CMC: Cancer miRNA Census - a list of cancer-related miRNA genes

A growing body of evidence indicates an important role of miRNAs in cancer; however, there is no definitive, convenient-to-use list of cancer-related miRNAs or miRNA genes that may serve as a reference for analyses of miRNAs in cancer. To this end, we created a list of 165 cancer-related miRNA genes called the Cancer miRNA Census (CMC). The list is based on a score, built on various types of functional and genetic evidence for the role of particular miRNAs in cancer, e.g. miRNA-cancer associations reported in databases, associations of miRNAs with cancer hallmarks, or signals of positive selection of genetic alterations in cancer. The presence of well-recognized cancer-related miRNA genes, such as MIR21, MIR155, MIR15A, MIR17 or MIRLET7s, at the top of the CMC ranking directly confirms the accuracy and robustness of the list. Additionally, to verify and indicate the reliability of CMC, we performed a validation of criteria used to build CMC, comparison of CMC with various cancer data (publications and databases), and enrichment analyses of biological pathways and processes such as Gene Ontology or DisGeNET. All validation steps showed a strong association of CMC with cancer/cancer-related processes confirming its usefulness as a reference list of miRNA genes associated with cancer.

HMDD v4.0: a database for experimentally supported human microRNA-disease associations

MicroRNAs (miRNAs) are a class of important small non-coding RNAs with critical molecular functions in almost all biological processes, and thus, they play important roles in disease diagnosis and therapy. Human MicroRNA Disease Database (HMDD) represents an important and comprehensive resource for biomedical researchers in miRNA-related medicine. Here, we introduce HMDD v4.0, which curates 53530 miRNA-disease association entries from literatures. In comparison to HMDD v3.0 released five years ago, HMDD v4.0 contains 1.5 times more entries. In addition, some new categories have been curated, including exosomal miRNAs implicated in diseases, virus-encoded miRNAs involved in human diseases, and entries containing miRNA-circRNA interactions. We also curated sex-biased miRNAs in diseases. Furthermore, in a case study, disease similarity analysis successfully revealed that sex-biased miRNAs related to developmental anomalies are associated with a number of human diseases with sex bias. HMDD can be freely visited at http://www.cuilab.cn/hmdd.

Curcumin modulates cell type-specific miRNA networks to induce cytotoxicity in ovarian cancer cells

AIM

To understand the epigenetic role of curcumin, a natural polyphenolic compound extracted from the spice Curcuma longa in inducing cytotoxicity in two molecularly distinct ovarian cancer cell lines: PA1 and A2780.

MATERIALS AND METHODS

An integrated mRNA-miRNA sequence analysis was performed to determine the curcumin-induced mRNA-miRNA regulatory networks in the induction of cytotoxicity. The miRNA-mRNA pathways, the miRNAs and their targets implicated in apoptosis, autophagy, DNA damage, and stemness markers were validated. Gene/miRNA expressions were validated using qPCR and protein expressions by western blotting. Curcumin-induced oncogenic /tumor-suppressor miRNAs were profiled utilising the oncomiRdb database. Similarly, the expressions of oncogenes/tumor suppressor genes were profiled and correlated with the TCGA ovarian cancer dataset. A dual luciferase assay was performed to investigate the interaction of miR-199a-5p to its direct target, DDR1.

KEY FINDINGS

The expression of several miRNAs demonstrated an inverse correlation with their respective direct targets. In curcumin-treated PA1 cells, miR-335-5p target ATG5 (autophagic), and OCT4 (pluripotent gene) were downregulated, miR-32a target PTEN (tumor suppressor) was upregulated, miR-1285 target P53 (tumor suppressor) was upregulated, and both miR-182-5p and miR-503-3p target BCL2, were down-regulated. Contrastingly, in curcumin-treated A2780 cells, miR-181a-3p target ATG5, miR-30a-5p, and miR-216a target BECN1 (autophagic) were upregulated, and miR-129a-5p target BCL2 were downregulated. The reversal of the oncomiR/TSmiR profile revealed suppression of oncogenic processes by curcumin. Curcumin treatment induced a moderate cisplatin-sensitisation effect and impaired epithelial-to-mesenchymal transition (EMT) characteristics. Curcumin also regulated the miR-199a-5p/DDR1 axis with a decrease in collagen deposition.

SIGNIFICANCE

The activity of curcumin is cell-type specific. Distinct miRNA regulatory networks were activated to induce multiple modes of cellular cytotoxicity in these ovarian cancer cells. This study further highlights the molecular mechanism of curcumin action in ovarian cancers establishing its candidacy as a promising drug candidate.

Identifying the driver miRNAs with somatic copy number alterations driving dysregulated ceRNA networks in cancers

BACKGROUND

MicroRNAs (miRNAs) play critical roles in cancer initiation and progression, which were critical components to maintain the dynamic balance of competing endogenous RNA (ceRNA) networks. Somatic copy number alterations (SCNAs) in the cancer genome could disturb the transcriptome level of miRNA to deregulate this balance. However, the driving effects of SCNAs of miRNAs were insufficiently understood.

METHODS

In this study, we proposed a method to dissect the functional roles of miRNAs under different copy number states and identify driver miRNAs by integrating miRNA SCNAs profile, miRNA-target relationships and expression profiles of miRNA, mRNA and lncRNA.

RESULTS

Applying our method to 813 TCGA breast cancer (BRCA) samples, we identified 29 driver miRNAs whose SCNAs significantly and concordantly regulated their own expression levels and further inversely dysregulated expression levels of their targets or disturbed the miRNA-target networks they directly involved. Based on miRNA-target networks, we further constructed dynamic ceRNA networks driven by driver SCNAs of miRNAs and identified three different patterns of SCNA interference in the miRNA-mediated dynamic ceRNA networks. Survival analysis of driver miRNAs showed that high-level amplifications of four driver miRNAs (including has-miR-30d-3p, has-mir-30b-5p, has-miR-30d-5p and has-miR-151a-3p) in 8q24 characterized a new BRCA subtype with poor prognosis and contributed to the dysfunction of cancer-associated hallmarks in a complementary way. The SCNAs of driver miRNAs across different cancer types contributed to the cancer development by dysregulating different components of the same cancer hallmarks, suggesting the cancer specificity of driver miRNA.

CONCLUSIONS

These results demonstrate the efficacy of our method in identifying driver miRNAs and elucidating their functional roles driven by endogenous SCNAs, which is useful for interpreting cancer genomes and pathogenic mechanisms.

