miRspongeR: an R/Bioconductor package for the identification and analysis of miRNA sponge interaction networks and modules

Noncoding RNAs in Vascular Cell Biology and Restenosis

In-stent restenosis (ISR), characterised by ≥50% re-narrowing of the target vessel, is a common complication following stent implantation and remains a significant challenge to the long-term success of angioplasty procedures. Considering the global burden of cardiovascular diseases, improving angioplasty patient outcomes remains a key priority. Noncoding RNAs (ncRNAs) including microRNA (miRNA), long noncoding RNA (lncRNA) and circular RNA (circRNA) have been extensively implicated in vascular cell biology and ISR through multiple, both distinct and overlapping, mechanisms. Vascular smooth muscle cells, endothelial cells and macrophages constitute the main cell types involved in the multifactorial pathophysiology of ISR. The identification of critical regulators exemplified by ncRNAs in all these cell types and processes makes them an exciting therapeutic target in the field of restenosis. In this review, we will comprehensively explore the potential functions and underlying molecular mechanisms of ncRNAs in vascular cell biology in the context of restenosis, with an in-depth focus on vascular cell dysfunction during restenosis development and progression. We will also discuss the diagnostic biomarker and therapeutic target potential of ncRNAs in ISR. Finally, we will discuss the current shortcomings, challenges, and perspectives toward the clinical application of ncRNAs.

miRspongeR 2.0: an enhanced R package for exploring miRNA sponge regulation

Summary

MicroRNA (miRNA) sponges influence the capability of miRNA-mediated gene silencing by competing for shared miRNA response elements and play significant roles in many physiological and pathological processes. It has been proved that computational or dry-lab approaches are useful to guide wet-lab experiments for uncovering miRNA sponge regulation. However, all of the existing tools only allow the analysis of miRNA sponge regulation regarding a group of samples, rather than the miRNA sponge regulation unique to individual samples. Furthermore, most existing tools do not allow parallel computing for the fast identification of miRNA sponge regulation. Here, we present an enhanced version of our R/Bioconductor package, miRspongeR 2.0. Compared with the original version introduced in 2019, this package extends the resolution of miRNA sponge regulation from the multi-sample level to the single-sample level. Moreover, it supports the identification of miRNA sponge networks using parallel computing, and the construction of sample-sample correlation networks. It also provides more computational methods to infer miRNA sponge regulation and expands the ground truth for validation. With these new features, we anticipate that miRspongeR 2.0 will further accelerate the research on miRNA sponges with higher resolution and more utilities.

Availability and implementation

http://bioconductor.org/packages/miRspongeR/.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Prognostic Roles of ceRNA Network-Based Signatures in Gastrointestinal Cancers

Gastrointestinal cancers (GICs) are high-incidence malignant tumors that seriously threaten human health around the world. Their complexity and heterogeneity make the classic staging system insufficient to guide patient management. Recently, competing endogenous RNA (ceRNA) interactions that closely link the function of protein-coding RNAs with that of non-coding RNAs, such as long non-coding RNA (lncRNA) and circular RNA (circRNA), has emerged as a novel molecular mechanism influencing miRNA-mediated gene regulation. Especially, ceRNA networks have proven to be powerful tools for deciphering cancer mechanisms and predicting therapeutic responses at the system level. Moreover, abnormal gene expression is one of the critical breaking events that disturb the stability of ceRNA network, highlighting the role of molecular biomarkers in optimizing cancer management and treatment. Therefore, developing prognostic signatures based on cancer-specific ceRNA network is of great significance for predicting clinical outcome or chemotherapy benefits of GIC patients. We herein introduce the current frontiers of ceRNA crosstalk in relation to their pathological implications and translational potentials in GICs, review the current researches on the prognostic signatures based on lncRNA or circRNA-mediated ceRNA networks in GICs, and highlight the translational implications of ceRNA signatures for GICs management. Furthermore, we summarize the computational approaches for establishing ceRNA network-based prognostic signatures, providing important clues for deciphering GIC biomarkers.

ceRNAshiny: An Interactive R/Shiny App for Identification and Analysis of ceRNA Regulation

