development of miRBind and a freely accessible web server are freely available.

Precision machine learning to understand micro-RNA regulation in neurodegenerative diseases

Micro-RNAs (miRNAs) are short (∼21 nt) non-coding RNAs that regulate gene expression through the degradation or translational repression of mRNAs. Accumulating evidence points to a role of miRNA regulation in the pathogenesis of a wide range of neurodegenerative (ND) diseases such as, for example, Alzheimer’s disease, Parkinson’s disease, amyotrophic lateral sclerosis and Huntington disease (HD). Several systems level studies aimed to explore the role of miRNA regulation in NDs, but these studies remain challenging. Part of the problem may be related to the lack of sufficiently rich or homogeneous data, such as time series or cell-type-specific data obtained in model systems or human biosamples, to account for context dependency. Part of the problem may also be related to the methodological challenges associated with the accurate system-level modeling of miRNA and mRNA data. Here, we critically review the main families of machine learning methods used to analyze expression data, highlighting the added value of using shape-analysis concepts as a solution for precisely modeling highly dimensional miRNA and mRNA data such as the ones obtained in the study of the HD process, and elaborating on the potential of these concepts and methods for modeling complex omics data.

Multi-modal molecular programs regulate melanoma cell state

Melanoma cells display distinct intrinsic phenotypic states. Here, we seek to characterize the molecular regulation of these states using multi-omic analyses of whole exome, transcriptome, microRNA, long non-coding RNA and DNA methylation data together with reverse-phase protein array data on a panel of 68 highly annotated early passage melanoma cell lines. We demonstrate that clearly defined cancer cell intrinsic transcriptomic programs are maintained in melanoma cells ex vivo and remain highly conserved within melanoma tumors, are associated with distinct immune features within tumors, and differentially correlate with checkpoint inhibitor and adoptive T cell therapy efficacy. Through integrative analyses we demonstrate highly complex multi-omic regulation of melanoma cell intrinsic programs that provide key insights into the molecular maintenance of phenotypic states. These findings have implications for cancer biology and the identification of new therapeutic strategies. Further, these deeply characterized cell lines will serve as an invaluable resource for future research in the field.

Recent Deep Learning Methodology Development for RNA–RNA Interaction Prediction

Genetic regulation of organisms involves complicated RNA–RNA interactions (RRIs) among messenger RNA (mRNA), microRNA (miRNA), and long non-coding RNA (lncRNA). Detecting RRIs is beneficial for discovering biological mechanisms as well as designing new drugs. In recent years, with more and more experimentally verified RNA–RNA interactions being deposited into databases, statistical machine learning, especially recent deep-learning-based automatic algorithms, have been widely applied to RRI prediction with remarkable success. This paper first gives a brief introduction to the traditional machine learning methods applied on RRI prediction and benchmark databases for training the models, and then provides a recent methodology overview of deep learning models in the prediction of microRNA (miRNA)–mRNA interactions and long non-coding RNA (lncRNA)–miRNA interactions.

Multi-relation graph embedding for predicting miRNA-target gene interactions by integrating gene sequence information

Accumulated studies have found that miRNAs are in charge of many complex diseases such as cancers by modulating gene expression. Predicting miRNA-target interactions is beneficial for uncovering the crucial roles of miRNAs in regulating target genes and the progression of diseases. The emergence of large-scale genomic and biological data as well as the recent development in heterogeneous networks provides new opportunities for miRNA target identification. Compared with conventional methods, computational methods become a decent solution for high efficiency. Thus, designing a method that could excavate valid information from the heterogeneous network and gene sequences is in great demand for improving the prediction accuracy. In this study, we proposed a graph-based model named MRMTI for the prediction of miRNA-target interactions. MRMTI utilized the multi-relation graph convolution module and the Bi-LSTM module to incorporate both network topology and sequential information. The learned embeddings of miRNAs and genes were then used to calculate the prediction scores of miRNA-target pairs. Comparisons with other state-of-the-art graph embedding methods and existing bioinformatic tools illustrated the superiority of MRMTI under multiple criteria metrics. Three variants of MRMTI implied the positive effect of multi-relation. The experimental results of case studies further demonstrated the prominent ability of MRMTI in predicting novel associations.

A review on methods for predicting miRNA–mRNA regulatory modules

Identification of complex interactions between miRNAs and mRNAs in a regulatory network helps better understand the underlying biological processes. Previously, identification of these interactions was based on sequence-based predicted target binding information. With the advancement in high-throughput omics technologies, miRNA and mRNA expression for the same set of samples are available. This helps develop more efficient and flexible approaches that work by integrating miRNA and mRNA expression profiles with target binding information. Since these integrative approaches of miRNA–mRNA regulatory modules (MRMs) detection is sufficiently able to capture the minute biological details, 26 such algorithms/methods/tools for MRMs identification are comprehensively reviewed in this article. The study covers the significant features underlying every method. Therefore, the methods are classified into eight groups based on mathematical approaches to understand their working and suitability for one’s study. An algorithm could be selected based on the available information with the users and the biological question under investigation.