A comprehensive review and evaluation of graph neural networks for non-coding RNA and complex disease associations

Non-coding RNAs (ncRNAs) play a critical role in the occurrence and development of numerous human diseases. Consequently, studying the associations between ncRNAs and diseases has garnered significant attention from researchers in recent years. Various computational methods have been proposed to explore ncRNA-disease relationships, with Graph Neural Network (GNN) emerging as a state-of-the-art approach for ncRNA-disease association prediction. In this survey, we present a comprehensive review of GNN-based models for ncRNA-disease associations. Firstly, we provide a detailed introduction to ncRNAs and GNNs. Next, we delve into the motivations behind adopting GNNs for predicting ncRNA-disease associations, focusing on data structure, high-order connectivity in graphs and sparse supervision signals. Subsequently, we analyze the challenges associated with using GNNs in predicting ncRNA-disease associations, covering graph construction, feature propagation and aggregation, and model optimization. We then present a detailed summary and performance evaluation of existing GNN-based models in the context of ncRNA-disease associations. Lastly, we explore potential future research directions in this rapidly evolving field. This survey serves as a valuable resource for researchers interested in leveraging GNNs to uncover the complex relationships between ncRNAs and diseases.

Inducing apoptosis by using microRNA in radio-resistant prostate cancer: an in-silico study with an in-vitro validation

BACKGROUND

One of the problems with radiation therapy (RT) is that prostate tumor cells are often radio-resistant, which results in treatment failure. This study aimed to determine the procedure involved in radio-resistant prostate cancer apoptosis. For a deeper insight, we devoted a novel bioinformatics approach to analyze the targeting between microRNAs and radio-resistant prostate cancer genes.

METHOD

This study uses the Tarbase, and the Mirtarbase databases as validated experimental databases and mirDIP as a predicted database to identify microRNAs that target radio-resistant anti-apoptotic genes. These genes are used to construct the radio-resistant prostate cancer genes network using the online tool STRING. The validation of causing apoptosis by using microRNA was confirmed with flow cytometry of Annexin V.

RESULTS

The anti-apoptotic gene of radio-resistant prostate cancer included BCL-2, MCL1, XIAP, STAT3, NOTCH1, REL, REL B, BIRC3, and AKT1 genes. These genes were identified as anti-apoptotic genes for radio-resistant prostate cancer. The crucial microRNA that knockdown all of these genes was hsa-miR-7-5p. The highest rate of apoptotic cells in a cell transfected with hsa-miR-7-5p was (32.90 ± 1.49), plenti III (21.99 ± 3.72), and the control group (5.08 ± 0.88) in 0 Gy (P < 0.001); also, this rate was in miR-7-5p (47.01 ± 2.48), plenti III (33.79 ± 3.40), and the control group (16.98 ± 3.11) (P < 0.001) for 4 Gy.

CONCLUSION

The use of this new treatment such as gene therapy to suppress genes involved in apoptosis can help to improve the treatment results and increase the quality of life of patients with prostate cancer.

KATZNCP: a miRNA-disease association prediction model integrating KATZ algorithm and network consistency projection

BACKGROUND

Clinical studies have shown that miRNAs are closely related to human health. The study of potential associations between miRNAs and diseases will contribute to a profound understanding of the mechanism of disease development, as well as human disease prevention and treatment. MiRNA-disease associations predicted by computational methods are the best complement to biological experiments.

RESULTS

In this research, a federated computational model KATZNCP was proposed on the basis of the KATZ algorithm and network consistency projection to infer the potential miRNA-disease associations. In KATZNCP, a heterogeneous network was initially constructed by integrating the known miRNA-disease association, integrated miRNA similarities, and integrated disease similarities; then, the KATZ algorithm was implemented in the heterogeneous network to obtain the estimated miRNA-disease prediction scores. Finally, the precise scores were obtained by the network consistency projection method as the final prediction results. KATZNCP achieved the reliable predictive performance in leave-one-out cross-validation (LOOCV) with an AUC value of 0.9325, which was better than the state-of-the-art comparable algorithms. Furthermore, case studies of lung neoplasms and esophageal neoplasms demonstrated the excellent predictive performance of KATZNCP.

CONCLUSION

A new computational model KATZNCP was proposed for predicting potential miRNA-drug associations based on KATZ and network consistency projections, which can effectively predict the potential miRNA-disease interactions. Therefore, KATZNCP can be used to provide guidance for future experiments.

GKLOMLI: a link prediction model for inferring miRNA-lncRNA interactions by using Gaussian kernel-based method on network profile and linear optimization algorithm

BACKGROUND

The limited knowledge of miRNA-lncRNA interactions is considered as an obstruction of revealing the regulatory mechanism. Accumulating evidence on Human diseases indicates that the modulation of gene expression has a great relationship with the interactions between miRNAs and lncRNAs. However, such interaction validation via crosslinking-immunoprecipitation and high-throughput sequencing (CLIP-seq) experiments that inevitably costs too much money and time but with unsatisfactory results. Therefore, more and more computational prediction tools have been developed to offer many reliable candidates for a better design of further bio-experiments.

METHODS

In this work, we proposed a novel link prediction model based on Gaussian kernel-based method and linear optimization algorithm for inferring miRNA-lncRNA interactions (GKLOMLI). Given an observed miRNA-lncRNA interaction network, the Gaussian kernel-based method was employed to output two similarity matrixes of miRNAs and lncRNAs. Based on the integrated matrix combined with similarity matrixes and the observed interaction network, a linear optimization-based link prediction model was trained for inferring miRNA-lncRNA interactions.

RESULTS

To evaluate the performance of our proposed method, k-fold cross-validation (CV) and leave-one-out CV were implemented, in which each CV experiment was carried out 100 times on a training set generated randomly. The high area under the curves (AUCs) at 0.8623 ± 0.0027 (2-fold CV), 0.9053 ± 0.0017 (5-fold CV), 0.9151 ± 0.0013 (10-fold CV), and 0.9236 (LOO-CV), illustrated the precision and reliability of our proposed method.

CONCLUSION

GKLOMLI with high performance is anticipated to be used to reveal underlying interactions between miRNA and their target lncRNAs, and deciphers the potential mechanisms of the complex diseases.

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.