The competing endogenous RNA (ceRNA) network is a newly discovered post-transcriptional regulation that controls both physiological and pathological progresses. Increasing research studies have been pivoted on this theory to explore the function of novel non-coding RNAs, pseudogenes, circular RNAs, and messenger RNAs. Although there are several R packages or computational tools to analyze ceRNA networks, an urgent need for easy-to-use computational tools still remains to identify ceRNA regulation. Besides, the conventional tools were mainly devoted to investigating ceRNAs in malignancies instead of those in neurodegenerative diseases. To fill this gap, we developed ceRNAshiny, an interactive R/Shiny application, which integrates widely used computational methods and databases to provide and visualize the construction and analysis of the ceRNA network, including differential gene analysis and functional annotation. In addition, demo data in ceRNAshiny could provide ceRNA network analyses about neurodegenerative diseases such as Parkinson's disease. Overall, ceRNAshiny is a user-friendly application that benefits all researchers, especially those who lack an established bioinformatic pipeline and are interested in studying ceRNA networks.

sRNAbench and sRNAtoolbox 2022 update: accurate miRNA and sncRNA profiling for model and non-model organisms

The NCBI Sequence Read Archive currently hosts microRNA sequencing data for over 800 different species, evidencing the existence of a broad taxonomic distribution in the field of small RNA research. Simultaneously, the number of samples per miRNA-seq study continues to increase resulting in a vast amount of data that requires accurate, fast and user-friendly analysis methods. Since the previous release of sRNAtoolbox in 2019, 55 000 sRNAbench jobs have been submitted which has motivated many improvements in its usability and the scope of the underlying annotation database. With this update, users can upload an unlimited number of samples or import them from Google Drive, Dropbox or URLs. Micro- and small RNA profiling can now be carried out using high-confidence Metazoan and plant specific databases, MirGeneDB and PmiREN respectively, together with genome assemblies and libraries from 441 Ensembl species. The new results page includes straightforward sample annotation to allow downstream differential expression analysis with sRNAde. Unassigned reads can also be explored by means of a new tool that performs mapping to microbial references, which can reveal contamination events or biologically meaningful findings as we describe in the example. sRNAtoolbox is available at: https://arn.ugr.es/srnatoolbox/.

Prognostic Implications and Immune Infiltration Characteristics of Chromosomal Instability-Related Dysregulated CeRNA in Lung Adenocarcinoma

An accumulating body of research indicates that long-noncoding RNAs (lncRNAs) regulate the target genes and act as competitive endogenous RNAs (ceRNAs) playing an indispensable role in lung adenocarcinoma (LUAD). LUAD is frequently accompanied by the feature of chromosomal instability (CIN); however, CIN-related ceRNAs have not been investigated yet. We systematically analyzed and integrated CIN-related dysregulated ceRNAs characteristics in LUAD samples for the first time. In TCGA LUAD cohort, CIN in tumor samples was significantly higher than that in those of adjacent, and patients with high CIN risk tended to have worse clinical outcomes. We constructed a double-weighted CIN-related dysregulated ceRNA network, in which edge weight and node weight represented the disorder extent of ceRNA and the correlation of RNA expression level and prognosis, respectively. After module mining and analysis, a potential prognostic biomarker composed of 12 RNAs (8 mRNAs and 4 lncRNAs) named CIN-related dysregulated ceRNAs (CRDC) was obtained. The CRDC risk score had a positive relation with clinical stage and CIN, and patients with high CRDC risk scores exhibited poor prognosis. Moreover, CRDC tended to be an independent risk factor with high robustness to overcome the effect of multicollinearity among other explanatory variables for disease-specific survival (DSS) in TCGA and two GEO cohorts. The result of functional analysis indicated that CRDC was involved in multiple cancer progresses, especially immune-related pathways. The patients with lower CRDC risk had higher B cell, T cell CD4+, T cell CD8+, neutrophil, macrophage, and myeloid dendritic cell infiltration than the patients with higher CRDC risk. Meanwhile, patients with lower CRDC risk could get more benefits from immunological therapy. The results suggested that the CRDC could be a potential prognostic biomarker and an immunotherapy predictor for lung adenocarcinoma.