Circr, a Computational Tool to Identify miRNA:circRNA Associations

Circular RNAs (circRNAs) are known to act as important regulators of the microRNA (miRNA) activity. Yet, computational resources to identify miRNA:circRNA interactions are mostly limited to already annotated circRNAs or affected by high rates of false positive predictions. To overcome these limitations, we developed Circr, a computational tool for the prediction of associations between circRNAs and miRNAs. Circr combines three publicly available algorithms for de novo prediction of miRNA binding sites on target sequences (miRanda, RNAhybrid, and TargetScan) and annotates each identified miRNA:target pairs with experimentally validated miRNA:RNA interactions and binding sites for Argonaute proteins derived from either ChIPseq or CLIPseq data. The combination of multiple tools for the identification of a single miRNA recognition site with experimental data allows to efficiently prioritize candidate miRNA:circRNA interactions for functional studies in different organisms. Circr can use its internal annotation database or custom annotation tables to enhance the identification of novel and not previously annotated miRNA:circRNA sites in virtually any species. Circr is written in Python 3.6 and is released under the GNU GPL3.0 License at https://github.com/bicciatolab/Circr.

Identification of a five-miRNA signature as a novel potential prognostic biomarker in patients with nasopharyngeal carcinoma

Background

MicroRNAs (miRNAs) are involved in the prognosis of nasopharyngeal carcinoma (NPC). This study used clinical data and expression data of miRNAs to develop a prognostic survival signature for NPC patients to detect high-risk subject.

Results

We identified 160 differentially expressed miRNAs using RNA-Seq data from the GEO database. Cox regression model consisting of hsa-miR-26a, hsa-let-7e, hsa-miR-647, hsa-miR-30e, and hsa-miR-93 was constructed by the least absolute contraction and selection operator (LASSO) in the training set. All the patients were classified into high-risk or low-risk groups by the optimal cutoff value of the 5-miRNA signature risk score, and the two risk groups demonstrated significant different survival. The 5-miRNA signature showed high predictive and prognostic accuracies. The results were further confirmed in validation and external validation set. Results from multivariate Cox regression analysis validated 5-miRNA signature as an independent prognostic factor. A total of 13 target genes were predicted to be the target genes of miRNA target genes. Both PPI analysis and KEGG analysis networks were closely related to tumor signaling pathways. The prognostic model of mRNAs constructed using data from the dataset GSE102349 had higher AUCs of the target genes and higher immune infiltration scores of the low-risk groups. The mRNA prognostic model also performed well on the independent immunotherapy dataset Imvigor210.

Conclusions

This study constructed a novel 5-miRNA signature for prognostic prediction of the survival of NPC patients and may be useful for individualized treatment of NPC patients.

Prediction methods for microRNA targets in bilaterian animals: Toward a better understanding by biologists

MicroRNAs (miRNAs) are small noncoding RNAs that regulate gene expression at the posttranscriptional level. Because of their wide network of interactions, miRNAs have become the focus of many studies over the past decade, particularly in animal species. To streamline the number of potential wet lab experiments, the use of miRNA target prediction tools is currently the first step undertaken. However, the predictions made may vary considerably depending on the tool used, which is mostly due to the complex and still not fully understood mechanism of action of miRNAs. The discrepancies complicate the choice of the tool for miRNA target prediction. To provide a comprehensive view of this issue, we highlight in this review the main characteristics of miRNA-target interactions in bilaterian animals, describe the prediction models currently used, and provide some insights for the evaluation of predictor performance.

Recent development of bioinformatics tools for microRNA target prediction

MicroRNAs (miRNAs) are central players that regulate the post-transcriptional processes of gene expression. Binding of miRNAs to target mRNAs can repress their translation by inducing the degradation or by inhibiting the translation of the target mRNAs. High-throughput experimental approaches for miRNA target identification are costly and time-consuming, depending on various factors. It is vitally important to develop the bioinformatics methods for accurately predicting miRNA targets. With the increase of RNA sequences in the post-genomic era, bioinformatics methods are being developed for miRNA studies specially for miRNA target prediction. This review summarizes the current development of state-of-the-art bioinformatics tools for miRNA target prediction, points out the progress and limitations of the available miRNA databases, and their working principles. Finally, we discuss the caveat and perspectives of the next-generation algorithms for the prediction of miRNA targets.