RNADisease v4.0: an updated resource of RNA-associated diseases, providing RNA-disease analysis, enrichment and prediction

Numerous studies have shown that RNA plays an important role in the occurrence and development of diseases, and RNA-disease associations are not limited to noncoding RNAs in mammals but also exist for protein-coding RNAs. Furthermore, RNA-associated diseases are found across species including plants and nonmammals. To better analyze diseases at the RNA level and facilitate researchers in exploring the pathogenic mechanism of diseases, we decided to update and change MNDR v3.0 to RNADisease v4.0, a repository for RNA-disease association (http://www.rnadisease.org/ or http://www.rna-society.org/mndr/). Compared to the previous version, new features include: (i) expanded data sources and categories of species, RNA types, and diseases; (ii) the addition of a comprehensive analysis of RNAs from thousands of high-throughput sequencing data of cancer samples and normal samples; (iii) the addition of an RNA-disease enrichment tool and (iv) the addition of four RNA-disease prediction tools. In summary, RNADisease v4.0 provides a comprehensive and concise data resource of RNA-disease associations which contains a total of 3 428 058 RNA-disease entries covering 18 RNA types, 117 species and 4090 diseases to meet the needs of biological research and lay the foundation for future therapeutic applications of diseases.

miRNA therapeutics in precision oncology: a natural premium to nurture

The dynamic spectrum of microRNA (miRNA) has grown significantly over the years with its identification and exploration in cancer therapeutics and is currently identified as an important resource for innovative strategies due to its functional behavior for gene regulation and modulation of complex biological networks. The progression of cancer is the consequence of uncontrolled, nonsynchronous procedural faults in the biological system. Diversified and variable cellular response of cancerous cells has always raised challenges in effective cancer therapy. miRNAs, a class of non-coding RNAs (ncRNAs), are the natural genetic gift, responsible to preserve the homeostasis of cell to nurture. The unprecedented significance of endogenous miRNAs has exhibited promising therapeutic potential in cancer therapeutics. Currently, miRNA mimic miR-34, and an antimiR aimed against miR-122 has entered the clinical trials for cancer treatments. This review, highlights the recent breakthroughs, challenges, clinical trials, and advanced delivery vehicles in the administration of miRNA therapies for precision oncology.

Role of MicroRNA-7 (MiR-7) in Cancer Physiopathology

miRNAs are non-coding RNA sequences of approximately 22 nucleotides that interact with genes by inhibiting their translation through binding to their 3′ or 5′ UTR regions. Following their discovery, the role they play in the development of various pathologies, particularly cancer, has been studied. In this context, miR-7 is described as an important factor in the development of cancer because of its role as a tumor suppressor, regulating a large number of genes involved in the development and progression of cancer. Recent data support the function of miR-7 as a prognostic biomarker in cancer, and miR-7 has been proposed as a strategy in cancer therapy. In this work, the role of miR-7 in various types of cancer is reviewed, illustrating its regulation, direct targets, and effects, as well as its possible relationship to the clinical outcome of cancer patients.

dbDEMC 3.0: Functional Exploration of Differentially Expressed miRNAs in Cancers of Human and Model Organisms

MicroRNAs (miRNAs) are important regulators in gene expression. The dysregulation of miRNA expression is widely reported in the transformation from physiological to pathological states of cells. A large number of differentially expressed miRNAs (DEMs) have been identified in various human cancers by using high-throughput technologies, such as microarray and miRNA-seq. Through mining of published studies with high-throughput experiment information, the database of DEMs in human cancers (dbDEMC) was constructed with the aim of providing a systematic resource for the storage and query of the DEMs. Here we report an update of the dbDEMC to version 3.0, which contains two-fold more data entries than the second version and now includes also data from mice and rats. The dbDEMC 3.0 contains 3268 unique DEMs in 40 different cancer types. The current datasets for differential expression analysis have expanded to 9 generalized categories. Moreover, the current release integrates functional annotations of DEMs obtained by using experimentally validated targets. The annotations can be of great benefit to the intensive analysis of the roles of DEMs in cancer. In summary, dbDEMC 3.0 provides a valuable resource for characterizing molecular functions and regulatory mechanisms of DEMs in human cancers. The dbDEMC 3.0 is freely accessible at https://www.biosino.org/dbDEMC.

PDMDA: predicting deep-level miRNA-disease associations with graph neural networks and sequence features

MOTIVATION

Many studies have shown that microRNAs (miRNAs) play a key role in human diseases. Meanwhile, traditional experimental methods for miRNA-disease association identification are extremely costly, time-consuming and challenging. Therefore, many computational methods have been developed to predict potential associations between miRNAs and diseases. However, those methods mainly predict the existence of miRNA-disease associations, and they cannot predict the deep-level miRNA-disease association types.

RESULTS

In this study, we propose a new end-to-end deep learning method (called PDMDA) to predict deep-level miRNA-disease associations with graph neural networks (GNNs) and miRNA sequence features. Based on the sequence and structural features of miRNAs, PDMDA extracts the miRNA feature representations by a fully connected network (FCN). The disease feature representations are extracted from the disease-gene network and gene-gene interaction network by GNN model. Finally, a multilayer with three fully connected layers and a softmax layer is designed to predict the final miRNA-disease association scores based on the concatenated feature representations of miRNAs and diseases. Note that PDMDA does not take the miRNA-disease association matrix as input to compute the Gaussian interaction profile similarity. We conduct three experiments based on six association type samples (including circulations, epigenetics, target, genetics, known association of which their types are unknown and unknown association samples). We conduct fivefold cross-validation validation to assess the prediction performance of PDMDA. The area under the receiver operating characteristic curve scores is used as metric. The experiment results show that PDMDA can accurately predict the deep-level miRNA-disease associations.

AVAILABILITY AND IMPLEMENTATION

Data and source codes are available at https://github.com/27167199/PDMDA.

Computing microRNA-gene interaction networks in pan-cancer using miRDriver

DNA copy number aberrated regions in cancer are known to harbor cancer driver genes and the short non-coding RNA molecules, i.e., microRNAs. In this study, we integrated the multi-omics datasets such as copy number aberration, DNA methylation, gene and microRNA expression to identify the signature microRNA-gene associations from frequently aberrated DNA regions across pan-cancer utilizing a LASSO-based regression approach. We studied 7294 patient samples associated with eighteen different cancer types from The Cancer Genome Atlas (TCGA) database and identified several cancer-specific and common microRNA-gene interactions enriched in experimentally validated microRNA-target interactions. We highlighted several oncogenic and tumor suppressor microRNAs that were cancer-specific and common in several cancer types. Our method substantially outperformed the five state-of-art methods in selecting significantly known microRNA-gene interactions in multiple cancer types. Several microRNAs and genes were found to be associated with tumor survival and progression. Selected target genes were found to be significantly enriched in cancer-related pathways, cancer hallmark and Gene Ontology (GO) terms. Furthermore, subtype-specific potential gene signatures were discovered in multiple cancer types.