Illuminating lncRNA Function Through Target Prediction

Most of the transcribed human genome codes for noncoding RNAs (ncRNAs), and long noncoding RNAs (lncRNAs) make for the lion's share of the human ncRNA space. Despite growing interest in lncRNAs, because there are so many of them, and because of their tissue specialization and, often, lower abundance, their catalog remains incomplete and there are multiple ongoing efforts to improve it. Consequently, the number of human lncRNA genes may be lower than 10,000 or higher than 200,000. A key open challenge for lncRNA research, now that so many lncRNA species have been identified, is the characterization of lncRNA function and the interpretation of the roles of genetic and epigenetic alterations at their loci. After all, the most important human genes to catalog and study are those that contribute to important cellular functions-that affect development or cell differentiation and whose dysregulation may play a role in the genesis and progression of human diseases. Multiple efforts have used screens based on RNA-mediated interference (RNAi), antisense oligonucleotide (ASO), and CRISPR screens to identify the consequences of lncRNA dysregulation and predict lncRNA function in select contexts, but these approaches have unresolved scalability and accuracy challenges. Instead-as was the case for better-studied ncRNAs in the past-researchers often focus on characterizing lncRNA interactions and investigating their effects on genes and pathways with known functions. Here, we focus most of our review on computational methods to identify lncRNA interactions and to predict the effects of their alterations and dysregulation on human disease pathways.

Comparisons of the immunological landscape of COVID-19 patients based on sex and disease severity by multi-omics analysis

Objective

To determine the differences in the immune response against SARS-CoV-2 infection of patients based on sex and disease severity.

Methods

We used an analytical framework of 382 transcriptional modules and multi-omics analyses to discriminate COVID-19 patients based on sex and disease severity.

Results

Male and female patients overexpressed modules related to the innate immune response. The expression of modules related to the adaptive immune response showed lower enrichment levels in males than females. Inflammation modules showed ascending overexpression in male and female patients, while a higher level was observed in severe female patients. Moderate female patients demonstrated significant overexpression to interferon, cytolytic lymphocyte, T & B cells, and erythrocytes modules. Moderate female patients showed a higher adaptive immune response than males matched group. Pathways involved in metabolism dysregulation and Hippo signaling were upregulated in females than in male patients. Females and moderate cases showed higher levels of metabolic dysregulation.

Conclusions

The immune landscape in COVID-19 patients was noticeably different between the sexes, and these differences may highlight disease vulnerability in males. This study suggested that certain treatments that increase or decrease the immune responses to SARS-CoV-2 might be necessary for male and female patients at certain disease stages.

Exploring cell-specific miRNA regulation with single-cell miRNA-mRNA co-sequencing data

Background

Existing computational methods for studying miRNA regulation are mostly based on bulk miRNA and mRNA expression data. However, bulk data only allows the analysis of miRNA regulation regarding a group of cells, rather than the miRNA regulation unique to individual cells. Recent advance in single-cell miRNA-mRNA co-sequencing technology has opened a way for investigating miRNA regulation at single-cell level. However, as currently single-cell miRNA-mRNA co-sequencing data is just emerging and only available at small-scale, there is a strong need of novel methods to exploit existing single-cell data for the study of cell-specific miRNA regulation.

Results

In this work, we propose a new method, CSmiR (Cell-Specific miRNA regulation) to combine single-cell miRNA-mRNA co-sequencing data and putative miRNA-mRNA binding information to identify miRNA regulatory networks at the resolution of individual cells. We apply CSmiR to the miRNA-mRNA co-sequencing data in 19 K562 single-cells to identify cell-specific miRNA-mRNA regulatory networks for understanding miRNA regulation in each K562 single-cell. By analyzing the obtained cell-specific miRNA-mRNA regulatory networks, we observe that the miRNA regulation in each K562 single-cell is unique. Moreover, we conduct detailed analysis on the cell-specific miRNA regulation associated with the miR-17/92 family as a case study. The comparison results indicate that CSmiR is effective in predicting cell-specific miRNA targets. Finally, through exploring cell–cell similarity matrix characterized by cell-specific miRNA regulation, CSmiR provides a novel strategy for clustering single-cells and helps to understand cell–cell crosstalk.

Conclusions

To the best of our knowledge, CSmiR is the first method to explore miRNA regulation at a single-cell resolution level, and we believe that it can be a useful method to enhance the understanding of cell-specific miRNA regulation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04498-6.