Non-Coding RNAs in the Transcriptional Network That Differentiates Skeletal Muscles of Sedentary from Long-Term Endurance- and Resistance-Trained Elderly

In a previous study, the whole transcriptome of the vastus lateralis muscle from sedentary elderly and from age-matched athletes with an exceptional record of high-intensity, life-long exercise training was compared-the two groups representing the two extremes on a physical activity scale. Exercise training enabled the skeletal muscle to counteract age-related sarcopenia by inducing a wide range of adaptations, sustained by the expression of protein-coding genes involved in energy handling, proteostasis, cytoskeletal organization, inflammation control, and cellular senescence. Building on the previous study, we examined here the network of non-coding RNAs participating in the orchestration of gene expression and identified differentially expressed micro- and long-non-coding RNAs and some of their possible targets and roles. Unsupervised hierarchical clustering analyses of all non-coding RNAs were able to discriminate between sedentary and trained individuals, regardless of the exercise typology. Validated targets of differentially expressed miRNA were grouped by KEGG analysis, which pointed to functional areas involved in cell cycle, cytoskeletal control, longevity, and many signaling pathways, including AMP-activated protein kinase (AMPK) and mammalian target of rapamycin (mTOR), which had been shown to be pivotal in the modulation of the effects of high-intensity, life-long exercise training. The analysis of differentially expressed long-non-coding RNAs identified transcriptional networks, involving lncRNAs, miRNAs and mRNAs, affecting processes in line with the beneficial role of exercise training.

miRNA Targets: From Prediction Tools to Experimental Validation

MicroRNAs (miRNAs) are post-transcriptional regulators of gene expression in both animals and plants. By pairing to microRNA responsive elements (mREs) on target mRNAs, miRNAs play gene-regulatory roles, producing remarkable changes in several physiological and pathological processes. Thus, the identification of miRNA-mRNA target interactions is fundamental for discovering the regulatory network governed by miRNAs. The best way to achieve this goal is usually by computational prediction followed by experimental validation of these miRNA-mRNA interactions. This review summarizes the key strategies for miRNA target identification. Several tools for computational analysis exist, each with different approaches to predict miRNA targets, and their number is constantly increasing. The major algorithms available for this aim, including Machine Learning methods, are discussed, to provide practical tips for familiarizing with their assumptions and understanding how to interpret the results. Then, all the experimental procedures for verifying the authenticity of the identified miRNA-mRNA target pairs are described, including High-Throughput technologies, in order to find the best approach for miRNA validation. For each strategy, strengths and weaknesses are discussed, to enable users to evaluate and select the right approach for their interests.

circPARD3 drives malignant progression and chemoresistance of laryngeal squamous cell carcinoma by inhibiting autophagy through the PRKCI-Akt-mTOR pathway

Background

Laryngeal squamous cell carcinoma (LSCC) is the second most common malignant tumor in head and neck. Autophagy and circular RNAs (circRNAs) play critical roles in cancer progression and chemoresistance. However, the function and mechanism of circRNA in autophagy regulation of LSCC remain unclear.

Methods

The autophagy-suppressive circRNA circPARD3 was identified via RNA sequencing of 107 LSCC tissues and paired adjacent normal mucosal (ANM) tissues and high-content screening. RT-PCR, Sanger sequencing, qPCR and fluorescence in situ hybridization were performed to detect circPARD3 expression and subcellular localization. Biological functions of circPARD3 were assessed by proliferation, migration, invasion, autophagic flux, and chemoresistance assays using in vitro and in vivo models. The mechanism of circPARD3 was investigated by RNA immunoprecipitation, RNA pulldown, luciferase reporter assays, western blotting and immunohistochemical staining.

Results

Autophagy was inhibited in LSCC, and circPARD3 was upregulated in the LSCC tissues (n = 100, p < 0.001). High circPARD3 level was associated with advanced T stages (p < 0.05), N stages (p = 0.001), clinical stages (p < 0.001), poor differentiation degree (p = 0.025), and poor prognosis (p = 0.002) of LSCC patients (n = 100). Functionally, circPARD3 inhibited autophagy and promoted LSCC cell proliferation, migration, invasion and chemoresistance. We further revealed that activation of the PRKCI-Akt-mTOR pathway through sponging miR-145-5p was the main mechanism of circPARD3 inhibited autophagy, promoting LSCC progression and chemoresistance.

Conclusion

Our study reveals that the novel autophagy-suppressive circPARD3 promotes LSCC progression and chemoresistance through the PRKCI-Akt-mTOR pathway, providing new insights into circRNA-mediated autophagy regulation and potential biomarker and target for LSCC treatment.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s12943-020-01279-2.