Integrated multi-omics analysis reveals miR-20a as a regulator for metabolic colorectal cancer

Single-driver molecular events specific to the metabolic colorectal cancer (CRC) have not been clearly elucidated. Herein, we identified 12 functional miRNAs linked to activated metabolism by integrating multi-omics features in metabolic CRC. These miRNAs exhibited significantly enriched CRC driver miRNAs, significant impacts on CRC cell growth and significantly correlated metabolites. Importantly, miR-20a is minimally expressed in normal colorectal tissues but highly expressed in metabolic CRC, suggesting the potential therapeutic target. Bioinformatics analyses further revealed miR-20a as the most powerful determinant that regulates a cascade of dysregulated events, including Wnt signaling pathway, core enzymes involved in FA metabolism program and triacylglycerol abundances. In vitro assays demonstrated that elevated miR-20a up-regulated FA synthesis enzymes via Wnt/β-catenin signaling, and finally promoted proliferative and migration of metabolic CRC cells. Overall, our study revealed that miR-20a promoted progression of metabolic CRC by regulating FA metabolism and served as a potential target for preventing tumor metastasis.

MicroRNA let-7 and viral infections: focus on mechanisms of action

MicroRNAs (miRNAs) are fundamental post-transcriptional modulators of several critical cellular processes, a number of which are involved in host defense mechanisms. In particular, miRNA let-7 functions as an essential regulator of the function and differentiation of both innate and adaptive immune cells. Let-7 is involved in several human diseases, including cancer and viral infections. Several viral infections have found ways to dysregulate the expression of miRNAs. Extracellular vesicles (EV) are membrane-bound lipid structures released from many types of human cells that can transport proteins, lipids, mRNAs, and miRNAs, including let-7. After their release, EVs are taken up by the recipient cells and their contents released into the cytoplasm. Let-7-loaded EVs have been suggested to affect cellular pathways and biological targets in the recipient cells, and can modulate viral replication, the host antiviral response, and the action of cancer-related viruses. In the present review, we summarize the available knowledge concerning the expression of let-7 family members, functions, target genes, and mechanistic involvement in viral pathogenesis and host defense. This may provide insight into the development of new therapeutic strategies to manage viral infections.

MicroRNA Targeting

MicroRNAs (miRNAs) are small noncoding elements that play essential roles in the posttranscriptional regulation of biochemical processes. miRNAs recognize and target multiple mRNAs; therefore, investigating miRNA dysregulation is an indispensable strategy to understand pathological conditions and to design innovative drugs. Targeting miRNAs in diseases improve outcomes of several therapeutic strategies thus, this present study highlights miRNA targeting methods through experimental assays and bioinformatics tools. The first part of this review focuses on experimental miRNA targeting approaches for elucidating key biochemical pathways. A growing body of evidence about the miRNA world reveals the fact that it is not possible to uncover these molecules' structural and functional characteristics related to the biological processes with a deterministic approach. Instead, a systemic point of view is needed to truly understand the facts behind the natural complexity of interactions and regulations that miRNA regulations present. This task heavily depends both on computational and experimental capabilities. Fortunately, several miRNA bioinformatics tools catering to nonexperts are available as complementary wet-lab approaches. For this purpose, this work provides recent research and information about computational tools for miRNA targeting research.

Circular RNA circCPA4 promotes tumorigenesis by regulating miR-214-3p/TGIF2 in lung cancer

Background

Lung cancer is the most prevalent malignancy in adults. Circular RNA (circRNA) circCPA4 (hsa_circ_0082374) is highly expressed in non‐small cell lung cancer (NSCLC). The purpose of this study was to explore the role and mechanism of circCPA4 in lung cancer.

Methods

CircCPA4, linear CPA4, TGF‐β‐induced factor homeobox 2 (TGIF2), and microRNA‐214‐3p (miR‐214‐3p) levels were measured by real‐time quantitative polymerase chain reaction (RT‐qPCR). The protein levels of TGIF2, Beclin1, and p62 were assessed by western blot assay. Colony numbers, migration, invasion, apoptosis, and cell cycle progression were examined by colony formation, wound‐healing, transwell, and flow cytometry assays, respectively. The binding relationship between miR‐214‐3p and circCPA4 or TGIF2 was predicted by StarBase or TargetScan and then verified by a dual‐luciferase reporter, RNA immunoprecipitation (RIP), and RNA pulldown assays. The biological role of circCPA4 on lung tumor growth was assessed by a xenograft tumor model in vivo, and TGIF2 and ki‐67 expression was assessed by immunohistochemistry.

Results

We determined that CircCPA4 and TGIF2 were increased, and miR‐214‐3p was decreased in lung cancer tissues and cells. Functionally, circCPA4 knockdown could suppress colony formation, migration, invasion, cell cycle progression, and expedite apoptosis of lung cancer cells in vitro. Mechanically, circCPA4 could regulate TGIF2 expression by sponging miR‐214‐3p. In addition, circCPA4 deficiency inhibited the tumor growth in lung cancer in the mouse model.

Conclusions

CircCPA4 could act as a sponge of miR‐214‐3p to upregulate TGIF2 expression, thereby promoting the progression of lung cancer cells. These findings suggested underlying therapeutic targets for the treatment of lung cancer.