MicroRNA as a Potential Biomarker and Treatment Strategy for Ischemia-Reperfusion Injury

Ischemia-reperfusion (I/R) injury is a progressive injury that aggravates the pathological state when the organ tissue restores blood supply after a certain period of ischemia, including the myocardial, brain, liver, kidney, and intestinal. With growing evidence that microRNAs (miRNAs) play an important role as posttranscription gene silencing mediators in many I/R injury, in this review, we highlight the microRNAs that are related to I/R injury and their regulatory molecular pathways. In addition, we discussed the potential role of miRNA as a biomarker and its role as a target in I/R injury treatment. Developing miRNAs are not without its challenges, but prudent design combined with existing clinical treatments will result in more effective therapies for I/R injury. This review is aimed at providing new research results obtained in this research field. It is hoped that new research on this topic will not only generate new insights into the pathophysiology of miRNA in I/R injury but also can provide a basis for the clinical application of miRNA in I/R.

ceRNAs in Cancer: Mechanism and Functions in a Comprehensive Regulatory Network

Noncoding RNAs have been shown with powerful ability in post-transcriptional regulation, enabling intertwined RNA crosstalk and global molecular interaction in a large amount of dysfunctional conditions including cancer. Competing endogenous RNAs (ceRNAs) are those competitively binding with shared microRNAs (miRNAs), freeing their counterparts from miRNA-induced degradation, thus actively influencing and connecting with each other. Constantly updated analytical approaches boost outstanding advancement achieved in this burgeoning hotspot in multilayered intracellular communication, providing new insights into pathogenesis and clinical treatment. Here, we summarize the mechanisms and correlated factors under this RNA interplay and deregulated transcription profile in neoplasm and tumor progression, underscoring the great significance of ceRNAs for diagnostic values, monitoring biomarkers, and prognosis evaluation in cancer.

Time to infer miRNA sponge modules

Inferring competing endogenous RNA (ceRNA) or microRNA (miRNA) sponge modules is a challenging and meaningful task for revealing ceRNA regulation mechanism at the module level. Modules in this context refer to groups of miRNA sponges which have mutual competitions and act as functional units for achieving biological processes. The recent development of computational methods based on heterogeneous data provides a novel way to discern the competitive effects of miRNA sponges on human complex diseases. This article aims to provide a comprehensive perspective of miRNA sponge module discovery methods. We first review the publicly available databases of cancer-related miRNA sponges, as the miRNA sponges involved in human cancers contribute to the discovery of cancer-associated modules. Then we review the existing computational methods for inferring miRNA sponge modules. Furthermore, we conduct an assessment on the performance of the module discovery methods with the pan-cancer dataset, and the comparison study indicates that it is useful to infer biologically meaningful miRNA sponge modules by directly mapping heterogeneous data to the competitive modules. Finally, we discuss the future directions and associated challenges in developing in silico methods to infer miRNA sponge modules. This article is categorized under: RNA Interactions with Proteins and Other Molecules > Small Molecule-RNA Interactions Regulatory RNAs/RNAi/Riboswitches > Regulatory RNAs.

miRMaster 2.0: multi-species non-coding RNA sequencing analyses at scale

Analyzing all features of small non-coding RNA sequencing data can be demanding and challenging. To facilitate this process, we developed miRMaster. After the analysis of over 125 000 human samples and 1.5 trillion human small RNA reads over 4 years, we present miRMaster 2 with a wide range of updates and new features. We extended our reference data sets so that miRMaster 2 now supports the analysis of eight species (e.g. human, mouse, chicken, dog, cow) and 10 non-coding RNA classes (e.g. microRNAs, piRNAs, tRNAs, rRNAs, circRNAs). We also incorporated new downstream analysis modules such as batch effect analysis or sample embeddings using UMAP, and updated annotation data bases included by default (miRBase, Ensembl, GtRNAdb). To accommodate the increasing popularity of single cell small-RNA sequencing data, we incorporated a module for unique molecular identifier (UMI) processing. Further, the output tables and graphics have been improved based on user feedback and new output formats that emerged in the community are now supported (e.g. miRGFF3). Finally, we integrated differential expression analysis with the miRNA enrichment analysis tool miEAA. miRMaster is freely available at https://www.ccb.uni-saarland.de/mirmaster2.