TV-circRGPD6 Nanoparticle Suppresses Breast Cancer Stem Cell-Mediated Metastasis via the miR-26b/YAF2 Axis

Metastatic tumor is a major contributor to death caused by breast cancer. However, effective and targeted therapy for metastatic breast cancer remains to be developed. Initially, we exploited a feasible biological rationale of the association between metastatic status and tumor-initiating properties in metastatic breast cancer stem cells (BCSCs). Further, we explored that circular RNA RANBP2-like and GRIP domain-containing protein 6 (circRGPD6) regulates the maintenance of stem cell-like characteristics of BCSCs. Targeted expression of circRGPD6 via human telomerase reverse transcriptase (hTERT) promoter-driven VP16-GAL4-woodchuck hepatitis virus post-transcriptional regulatory element (WPRE)-integrated systemic amplifier delivery composite vector (TV-circRGPD6) significantly inhibited expression of stem-cell marker CD44 and increased expression of the DNA damage marker p-H2AX. Furthermore, we determined TV-circRGPD6, alone or synergized with docetaxel, displays significant therapeutic responses on metastatic BCSCs. Mechanistic analyses exploited that TV-circRGPD6 suppresses BCSC-mediated metastasis via the microRNA (miR)-26b/YAF2 axis. Clinically, for the first time, we observed that expressions of circRGPD6 and YAF2 predict a favorable prognosis in patients with breast cancer, whereas expression of miR-26b is an unfavorable prognostic factor. Overall, we have developed a TV-circRGPD6 nanoparticle that selectively expresses circRGPD6 in metastatic BCSCs to eradicate breast cancer metastasis, therefore providing a novel avenue to treat breast cancers.

The Platelet microRNA Profile of Kawasaki Disease: Identification of Novel Diagnostic Biomarkers

Challenging diagnosis and unknown etiology of Kawasaki disease (KD) increase the coronary artery lesions incidence. microRNAs (miRNAs) are the most promising biomarkers because of their stability in peripheral blood and noninvasive measurement procedure, whose potential utility have been proved in cancers. To explore the utility of differentially expressed (DE) miRNAs as early diagnostic markers, 44 patients (25 incomplete KD and 19 complete KD) and 31 febrile controls were recruited for small RNA sequencing. From all the 1922 expressed miRNA, 210 DE miRNAs were found between KD and febrile control groups. Though platelet miRNA profiles of complete KD incomplete KD were much similar through cluster analysis, the DE miRNAs were not identical. Eight DE miRNAs were validated by real-time quantitative PCR (qRT-PCR) in complete or incomplete KD groups using a normalizer, miR-126-3p, which was identified by geNorm and NormFinder tools. The expression level of miRNAs continuous changed over time was observed and the function analysis showed the potential role of miRNAs as therapeutic biomarkers. Additionally, the prediction model for KD showed a sensitivity of 78.8% and a specificity of 71.4%, respectively. This study used small RNA sequencing to identify miRNA biomarkers KD diagnosis based on a large sample size. Our findings shine a light on the understanding of molecular pathogenesis of KD and may improve the accuracy of KD diagnosis and prognosis in clinical.

circRAD18 sponges miR-208a/3164 to promote triple-negative breast cancer progression through regulating IGF1 and FGF2 expression

As a new rising star of non-coding RNA, circular RNAs (circRNAs) emerged as vital regulators with biological functions in diverse of cancers. However, the function and precise mechanism of the vast majority of circRNAs in triple-negative breast cancer (TNBC) occurrence and progression have not been clearly elucidated. In the current study, we identified and further investigated hsa_circ_0002453 (circRAD18) by analyzing our previous microarray profiling. Expression of circRAD18 was found significantly upregulated in TNBC compared with normal mammary tissues and cell lines. circRAD18 was positively correlated with T stage, clinical stage and pathological grade and was an independent risk factor for TNBC patients. We performed proliferation, colony formation, cell migration, apoptosis and mouse xenograft assays to verify the functions of circRAD18. Knockdown of circRAD18 significantly suppressed cell proliferation and migration, promoted cell apoptosis and inhibited tumor growth in functional and xenograft experiments. Through luciferase reporter assays, we confirmed that circRAD18 acts as a sponge of miR-208a and miR-3164 and promotes TNBC progression through upregulating IGF1 and FGF2 expression. Altogether, our research revealed the pivotal role of circRAD18-miR-208a/3164-IGF1/FGF2 axis in TNBC tumorigenesis and metastasis though the mechanism of competing endogenous RNAs. Thus, circRAD18 may serve as a novel prognostic biomarker and potential target for TNBC treatment in the future.

AKNA Is a Potential Prognostic Biomarker in Gastric Cancer and Function as a Tumor Suppressor by Modulating EMT-Related Pathways