Screening and cellular validation of prognostic genes regulated by super enhancers in oral squamous cell carcinoma

Oral squamous cell carcinoma (OSCC) is the leading cause of death in patients with head and neck cancer. Reliable biomarkers to guide treatment decisions for OSCC remain scarce. The purpose of this study was to identify novel prognostic markers regulated by super enhancers in OSCC. Eight modules were obtained by weighted gene co-expression network analysis (WGCNA), among which MEblue module had the highest correlation with tumor stage, alcohol consumption and smoking. There were 41 genes regulated by super enhancers in MEblue module. Functional analysis showed that 41 super enhancer-regulated genes were involved in cancer progression. A total of twenty transcription factors of the 41 genes were predicted. Prognostic analysis of the 41 genes and the top 5 transcription factors showed that patients with high expression of AHCY, KCMF1, MANBAL and TFDP1 had a poor prognosis. Immunohistochemical analysis showed that AHCY, KCMF1 and MANBAL were highly expressed in OSCC tissue. Cellular experiment demonstrated that TFDP1 promoted AHCY, KCMF1 and MANBAL expression by binding to the super enhancers of these genes. Knockdown of TFDP1, AHCY, KCMF1 and MANBAL inhibited the proliferation of OSCC cells. In conclusion, AHCY, KCMF1 and MANBAL were recognized as super enhancer-regulated prognostic biomarkers regulated by TFDP1 in OSCC.

emiRIT: a text-mining-based resource for microRNA information

microRNAs (miRNAs) are essential gene regulators, and their dysregulation often leads to diseases. Easy access to miRNA information is crucial for interpreting generated experimental data, connecting facts across publications and developing new hypotheses built on previous knowledge. Here, we present extracting miRNA Information from Text (emiRIT), a text-miningbased resource, which presents miRNA information mined from the literature through a user-friendly interface. We collected 149 ,233 miRNA –PubMed ID pairs from Medline between January 1997 and May 2020. emiRIT currently contains ‘miRNA –gene regulation’ (69 ,152 relations), ‘miRNA disease (cancer)’ (12 ,300 relations), ‘miRNA –biological process and pathways’ (23, 390 relations) and circulatory ‘miRNAs in extracellular locations’ (3782 relations). Biological entities and their relation to miRNAs were extracted from Medline abstracts using publicly available and in-house developed text-mining tools, and the entities were normalized to facilitate querying and integration. We built a database and an interface to store and access the integrated data, respectively. We provide an up-to-date and user-friendly resource to facilitate access to comprehensive miRNA information from the literature on a large scale, enabling users to navigate through different roles of miRNA and examine them in a context specific to their information needs. To assess our resource’s information coverage, we have conducted two case studies focusing on the target and differential expression information of miRNAs in the context of cancer and a third case study to assess the usage of emiRIT in the curation of miRNA information.

Database URL: https://research.bioinformatics.udel.edu/emirit/

A Super-Enhancer Driven by FOSL1 Controls miR-21-5p Expression in Head and Neck Squamous Cell Carcinoma

MiR-21-5p is one of the most common oncogenic miRNAs that is upregulated in many solid cancers by inhibiting its target genes at the posttranscriptional level. However, the upstream regulatory mechanisms of miR-21-5p are still not well documented in cancers. Here, we identify a super-enhancer associated with the MIR21 gene (MIR21-SE) by analyzing the MIR21 genomic regulatory landscape in head and neck squamous cell carcinoma (HNSCC). We show that the MIR21-SE regulates miR-21-5p expression in different HNSCC cell lines and disruption of MIR21-SE inhibits miR-21-5p expression. We also identified that a key transcription factor, FOSL1 directly controls miR-21-5p expression by interacting with the MIR21-SE in HNSCC. Moreover, functional studies indicate that restoration of miR-21-5p partially abrogates FOSL1 depletion-mediated inhibition of cell proliferation and invasion. Clinical studies confirmed that miR-21-5p expression is positively correlated with FOSL1 expression. These findings suggest that FOSL1-SE drives miR-21-5p expression to promote malignant progression of HNSCC

Approaches to Identify and Characterise the Post-Transcriptional Roles of lncRNAs in Cancer

It is becoming increasingly evident that the non-coding genome and transcriptome exert great influence over their coding counterparts through complex molecular interactions. Among non-coding RNAs (ncRNA), long non-coding RNAs (lncRNAs) in particular present increased potential to participate in dysregulation of post-transcriptional processes through both RNA and protein interactions. Since such processes can play key roles in contributing to cancer progression, it is desirable to continue expanding the search for lncRNAs impacting cancer through post-transcriptional mechanisms. The sheer diversity of mechanisms requires diverse resources and methods that have been developed and refined over the past decade. We provide an overview of computational resources as well as proven low-to-high throughput techniques to enable identification and characterisation of lncRNAs in their complex interactive contexts. As more cancer research strategies evolve to explore the non-coding genome and transcriptome, we anticipate this will provide a valuable primer and perspective of how these technologies have matured and will continue to evolve to assist researchers in elucidating post-transcriptional roles of lncRNAs in cancer.

Single-cell sequencing reveals the potential oncogenic expression atlas of human iPSC-derived cardiomyocytes

Human induced pluripotent stem cells (iPSCs) are important source for regenerative medicine. However, the links between pluripotency and oncogenic transformation raise safety issues. To understand the characteristics of iPSC-derived cells at single-cell resolution, we directly reprogrammed two human iPSC lines into cardiomyocytes and collected cells from four time points during cardiac differentiation for single-cell sequencing. We captured 32,365 cells and identified five molecularly distinct clusters that aligned well with our reconstructed differentiation trajectory. We discovered a set of dynamic expression events related to the upregulation of oncogenes and the decreasing expression of tumor suppressor genes during cardiac differentiation, which were similar to the gain-of-function and loss-of-function patterns during oncogenesis. In practice, we characterized the dynamic expression of the TP53 and Yamanaka factor genes (OCT4, SOX2, KLF4 and MYC), which were widely used for human iPSCs lines generation; and revealed the co-occurrence of MYC overexpression and TP53 silencing in some of human iPSC-derived TNNT2+ cardiomyocytes. In summary, our oncogenic expression atlas is valuable for human iPSCs application and the single-cell resolution highlights the clues potentially associated with the carcinogenic risk of human iPSC-derived cells.

Summary: The single-cell expression atlas in the cardiomyocytes generated from human iPSCs provide potential carcinogenic information for the clinical application of human iPSC-derived cells.

MNDR v3.0: mammal ncRNA-disease repository with increased coverage and annotation

Many studies have indicated that non-coding RNA (ncRNA) dysfunction is closely related to numerous diseases. Recently, accumulated ncRNA-disease associations have made related databases insufficient to meet the demands of biomedical research. The constant updating of ncRNA-disease resources has become essential. Here, we have updated the mammal ncRNA-disease repository (MNDR, http://www.rna-society.org/mndr/) to version 3.0, containing more than one million entries, four-fold increment in data compared to the previous version. Experimental and predicted circRNA-disease associations have been integrated, increasing the number of categories of ncRNAs to five, and the number of mammalian species to 11. Moreover, ncRNA-disease related drug annotations and associations, as well as ncRNA subcellular localizations and interactions, were added. In addition, three ncRNA-disease (miRNA/lncRNA/circRNA) prediction tools were provided, and the website was also optimized, making it more practical and user-friendly. In summary, MNDR v3.0 will be a valuable resource for the investigation of disease mechanisms and clinical treatment strategies.