Exploring miRNA Sponge Networks of Breast Cancer by Combining miRNA-disease-lncRNA and miRNA-target Networks

Background:

Recently, ample researches show that microRNAs (miRNAs) not only interact with coding genes but interact with a pool of different RNAs. Those RNAs are called miRNA sponges, including long non-coding RNAs (lncRNAs), circular RNA, pseudogenes and various messenger RNAs. Understanding regulatory networks of miRNA sponges can better help researchers to study the mechanisms of breast cancers.

Objective:

We develop a new method to explore miRNA sponge networks of breast cancer by combining miRNAdisease-lncRNA and miRNA-target networks (MSNMDL).

Method:

Firstly, MSNMDL infers miRNA-lncRNA functional similarity networks from miRNAdisease- lncRNA networks. Secondly, MSNMDL forms lncRNA-target networks by using lncRNA to replace the role of matched miRNA in miRNA-target networks according to the lncRNA-miRNA pair of miRNA-lncRNA functional similarity networks. And MSNMDL only retains the genes of breast cancer in lncRNA-target networks to construct candidate miRNA sponge networks. Thirdly, MSNMDL merges these candidate miRNA sponge networks with other miRNA sponge interactions and then selects top-hub lncRNA and its interactions to construct miRNA sponge networks.

Results:

MSNMDL is superior to other methods in terms of biological significance and its identified modules might act as module signatures for prognostication of breast cancer.

Conclusion:

MiRNA sponge networks identified by MSNMDL are biologically significant and are closely associated with breast cancer, which makes MSNMDL a promising way for researchers to study the pathogenesis of breast cancer.

miRSM: an R package to infer and analyse miRNA sponge modules in heterogeneous data

In molecular biology, microRNA (miRNA) sponges are RNA transcripts which compete with other RNA transcripts for binding with miRNAs. Research has shown that miRNA sponges have a fundamental impact on tissue development and disease progression. Generally, to achieve a specific biological function, miRNA sponges tend to form modules or communities in a biological system. Until now, however, there is still a lack of tools to aid researchers to infer and analyse miRNA sponge modules from heterogeneous data. To fill this gap, we develop an R/Bioconductor package, miRSM, for facilitating the procedure of inferring and analysing miRNA sponge modules. miRSM provides a collection of 50 co-expression analysis methods to identify gene co-expression modules (which are candidate miRNA sponge modules), four module discovery methods to infer miRNA sponge modules and seven modular analysis methods for investigating miRNA sponge modules. miRSM will enable researchers to quickly apply new datasets to infer and analyse miRNA sponge modules, and will consequently accelerate the research on miRNA sponges.

An Overview of the Computational Models Dealing with the Regulatory ceRNA Mechanism and ceRNA Deregulation in Cancer

Pools of RNA molecules can act as competing endogenous RNAs (ceRNAs) and indirectly alter their expression levels by competitively binding shared microRNAs. This ceRNA cross talk yields an additional posttranscriptional regulatory layer, which plays key roles in both physiological and pathological processes. MicroRNAs can act as decoys by binding multiple RNAs, as well as RNAs can act as ceRNAs by competing for binding multiple microRNAs, leading to many cross talk interactions that could favor significant large-scale effects in spite of the weakness of single interactions. Identifying and studying these extended ceRNA interaction networks could provide a global view of the fine-tuning gene regulation in a wide range of biological processes and tumor progressions. In this chapter, we review current progress of predicting ceRNA cross talk, by summarizing the most up-to-date databases, which collect computationally predicted and/or experimentally validated miRNA-target and ceRNA-ceRNA interactions, as well as the widespread computational methods for discovering and modeling possible evidences of ceRNA-ceRNA interaction networks. These methods can be grouped in two categories: statistics-based methods exploit multivariate analysis to build ceRNA networks, by considering the miRNA expression levels when evaluating miRNA sponging relationships; mathematical methods build deterministic or stochastic models to analyze and predict the behavior of ceRNA cross talk.