The AT-hook transcription factor, AKNA, is a nuclear protein that affects a few physiological and pathological processes including cancer. Here, we investigated the role of AKNA in gastric cancer (GC). By using quantitative real-time polymerase chain reaction (qRT-PCR) and Western blot assays, AKNA was found deregulated in both GC cell lines and 32 paired GC tissues. Subsequently, Kaplan-Meier analysis and clinicopathological analysis were conducted using both 32 GC cases' data above and RNA-Seq data of AKNA in 354 GC patients and the corresponding clinical-pathological data obtained from The Cancer Genome Atlas (TCGA), and AKNA expression was found closely related to location, metastasis, and TNM staging of GC. Then, the potential molecular mechanisms of AKNA in GC were explored by gene set enrichment analysis (GSEA), qRT-PCR, and Western blot assays. AKNA was found to be a hub gene related to homotypic cell to cell adhesion, regulation of cell to cell adhesion, leukocyte cell to cell adhesion, and regulation of T cell proliferation in GC. GO analysis revealed that AKNA involved in the regulation of epithelial-mesenchymal transition (EMT)-related pathways including chemokine signaling pathway, cytokine to cytokine receptor interaction, cell adhesion molecules, and jak-stat signaling pathway in GC. To explore the regulation of AKNA expression, Targetscan and TargetMiner were used to predict the possible miRNA which targeted AKNA and found the expression of AKNA was negatively correlated to miR-762 which could be sponged by circTRNC18. In conclusion, AKNA could function as a tumor suppressor by modulating EMT-related pathways in GC. The expression of AKNA might be regulated by circTRNC18/miR-762 axis. AKNA could serve as a potential biomarker and an effective target for GC diagnosis and therapy.

Combining feature selection and shape analysis uncovers precise rules for miRNA regulation in Huntington's disease mice

Background

MicroRNA (miRNA) regulation is associated with several diseases, including neurodegenerative diseases. Several approaches can be used for modeling miRNA regulation. However, their precision may be limited for analyzing multidimensional data. Here, we addressed this question by integrating shape analysis and feature selection into miRAMINT, a methodology that we used for analyzing multidimensional RNA-seq and proteomic data from a knock-in mouse model (Hdh mice) of Huntington’s disease (HD), a disease caused by CAG repeat expansion in huntingtin (htt). This dataset covers 6 CAG repeat alleles and 3 age points in the striatum and cortex of Hdh mice.

Results

Remarkably, compared to previous analyzes of this multidimensional dataset, the miRAMINT approach retained only 31 explanatory striatal miRNA-mRNA pairs that are precisely associated with the shape of CAG repeat dependence over time, among which 5 pairs with a strong change of target expression levels. Several of these pairs were previously associated with neuronal homeostasis or HD pathogenesis, or both. Such miRNA-mRNA pairs were not detected in cortex.

Conclusions

These data suggest that miRNA regulation has a limited global role in HD while providing accurately-selected miRNA-target pairs to study how the brain may compute molecular responses to HD over time. These data also provide a methodological framework for researchers to explore how shape analysis can enhance multidimensional data analytics in biology and disease.

MicroRNA-145 suppresses cell migration and invasion in upper tract urothelial carcinoma by targeting ARF6

ADP-ribosylation factor 6 (ARF6) is a well-studied protein that is involved in multiple biological functions including cell migration and invasion. The mechanism by which ARF6 regulates the migration and invasion of upper tract urothelial carcinoma (UTUC) is still unknown. MiR-145-5p is a tumor suppressor microRNA, which is downregulated in several cancer types. We aimed to elucidate the molecular mechanism underlying the regulation of ARF6 by miR-145-5p in UTUC. ARF6 expression was observed to be higher in UTUC tissues than paired adjacent normal tissues. A reverse correlation between ARF6 and miR-145-5p was found in UTUC tissues. MiR-145-5p inhibited ARF6 expression by directly targeting its 3'-UTR. The functional studies indicated that ARF6 expression reversed the miR-145-5p-reduced tumor cell migration and invasion. Notably, miR-145-5p reduced MMP2, N-cadherin, FAK and MMP7, and elevated E-cadherin protein levels in vitro; however, the above effects were reversed by ARF6. Further, the expression of epithelial-to-mesenchymal transition (EMT) markers and cell invasion was suppressed by knocking down MMP7 in UTUC cells. These findings suggest that miR-145-5p may suppress UTUC cell motility and invasion by targeting ARF6/MMP7 through EMT.

Pan-cancer analysis reveals cooperativity of both strands of microRNA that regulate tumorigenesis and patient survival

Recently, both 5p and 3p miRNA strands are being recognized as functional instead of only one, leaving many miRNA strands uninvestigated. To determine whether both miRNA strands, which have different mRNA-targeting sequences, cooperate to regulate pathways/functions across cancer types, we evaluate genomic, epigenetic, and molecular profiles of >5200 patient samples from 14 different cancers, and RNA interference and CRISPR screens in 290 cancer cell lines. We identify concordantly dysregulated miRNA 5p/3p pairs that coordinately modulate oncogenic pathways and/or cell survival/growth across cancers. Down-regulation of both strands of miR-30a and miR-145 recurrently increased cell cycle pathway genes and significantly reduced patient survival in multiple cancers. Forced expression of all four strands show cooperativity, reducing cell cycle pathways and inhibiting lung cancer cell proliferation and migration. Therefore, we identify miRNA whose 5p/3p strands function together to regulate core tumorigenic processes/pathways and reveal a previously unknown pan-cancer miRNA signature with patient prognostic power.