Targeting Akt-associated microRNAs for cancer therapeutics

The uncontrolled growth and spread of abnormal cells because of activating protooncogenes and/or inactivating tumor suppressor genes are the hallmarks of cancer. The PI3K/Akt signaling is one of the most frequently activated pathways in cancer cells responsible for the regulation of cell survival and proliferation in stress and hypoxic conditions during oncogenesis. Non-coding RNAs are a large family of RNAs that are not involved in protein-coding, and microRNAs (miRNAs) are a sub-set of non-coding RNAs with a single strand of 18-25 nucleotides. miRNAs are extensively involved in the post-transcriptional regulation of gene expression and play an extensive role in the regulatory mechanisms including cell differentiation, proliferation, apoptosis, and tumorigenesis. The impact of cancer on mRNA stability and translation efficiency is extensive and therefore, cancerous tissues exhibit drastic alterations in the expression of miRNAs. miRNAs can be modulated by utilizing techniques such as miRNA mimics, miRNA antagonists, or CRISPR/Cas9. In addition to their capacity as potential targets in cancer therapy, they can be used as reliable biomarkers to diagnose the disease at the earliest stage. Recent evidence indicates that microRNA-mediated gene regulation intersects with the Akt pathway, forming an Akt-microRNA regulatory network. miRNAs and Akt in this network operate together to exert their cellular tasks. In the current review, we discuss the Akt-associated miRNAs in several cancers, their molecular regulation, and how this newly emerging knowledge may contribute greatly to revolutionize cancer therapy.

Construction and Characterization of a Synergistic lncRNA-miRNA Network Reveals a Crucial and Prognostic Role of lncRNAs in Colon Cancer

Non-coding RNAs such as long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) have been found to be indispensable factors in carcinogenesis and cancer development. Numerous studies have explored the regulatory functions of these molecules and identified the synergistic interactions among lncRNAs or miRNAs, while those between lncRNAs and miRNAs remain to be investigated. In this study, we constructed and characterized an lncRNA–miRNA synergistic network following a four-step approach by integrating the regulatory pairs and expression profiles. The synergistic interactions with more shared regulatory mRNAs were found to have higher interactional intensity. Through the analysis of nodes in the network, we found that lncRNAs played roles that are more central and had similar synergistic interactions with their neighbors when compared with miRNAs. In addition, known colon adenocarcinoma (COAD)-related RNAs were found to be enriched in this synergistic network, with higher degrees, betweenness, and closeness. Finally, we proposed a risk score model to predict the clinical outcome for COAD patients based on two prognostic hub lncRNAs, MEG3 and ZEB1-AS1. Moreover, the hierarchical networks of these two lncRNAs could contribute to the understanding of the biological mechanism of tumorigenesis. For each lncRNA–miRNA interaction in the hub-related subnetwork and two hierarchical networks, we performed RNAup method to evaluate their binding energy. Our results identified two important lncRNAs with prognostic roles in colon cancer and dissected their regulatory mechanism involving synergistic interaction with miRNAs.

MiRACLe: an individual-specific approach to improve microRNA-target prediction based on a random contact model

Deciphering microRNA (miRNA) targets is important for understanding the function of miRNAs as well as miRNA-based diagnostics and therapeutics. Given the highly cell-specific nature of miRNA regulation, recent computational approaches typically exploit expression data to identify the most physiologically relevant target messenger RNAs (mRNAs). Although effective, those methods usually require a large sample size to infer miRNA-mRNA interactions, thus limiting their applications in personalized medicine. In this study, we developed a novel miRNA target prediction algorithm called miRACLe (miRNA Analysis by a Contact modeL). It integrates sequence characteristics and RNA expression profiles into a random contact model, and determines the target preferences by relative probability of effective contacts in an individual-specific manner. Evaluation by a variety of measures shows that fitting TargetScan, a frequently used prediction tool, into the framework of miRACLe can improve its predictive power with a significant margin and consistently outperform other state-of-the-art methods in prediction accuracy, regulatory potential and biological relevance. Notably, the superiority of miRACLe is robust to various biological contexts, types of expression data and validation datasets, and the computation process is fast and efficient. Additionally, we show that the model can be readily applied to other sequence-based algorithms to improve their predictive power, such as DIANA-microT-CDS, miRanda-mirSVR and MirTarget4. MiRACLe is publicly available at https://github.com/PANWANG2014/miRACLe.

ncRPheno: a comprehensive database platform for identification and validation of disease related noncoding RNAs

Noncoding RNAs (ncRNAs) play critical roles in many critical biological processes and have become a novel class of potential targets and bio-markers for disease diagnosis, therapy, and prognosis. Annotating and analysing ncRNA-disease association data are essential but challenging. Current computational resources lack comprehensive database platforms to consistently interpret and prioritize ncRNA-disease association data for biomedical investigation and application. Here, we present the ncRPheno database platform (http://lilab2.sysu.edu.cn/ncrpheno), which comprehensively integrates and annotates ncRNA-disease association data and provides novel searches, visualizations, and utilities for association identification and validation. ncRPheno contains 482,751 non-redundant associations between 14,494 ncRNAs and 3,210 disease phenotypes across 11 species with supporting evidence in the literature. A scoring model was refined to prioritize the associations based on evidential metrics. Moreover, ncRPheno provides user-friendly web interfaces, novel visualizations, and programmatic access to enable easy exploration, analysis, and utilization of the association data. A case study through ncRPheno demonstrated a comprehensive landscape of ncRNAs dysregulation associated with 22 cancers and uncovered 821 cancer-associated common ncRNAs. As a unique database platform, ncRPheno outperforms the existing similar databases in terms of data coverage and utilities, and it will assist studies in encoding ncRNAs associated with phenotypes ranging from genetic disorders to complex diseases.

ABBREVIATIONS

APIs: application programming interfaces; circRNA: circular RNA; ECO: Evidence & Conclusion Ontology; EFO: Experimental Factor Ontology; FDR: false discovery rate; GO: Gene Ontology; GWAS: genome wide association studies; HPO: Human Phenotype Ontology; ICGC: International Cancer Genome Consortium; lncRNA: long noncoding RNA; miRNA: micro RNA; ncRNA: noncoding RNA; NGS: next generation sequencing; OMIM: Online Mendelian Inheritance in Man; piRNA: piwi-interacting RNA; snoRNA: small nucleolar RNA; TCGA: The Cancer Genome Atlas.