Competing endogenous RNAs and cancer: How coding and non-coding molecules cross-talk can impinge on disease

Cancers are characterized by several dramatic biological changes. Among the many post-transcriptional regulatory mechanisms, microRNAs are known as fine-tune regulators for their transcript silencing ability. Competing endogenous RNAs (ceRNAs) are transcripts that share microRNA binding elements and can compete for them, thus regulating each other indirectly. ceRNA networks interconnect the regulatory control of different transcript classes of the coding and non-coding space and co-operate with other cellular and molecular regulatory mechanisms. Altered ceRNA networks are involved in tumor formation and progression as well as in chemoresistance, in invasion and in the onset of metastases. The analysis of changes in the balance between ceRNA transcripts could offer hints to identify novel pathways for diagnosis, prognosis and therapies in precision medicine interventions. Moreover, the possibility to query highly specific tumor databases, such as TCGA, and to combine clinical data, transcript expression and sequence information is allowing to develop specific predictive tools for precision medicine.

ASDmiR: A Stepwise Method to Uncover miRNA Regulation Related to Autism Spectrum Disorder

Autism spectrum disorder (ASD) is a class of neurodevelopmental disorders characterized by genetic and environmental risk factors. The pathogenesis of ASD has a strong genetic basis, consisting of rare de novo or inherited variants among a variety of multiple molecules. Previous studies have shown that microRNAs (miRNAs) are involved in neurogenesis and brain development and are closely associated with the pathogenesis of ASD. However, the regulatory mechanisms of miRNAs in ASD are largely unclear. In this work, we present a stepwise method, ASDmiR, for the identification of underlying pathogenic genes, networks, and modules associated with ASD. First, we conduct a comparison study on 12 miRNA target prediction methods by using the matched miRNA, lncRNA, and mRNA expression data in ASD. In terms of the number of experimentally confirmed miRNA-target interactions predicted by each method, we choose the best method for identifying miRNA-target regulatory network. Based on the miRNA-target interaction network identified by the best method, we further infer miRNA-target regulatory bicliques or modules. In addition, by integrating high-confidence miRNA-target interactions and gene expression data, we identify three types of networks, including lncRNA-lncRNA, lncRNA-mRNA, and mRNA-mRNA related miRNA sponge interaction networks. To reveal the community of miRNA sponges, we further infer miRNA sponge modules from the identified miRNA sponge interaction network. Functional analysis results show that the identified hub genes, as well as miRNA-associated networks and modules, are closely linked with ASD. ASDmiR is freely available at https://github.com/chenchenxiong/ASDmiR.

The Role of MicroRNAs in Acute Respiratory Distress Syndrome and Sepsis, From Targets to Therapies: A Narrative Review

Acute respiratory distress syndrome (ARDS) is a significant cause of morbidity and mortality in the intensive care unit (ICU) and is characterized by lung epithelial and endothelial cell injury, with increased permeability of the alveolar-capillary membrane, leading to pulmonary edema, severe hypoxia, and difficulty with ventilation. The most common cause of ARDS is sepsis, and currently, treatment of ARDS and sepsis has consisted mostly of supportive care because targeted therapies have largely been unsuccessful. The molecular mechanisms behind ARDS remain elusive. Recently, a number of microRNAs (miRNAs) identified through high-throughput screening studies in ARDS patients and preclinical animal models have suggested a role for miRNA in the pathophysiology of ARDS. miRNAs are small noncoding RNAs ranging from 18 to 24 nucleotides that regulate gene expression via inhibition of the target mRNA translation or by targeting complementary mRNA for early degradation. Unsurprisingly, some miRNAs that are differentially expressed in ARDS overlap with those important in sepsis. In addition, circulatory miRNA may be useful as biomarkers or as targets for pharmacologic therapy. This can be revolutionary in a syndrome that has neither a measurable indicator of the disease nor a targeted therapy. While there are currently no miRNA-based therapies targeted for ARDS, therapies targeting miRNA have reached phase II clinical trials for the treatment of a wide range of diseases. Further studies may yield a unique miRNA profile pattern that serves as a biomarker or as targets for miRNA-based pharmacologic therapy. In this review, we discuss miRNAs that have been found to play a role in ARDS and sepsis, the potential mechanism of how particular miRNAs may contribute to the pathophysiology of ARDS, and strategies for pharmacologically targeting miRNA as therapy.