5p and 3p miRNA strands have different mRNA-targeting sequences and may both functionally impact gene expression in cancer. Here, the authors undertake a pan-cancer analysis that indicates 5p/3p miRNA strands function together to regulate tumorigenic processes.

MicroRNA-206 Regulation of Skin Pigmentation in Koi Carp (Cyprinus carpio L.)

MicroRNAs (miRNAs) are ∼22 nucleotide non-coding RNA molecules that act as crucial roles in plenty of biological processes. However, the molecular and cellular mechanisms of miRNAs to regulate skin color differentiation and pigmentation in fish have not been fully understood. Herein, we revealed that miR-206, a skin-enriched miRNA, regulates melanocortin 1 receptor (Mc1r, a key regulator of melanogenesis) expression by binding to its 3'-untranslated (UTR) region through bioinformatics and luciferase reporter assay in koi carp (Cyprinus carpio L.). The analysis of spatial and temporal expression patterns suggested that miR-206 is a potential regulator in the skin pigmentation process. Then, we silenced it in vivo with an antagomir method. The result showed a substantial increase of Mc1r mRNA expression and protein level, and also its downstream genes: tyrosinase (Tyr) and dopachrome tautomerase (Dct) that encoding key enzymes involved in melanin synthesis. Moreover, we constructed the miRNA-206 sponge lentivirus vector to transfect koi carp melanocytes in vitro, further checked the functions of melanocytes using Cck-8 and Transwell assays. As a result, inhibition of miR-206 significantly up-regulated Mc1r mRNA expression and protein level and accelerated the melanocyte proliferation and migration ability compared with the scrambled-sequence negative control group (miR-NC). Overall, these findings provide the evidence that miR-206 plays a regulatory role in the skin color pigmentation through targeting the Mc1r gene and would facilitate understanding the molecular regulatory mechanisms underlying miRNA-mediated skin color pigmentation in koi carp.

Loss of p53 drives neuron reprogramming in head and neck cancer

The solid tumour microenvironment includes nerve fibres that arise from the peripheral nervous system^1,2. Recent work indicates that newly formed adrenergic nerve fibres promote tumour growth, but the origin of these nerves and the mechanism of their inception are unknown^1,3. Here, by comparing the transcriptomes of cancer-associated trigeminal sensory neurons with those of endogenous neurons in mouse models of oral cancer, we identified an adrenergic differentiation signature. We show that loss of TP53 leads to adrenergic transdifferentiation of tumour-associated sensory nerves through loss of the microRNA miR-34a. Tumour growth was inhibited by sensory denervation or pharmacological blockade of adrenergic receptors, but not by chemical sympathectomy of pre-existing adrenergic nerves. A retrospective analysis of samples from oral cancer revealed that p53 status was associated with nerve density, which was in turn associated with poor clinical outcomes. This crosstalk between cancer cells and neurons represents mechanism by which tumour-associated neurons are reprogrammed towards an adrenergic phenotype that can stimulate tumour progression, and is a potential target for anticancer therapy.

The PGC-1α/NRF1/miR-378a axis protects vascular smooth muscle cells from FFA-induced proliferation, migration and inflammation in atherosclerosis

BACKGROUND AND AIMS

Atherosclerosis (AS) is the leading cause of cardiovascular diseases. PGC-1α is a key regulator of cellular energy homeostasis, but its role in AS remains debatable.

METHODS AND RESULTS

In our study, PGC-1α was shown to be significantly decreased in the media of human atherosclerotic vessels. To explore whether miRNAs might be regulated by PGC-1α in vascular smooth muscle cells (VSMCs), microarray analysis was performed. Microarray and Pearson's correlation analysis showed that PGC-1α and miR-378a were positively correlated in vivo and in vitro. As an upstream co-activator, PGC-1α was found to regulate miR-378a through binding to the transcriptional factor NRF1 in VSMCs. Therefore, the decreased expression of PGC-1α might account for suppression of miR-378a in VSMCs in AS. Furthermore, IGF1 and TLR8, two genes known to be aberrantly up-regulated in atherogenic vessels, were identified as direct targets of miR-378a. In vitro up-regulation of miR-378a markedly inhibited free fatty acid (FFA)-induced VSMC proliferation, migration and inflammation through targeting IGF1 and TLR8.

CONCLUSIONS

These findings highlight the protective role of the PGC-1α/NRF1/miR-378a regulatory axis in AS progression and suggest miR-378a as potential therapeutic target for AS treatment.