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.

PESM: predicting the essentiality of miRNAs based on gradient boosting machines and sequences

Background

MicroRNAs (miRNAs) are a kind of small noncoding RNA molecules that are direct posttranscriptional regulations of mRNA targets. Studies have indicated that miRNAs play key roles in complex diseases by taking part in many biological processes, such as cell growth, cell death and so on. Therefore, in order to improve the effectiveness of disease diagnosis and treatment, it is appealing to develop advanced computational methods for predicting the essentiality of miRNAs.

Result

In this study, we propose a method (PESM) to predict the miRNA essentiality based on gradient boosting machines and miRNA sequences. First, PESM extracts the sequence and structural features of miRNAs. Then it uses gradient boosting machines to predict the essentiality of miRNAs. We conduct the 5-fold cross-validation to assess the prediction performance of our method. The area under the receiver operating characteristic curve (AUC), F-measure and accuracy (ACC) are used as the metrics to evaluate the prediction performance. We also compare PESM with other three competing methods which include miES, Gaussian Naive Bayes and Support Vector Machine.

Conclusion

The results of experiments show that PESM achieves the better prediction performance (AUC: 0.9117, F-measure: 0.8572, ACC: 0.8516) than other three computing methods. In addition, the relative importance of all features also further shows that newly added features can be helpful to improve the prediction performance of methods.

Comprehensive analysis of miRNA-gene regulatory network with clinical significance in human cancers

microRNAs (miRNAs), particularly the exosomal miRNAs have been widely used as biomarkers and promising therapeutic targets in cancer. However, a comprehensive analysis of miRNA-gene regulatory network with clinical significance remains scarce. The emergence of high-throughput multi-omics data over large, well-characterized patient cohorts provides an unprecedented opportunity to address this problem. Herein, we performed a clinic-centered analysis to identify cancer-associated miRNAs, miRNA-target axis. We first calculated the correlation among miRNA, mRNA and 75 unique clinico-pathological characteristics (CPCs) in 26 cancer types, and established an online resource (4CR). Interestingly, we found that the high expression of several DNA methylation-related enzymes was associated with adverse outcomes of cancer patients, and these genes were regulated by a cluster of miRNAs. Furthermore, by integrating exosomal miRNA and mRNA databases, we identified exosomal miRNA biomarkers for non-invasive cancer surveillance and therapy monitoring. Finally, we explored the role of CPC-related miRNAs for therapeutic effect prediction of drugs based on their shared targets. Our analysis pipeline illustrated the significance of clinic-centered analysis in miRNA-gene pair identification and provided helpful clues for future cancer studies.

RNAInter in 2020: RNA interactome repository with increased coverage and annotation

Research on RNA-associated interactions has exploded in recent years, and increasing numbers of studies are not limited to RNA-RNA and RNA-protein interactions but also include RNA-DNA/compound interactions. To facilitate the development of the interactome and promote understanding of the biological functions and molecular mechanisms of RNA, we updated RAID v2.0 to RNAInter (RNA Interactome Database), a repository for RNA-associated interactions that is freely accessible at http://www.rna-society.org/rnainter/ or http://www.rna-society.org/raid/. Compared to RAID v2.0, new features in RNAInter include (i) 8-fold more interaction data and 94 additional species; (ii) more definite annotations organized, including RNA editing/localization/modification/structure and homology interaction; (iii) advanced functions including fuzzy/batch search, interaction network and RNA dynamic expression and (iv) four embedded RNA interactome tools: RIscoper, IntaRNA, PRIdictor and DeepBind. Consequently, RNAInter contains >41 million RNA-associated interaction entries, involving more than 450 thousand unique molecules, including RNA, protein, DNA and compound. Overall, RNAInter provides a comprehensive RNA interactome resource for researchers and paves the way to investigate the regulatory landscape of cellular RNAs.

Identifying miRNA synergism using multiple-intervention causal inference

BACKGROUND

Studying multiple microRNAs (miRNAs) synergism in gene regulation could help to understand the regulatory mechanisms of complicated human diseases caused by miRNAs. Several existing methods have been presented to infer miRNA synergism. Most of the current methods assume that miRNAs with shared targets at the sequence level are working synergistically. However, it is unclear if miRNAs with shared targets are working in concert to regulate the targets or they individually regulate the targets at different time points or different biological processes. A standard method to test the synergistic activities is to knock-down multiple miRNAs at the same time and measure the changes in the target genes. However, this approach may not be practical as we would have too many sets of miRNAs to test.

RESULTS

n this paper, we present a novel framework called miRsyn for inferring miRNA synergism by using a causal inference method that mimics the multiple-intervention experiments, e.g. knocking-down multiple miRNAs, with observational data. Our results show that several miRNA-miRNA pairs that have shared targets at the sequence level are not working synergistically at the expression level. Moreover, the identified miRNA synergistic network is small-world and biologically meaningful, and a number of miRNA synergistic modules are significantly enriched in breast cancer. Our further analyses also reveal that most of synergistic miRNA-miRNA pairs show the same expression patterns. The comparison results indicate that the proposed multiple-intervention causal inference method performs better than the single-intervention causal inference method in identifying miRNA synergistic network.

CONCLUSIONS

Taken together, the results imply that miRsyn is a promising framework for identifying miRNA synergism, and it could enhance the understanding of miRNA synergism in breast cancer.

Differential network analysis depicts regulatory mechanisms for hepatocellular carcinoma from diverse backgrounds

Aim: To elucidate the integrative combinational gene regulatory network landscape of hepatocellular carcinoma (HCC) molecular carcinogenesis from diverse background. Materials & methods: Modified gene regulatory network analysis was used to prioritize differentially regulated genes and links. Integrative comparisons using bioinformatics methods were applied to identify potential critical molecules and pathways in HCC with different backgrounds. Results: E2F1 with its surrounding regulatory links were identified to play different key roles in the HCC risk factor dysregulation mechanisms. Hsa-mir-19a was identified as showed different effects in the three HCC differential regulation networks, and showed vital regulatory role in HBV-related HCC. Conclusion: We describe in detail the regulatory networks involved in HCC with different backgrounds. E2F1 may serve as a universal target for HCC treatment.