Competing Endogenous RNAs, Non-Coding RNAs and Diseases: An Intertwined Story

MicroRNAs (miRNAs), a class of small non-coding RNA molecules, are responsible for RNA silencing and post-transcriptional regulation of gene expression. They can mediate a fine-tuned crosstalk among coding and non-coding RNA molecules sharing miRNA response elements (MREs). In a suitable environment, both coding and non-coding RNA molecules can be targeted by the same miRNAs and can indirectly regulate each other by competing for them. These RNAs, otherwise known as competing endogenous RNAs (ceRNAs), lead to an additional post-transcriptional regulatory layer, where non-coding RNAs can find new significance. The miRNA-mediated interplay among different types of RNA molecules has been observed in many different contexts. The analyses of ceRNA networks in cancer and other pathologies, as well as in other physiological conditions, provide new opportunities for interpreting omics data for the field of personalized medicine. The development of novel computational tools, providing putative predictions of ceRNA interactions, is a rapidly growing field of interest. In this review, I discuss and present the current knowledge of the ceRNA mechanism and its implications in a broad spectrum of different pathologies, such as cardiovascular or autoimmune diseases, cancers and neurodegenerative disorders.

Exploring the potential microRNA sponge interactions of breast cancer based on some known interactions

MicroRNA (miRNA) sponges' regulatory mechanisms play an important role in developing human cancer. Herein, we develop a new method to explore potential miRNA sponge interactions (EPMSIs) for breast cancer. Based on some known interactions, and a matching gene expression profile, EPMSIs explored other potential miRNA sponge interactions for breast cancer. Every interaction is inferred with a value representing interaction intensity. Then, we apply a clustering algorithm called BCPlaid to potential interactions. Ten modules are identified; nine of them are closely associated with biological enrichments. When we employ a classification algorithm to separate normal and tumor samples in each module, each module demonstrates powerful classification performance. Furthermore, EPMSI illustrates a new method to explore the miRNA sponge regulatory network for breast cancer by applying its superior performance.

CeRNASeek: an R package for identification and analysis of ceRNA regulation

Competitive endogenous RNA (ceRNA) represents a novel layer of gene regulation that controls both physiological and pathological processes. However, there is still lack of computational tools for quickly identifying ceRNA regulation. To address this problem, we presented an R-package, CeRNASeek, which allows identifying and analyzing ceRNA-ceRNA interactions by integration of multiple-omics data. CeRNASeek integrates six widely used computational methods to identify ceRNA-ceRNA interactions, including two global and four context-specific ceRNA regulation prediction methods. In addition, it provides several downstream analyses for predicted ceRNA-ceRNA pairs, including regulatory network analysis, functional annotation and survival analysis. With examples of cancer-related ceRNA prioritization and cancer subtyping, we demonstrate that CeRNASeek is a valuable tool for investigating the function of ceRNAs in complex diseases. In summary, CeRNASeek provides a comprehensive and efficient tool for identifying and analysis of ceRNA regulation. The package is available on the Comprehensive R Archive Network (CRAN) at https://CRAN.R-project.org/package=CeRNASeek.

LMSM: A modular approach for identifying lncRNA related miRNA sponge modules in breast cancer

Until now, existing methods for identifying lncRNA related miRNA sponge modules mainly rely on lncRNA related miRNA sponge interaction networks, which may not provide a full picture of miRNA sponging activities in biological conditions. Hence there is a strong need of new computational methods to identify lncRNA related miRNA sponge modules. In this work, we propose a framework, LMSM, to identify LncRNA related MiRNA Sponge Modules from heterogeneous data. To understand the miRNA sponging activities in biological conditions, LMSM uses gene expression data to evaluate the influence of the shared miRNAs on the clustered sponge lncRNAs and mRNAs. We have applied LMSM to the human breast cancer (BRCA) dataset from The Cancer Genome Atlas (TCGA). As a result, we have found that the majority of LMSM modules are significantly implicated in BRCA and most of them are BRCA subtype-specific. Most of the mediating miRNAs act as crosslinks across different LMSM modules, and all of LMSM modules are statistically significant. Multi-label classification analysis shows that the performance of LMSM modules is significantly higher than baseline’s performance, indicating the biological meanings of LMSM modules in classifying BRCA subtypes. The consistent results suggest that LMSM is robust in identifying lncRNA related miRNA sponge modules. Moreover, LMSM can be used to predict miRNA targets. Finally, LMSM outperforms a graph clustering-based strategy in identifying BRCA-related modules. Altogether, our study shows that LMSM is a promising method to investigate modular regulatory mechanism of sponge lncRNAs from heterogeneous data.