Role of miR-124 in the regulation of retinoic acid-induced Neuro-2A cell differentiation

Retinoic acid can cause many types of cells, including mouse neuroblastoma Neuro-2A cells, to differentiate into neurons. However, it is still unknown whether microRNAs (miRNAs) play a role in this neuronal differentiation. To address this issue, real-time polymerase chain reaction assays were used to detect the expression of several differentiation-related miRNAs during the differentiation of retinoic acid-treated Neuro-2A cells. The results revealed that miR-124 and miR-9 were upregulated, while miR-125b was downregulated in retinoic acid-treated Neuro-2A cells. To identify the miRNA that may play a key role, miR-124 expression was regulated by transfection of miRNA mimics or inhibitors. Morphological analysis results showed that inhibition of miR-124 expression reversed the effects of retinoic acid on neurite outgrowth. Moreover, miR-124 overexpression alone caused Neuro-2A cells to differentiate into neurons, and its inhibitor could block this effect. These results suggest that miR-124 plays an important role in retinoic acid-induced differentiation of Neuro-2A cells.

A Supervised Ensemble Approach for Sensitive microRNA Target Prediction

MicroRNAs, a class of small non-coding RNAs, regulate important biological functions via post-transcriptional regulation of messenger RNAs (mRNAs). Despite rapid development in miRNA research, precise experimental methods to determine miRNA target interactions are still lacking. This motivated us to explore the in silico target interaction features and incorporate them in predictive modeling. We propose a systematic approach towards developing a sensitive miRNA target prediction model to explore the interplay of target recognition features. In the first step, we have employed a supervised ensemble under-sampling approach to address the problem of imbalance in the training dataset due to a larger number of negative instances. Various feature selection techniques were evaluated to obtain the optimal feature subset that best recognizes the true miRNA-mRNA targets. In the second step, we have built our optimal model, miRTPred, a novel blending ensemble-based approach that combines the predictions of the best performing traditional and classical ensemble models, through a weighted voting classifier, achieving a sensitivity of 87 percent and F1-score of 0.88 for 3'UTR region of the mRNA transcript. miRTPred outperforms popular machine learning (ML) and non-ML approaches to target prediction algorithms. miRTPred is freely available at http://bicresources.jcbose.ac.in/zhumur/mirtpred.

Reduced expression of microRNA-139-5p in hepatocellular carcinoma results in a poor outcome: An exploration the roles of microRNA-139-5p in tumorigenesis, advancement and prognosis at the molecular biological level using an integrated meta-analysis and bioinformatic investigation

Hepatocellular carcinoma (HCC) is generally considered one of the most common gastrointestinal malignant tumors, characterized by high invasiveness and metastatic rate, as well as insidious onset. A relationship between carcinogenicity and aberrant microRNA-139-5p (miR-139-5p) expression has been identified in multiple tumors while the specific molecular mechanisms of miR-139-5p in HCC have not yet been thoroughly elucidated. A meta-analysis of available data from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus, ArrayExpress and Oncomine databases, as well as the published literature, was comprehensively conducted with the aim of examining the impact of miR-139-5p expression on HCC. Additionally, predicted downstream target genes were confirmed using a series of bioinformatics tools. Moreover, a correlative biological analysis was performed to ascertain the precise function of miR-139-5p in HCC. The results revealed that the expression of miR-139-5p was noticeably lower in HCC compared with non-tumor liver tissues according to the pooled standard mean difference, which was -0.84 [95% confidence interval (CI): -1.36 to -0.32; P<0.001]. Furthermore, associations were detected between miR-139-5p expression and certain clinicopathological characteristics of TCGA samples, including tumor grade, pathological stage and T stage. Moreover, the pooled hazard ratio (HR) for overall survival (HR=1.37; 95% CI: 1.07-1.76; P=0.001) indicated that decreased miR-139-5p expression was a risk factor for adverse outcomes. Additionally, 382 intersecting genes regulated by miR-139-5p were obtained and assembled in signaling pathways, including 'transcription factor activity, sequence-specific DNA binding', 'pathways in cancer' and 'Ras signaling pathway'. Notably, four targeted genes that were focused in 'pathways in cancer' were identified as hub genes and immunohistochemical staining of the proteins encoded by these four hub genes in liver tissues, explored using the Human Protein Atlas database, confirmed their expression patterns in HCC and normal liver tissues Findings of the present study suggest that reduced miR-139-5p expression is capable of accelerating tumor progression and is associated with a poor clinical outcome by modulating the expression of downstream target genes involved in tumor-associated signaling pathways.

DeepMirTar: a deep-learning approach for predicting human miRNA targets

Motivation

MicroRNAs (miRNAs) are small non-coding RNAs that function in RNA silencing and post-transcriptional regulation of gene expression by targeting messenger RNAs (mRNAs). Because the underlying mechanisms associated with miRNA binding to mRNA are not fully understood, a major challenge of miRNA studies involves the identification of miRNA-target sites on mRNA. In silico prediction of miRNA-target sites can expedite costly and time-consuming experimental work by providing the most promising miRNA-target-site candidates.

Results

In this study, we reported the design and implementation of DeepMirTar, a deep-learning-based approach for accurately predicting human miRNA targets at the site level. The predicted miRNA-target sites are those having canonical or non-canonical seed, and features, including high-level expert-designed, low-level expert-designed and raw-data-level, were used to represent the miRNA-target site. Comparison with other state-of-the-art machine-learning methods and existing miRNA-target-prediction tools indicated that DeepMirTar improved overall predictive performance.

Availability and implementation

DeepMirTar is freely available at https://github.com/Bjoux2/DeepMirTar_SdA.

Supplementary information

Supplementary data are available at Bioinformatics online.

miRBaseMiner, a tool for investigating miRBase content

microRNAs are small non-coding RNA molecules playing a central role in gene regulation. miRBase is the standard reference source for analysis and interpretation of experimental studies. However, the richness and complexity of the annotation is often underappreciated by users. Moreover, even for experienced users, the size of the resource can make it difficult to explore annotation to determine features such as species coverage, the impact of specific characteristics and changes between successive releases. A further consideration is that each new miRBase release contains entries that have had limited review and which may subsequently be removed in a future release to ensure the quality of annotation. To aid the miRBase user, we developed a software tool, miRBaseMiner, for investigating miRBase annotation and generating custom annotation sets. We apply the tool to characterize each release from v9.2 to v22 to examine how annotation has changed across releases and highlight some of the annotation features that users should keep in mind when using for miRBase for data analysis. These include: (1) entries with identical or very similar sequences; (2) entries with multiple annotated genome locations; (3) hairpin precursor entries with extremely low-estimated minimum free energy; (4) entries possessing reverse complementary; (5) entries with 3ʹ poly(A) ends. As each of these factors can impact the identification of dysregulated features and subsequent clinical or biological conclusions, miRBaseMiner is a valuable resource for any user using miRBase as a reference source.

mirDIP 4.1-integrative database of human microRNA target predictions

MicroRNAs are important regulators of gene expression, achieved by binding to the gene to be regulated. Even with modern high-throughput technologies, it is laborious and expensive to detect all possible microRNA targets. For this reason, several computational microRNA-target prediction tools have been developed, each with its own strengths and limitations. Integration of different tools has been a successful approach to minimize the shortcomings of individual databases. Here, we present mirDIP v4.1, providing nearly 152 million human microRNA-target predictions, which were collected across 30 different resources. We also introduce an integrative score, which was statistically inferred from the obtained predictions, and was assigned to each unique microRNA-target interaction to provide a unified measure of confidence. We demonstrate that integrating predictions across multiple resources does not cumulate prediction bias toward biological processes or pathways. mirDIP v4.1 is freely available at http://ophid.utoronto.ca/mirDIP/.

miR-27a-3p regulates proliferation and apoptosis of colon cancer cells by potentially targeting BTG1

microRNA (miR/miRNA)-27a-3p has been reported to be abnormally expressed in various types of cancer, including colorectal cancer (CRC). B-cell translocation gene 1 (BTG1) has also been implicated with CRC. However, the association between miR-27a-3p and BTG1 in CRC, to the best of our knowledge, has not been investigated. In order to assess whether miR-27a-3p is associated with CRC, reverse transcription-quantitative PCR was performed on 20 paired CRC and paracancerous tissues for miRNA analysis. For the screening and validation of miR-27a-3p expression in colon cancer, several colon cancer cell lines (HCT-116, HCT8, SW480, HT29, LOVO and Caco2) and the normal colorectal epithelial cell line NCM460 were examined. The highest expression levels of miR-27a-3p were detected in the HCT-116, which was selected for further experimentation. The HCT-116 cells were divided into control, miR-27a-3p mimic and inhibitor groups, and cell proliferation was tested using an MTT assay. Additionally, miR-27a-3p inhibitor/mimic or BTG1 plasmid were transfected into the HCT-116 cells, and flow cytometry was performed to analyze cell cycle distributions. TUNEL analysis was performed to detect apoptosis. Protein levels of factors in the downstream signaling pathway mediated by miR-27a-3p [ERK/mitogen-activated extracellular signal-regulated kinase (MEK)] were detected. miR-27a-3p was revealed to be overexpressed in human CRC tissues and colon cancer cell lines. Knockdown of miR-27a-3p suppressed proliferation of HCT-116 cells and apoptosis was increased. It further markedly upregulated expression levels of BTG1 and inhibited activation of proteins of the ERK/MEK signaling pathway. In addition, overexpression of BTG1 in HCT-116 cells triggered G₁/S phase cell cycle arrest and increased apoptosis via the ERK/MEK signaling pathway. In conclusion, the present study demonstrated that the effects of miR-27a-3p on colon cancer cell proliferation and apoptosis were similar to those of the tumor suppressor gene BTG1. The miR-27a-3p/BTG1 axis may have potential implications for diagnostic and therapeutic approaches in CRC.