Previous studies have revealed that long non-coding RNAs (lncRNAs), as microRNA (miRNA) sponges or competing endogenous RNAs (ceRNAs), can regulate the expression levels of messenger RNAs (mRNAs) by decreasing the amount of miRNAs interacting with mRNAs. In this work, we hypothesize that the “tug-of-war” between RNA transcripts for attracting miRNAs is across groups or modules. Based on the hypothesis, we propose a framework called LMSM, to identify LncRNA related MiRNA Sponge Modules. Based on the two miRNA sponge modular competition principles, significant sharing of miRNAs and high canonical correlation between the sponge lncRNAs and mRNAs, LMSM is also capable of predicting miRNA targets. LMSM not only extends the ceRNA hypothesis, but also provides a novel way to investigate the biological functions and modular mechanism of lncRNAs in breast cancer.

A Novel Long Non-coding RNA, MSTRG.51053.2 Regulates Cisplatin Resistance by Sponging the miR-432-5p in Non-small Cell Lung Cancer Cells

Objective: The aim of this study was to investigate the molecular mechanisms underlying cisplatin (DDP) resistance in non-small cell lung cancer (NSCLC) cells by constructing a competing endogenous RNA (ceRNA) network.

Methods: The gene expression profiles of human lung adenocarcinoma DDP-resistant cell line (A549/DDP) and its progenitor (A549) were comparatively evaluated by whole-transcriptome sequencing. The differentially expressed genes (DEGs) were subjected to KEGG pathway analysis. The expression levels of mRNAs involved in several pathways associated with conferring DDP resistance to tumor cells were evaluated. The ceRNA network was constructed based on the mRNA expression levels and the sequencing data of miRNA and lncRNA. Several ceRNA regulatory relationships were validated.

Results: We quantified the expression of 17 genes involved in the six pathways by quantitative real-time polymerase chain reaction (qRT-PCR). The differential protein expression levels of eight genes were quantified by western blotting. Western blot analysis revealed six differentially expressed proteins (MGST1, MGST3, ABCG2, FXYD2, ALDH3A1, and GST-ω1). Among the genes encoding these six proteins, ABCG2, ALDH3A1, MGST3, and FXYD2 exhibited interaction with 8 lncRNAs and 4 miRNAs in the ceRNA regulatory network. The expression levels of these lncRNAs and miRNAs were quantified in cells; among these, 6 lncRNAs and 4 miRNAs exhibited differential expression between A549/DDP and A549 groups, which were used to construct a ceRNA network. The ceRNA regulatory network of MSTRG51053.2-miR-432-5p-MGST3 was validated by luciferase reporter assay.

Conclusion: The MSTRG51053.2 lncRNA may function as a ceRNA for miR-432-5p to regulate the DDP resistance in NSCLC. The MGST1, MGST3, GST-ω1, and ABCG2 mRNAs, miR-432-5p and miR-665 miRNAs, and MSTRG51053.2 and PPAN lncRNAs can serve as potential DDP drug targets to reverse DDP resistance in NSCLC.

Application of the microRNA-302/367 cluster in cancer therapy

As a novel class of noncoding RNAs, microRNAs (miRNAs) can effectively silence their target genes at the posttranscriptional level. Various biological processes, such as cell proliferation, differentiation, and motility, are regulated by miRNAs. In different diseases and different stages of disease, miRNAs have various expression patterns, which makes them candidate prognostic markers and therapeutic targets. Abnormal miRNA expression has been detected in numerous neoplastic diseases in humans, which indicates the potential role of miRNAs in tumorigenesis. Previous studies have indicated that miRNAs are involved in nearly the entire process of tumor development. MicroRNA‐302a, miR‐302b, miR‐302c, miR‐302d, and miR‐367 are members of the miR‐302/367 cluster that plays various biological roles in diverse neoplastic diseases by targeting different genes. These miRNAs have been implicated in several unique characteristics of cancer, including the evasion of growth suppressors, the sustained activation of proliferative signaling, the evasion of cell death and senescence, and the regulation of angiogenesis, invasion, and metastasis. This review provides a critical overview of miR‐302/367 cluster dysregulation and the subsequent effects in cancer and highlights the vast potential of members of this cluster as therapeutic targets and novel biomarkers.