The Regulatory Roles of Non-coding RNAs in Angiogenesis and Neovascularization From an Epigenetic Perspective

Angiogenesis is a crucial process for organ morphogenesis and growth during development, and it is especially relevant during the repair of wounded tissue in adults. It is coordinated by an equilibrium of pro- and anti-angiogenic factors; nevertheless, when affected, it promotes several diseases. Lately, a growing body of evidence is indicating that non-coding RNAs (ncRNAs), such as miRNAs, circRNAs, and lncRNAs, play critical roles in angiogenesis. These ncRNAs can act in cis or trans and alter gene transcription by several mechanisms including epigenetic processes. In the following pages, we will discuss the functions of ncRNAs in the regulation of angiogenesis and neovascularization, both in normal and disease contexts, from an epigenetic perspective. Additionally, we will describe the contribution of Next-Generation Sequencing (NGS) techniques to the discovery and understanding of the role of ncRNAs in angiogenesis.

miR2Diabetes: A Literature-Curated Database of microRNA Expression Patterns, in Diabetic Microvascular Complications

microRNAs (miRNAs) have been established as critical regulators of the pathogenesis of diabetes mellitus (DM), and diabetes microvascular complications (DMCs). However, manually curated databases for miRNAs, and DM (including DMCs) association studies, have yet to be established. Here, we constructed a user-friendly database, “miR2Diabetes,” equipped with a graphical web interface for simple browsing or searching manually curated annotations. The annotations in our database cover 14 DM and DMC phenotypes, involving 156 miRNAs, by browsing diverse sample origins (e.g., blood, kidney, liver, and other tissues). Additionally, we provide miRNA annotations for disease-model organisms (including rats and mice), of DM and DMCs, for the purpose of improving knowledge of the biological complexity of these pathologies. We assert that our database will be a comprehensive resource for miRNA biomarker studies, as well as for prioritizing miRNAs for functional validation, in DM and DMCs, with likely extension to other diseases.

miRNACancerMAP: an integrative web server inferring miRNA regulation network for cancer

Summary

MicroRNAs play critical roles in oncogenesis by targeting a few key regulators or a large cohort of genes impinging on downstream signaling pathways. Conversely, miRNA activity is also titrated by competitive endogenous RNA such as lncRNA with sponge effect. Web-based server, miRNACancerMap, aims to unravel lncRNA-miRNA-mRNA tripartite complexity to predict the function and clinical relevance of miRNA with network perspective. In conjunction with large-scale data and information integration, miRNACancerMap implements various algorithms and pipelines to construct dynamic miRNA-centered network with rigorous Systems Biology approaches and the state-of-the-art visualization tool. The capability of the server to generate testable hypotheses was exemplified with cases to identify hub miRNAs regulating most of the differentially-expressed genes involved in cancer stage transition, miRNA-TF pairs shared by pan-cancers and lncRNA sponges validated by multiple datasets. LncRNAs sharing the same miRNAs binding sites as mRNAs can sequester miRNAs and indirectly regulate the activity of the related mRNAs. We have re-annotated traditional microarray chips, and included these datasets in the server to enable validation of the predicted lncRNA-miRNA-mRNA regulations derived from TCGA RNA-seq data. Of note, our server enables identifying miRNAs associated with cancer signaling pathways, and related lncRNA sponges from pan-cancers with only a few mouse clicks.

Availability and implementation

http://cis.hku.hk/miRNACancerMAP.

Supplementary information

Supplementary data are available at Bioinformatics online.

MicroRNAs and complex diseases: from experimental results to computational models

Plenty of microRNAs (miRNAs) were discovered at a rapid pace in plants, green algae, viruses and animals. As one of the most important components in the cell, miRNAs play a growing important role in various essential and important biological processes. For the recent few decades, amounts of experimental methods and computational models have been designed and implemented to identify novel miRNA-disease associations. In this review, the functions of miRNAs, miRNA-target interactions, miRNA-disease associations and some important publicly available miRNA-related databases were discussed in detail. Specially, considering the important fact that an increasing number of miRNA-disease associations have been experimentally confirmed, we selected five important miRNA-related human diseases and five crucial disease-related miRNAs and provided corresponding introductions. Identifying disease-related miRNAs has become an important goal of biomedical research, which will accelerate the understanding of disease pathogenesis at the molecular level and molecular tools design for disease diagnosis, treatment and prevention. Computational models have become an important means for novel miRNA-disease association identification, which could select the most promising miRNA-disease pairs for experimental validation and significantly reduce the time and cost of the biological experiments. Here, we reviewed 20 state-of-the-art computational models of predicting miRNA-disease associations from different perspectives. Finally, we summarized four important factors for the difficulties of predicting potential disease-related miRNAs, the framework of constructing powerful computational models to predict potential miRNA-disease associations including five feasible and important research schemas, and future directions for further development of computational models.

Cluster miRNAs and cancer: Diagnostic, prognostic and therapeutic opportunities

MiRNAs are class of noncoding RNA important for gene expression regulation in many plants, animals and viruses. MiRNA clusters contain a set of two or more miRNA encoding genes, transcribed together as polycistronic miRNAs. Currently, there are approximately 159 miRNA clusters reported in the human genome consisting of miRNAs ranging from two or more miRNA genes. A large proportion of clustered miRNAs resides in and around the fragile sites or cancer associated genomic hotspots and plays an important role in carcinogenesis. Altered expression of miRNA cluster can be pro-tumorigenic or anti-tumorigenic and can be targeted for clinical management of cancer. Over the past few years, manipulation of miRNA clusters expression is attempted for experimental purpose as well as for diagnostic, prognostic and therapeutic applications in cancer. Re-expression of miRNAs by epigenetic therapy, genome editing such as clustered regulatory interspaced short palindromic repeats (CRISPR) and miRNA mowers showed promising results in cancer therapy. In this review, we focused on the potential of miRNA clusters as a biomarker for diagnosis, prognosis, targeted therapy as well as strategies for modulating their expression in a therapeutic context. This article is categorized under: Regulatory RNAs/RNAi/Riboswitches > Regulatory RNAs RNA Processing > Processing of Small RNAs RNA in Disease and Development > RNA in Disease Regulatory RNAs/RNAi/Riboswitches > Biogenesis of Effector Small RNAs.

Integrative multi-omics analysis identifies a prognostic miRNA signature and a targetable miR-21-3p/TSC2/mTOR axis in metastatic pheochromocytoma/paraganglioma

Rationale: Pheochromocytomas and paragangliomas (PPGLs) are rare neuroendocrine tumors that present variable outcomes. To date, no effective therapies or reliable prognostic markers are available for patients who develop metastatic PPGL (mPPGL). Our aim was to discover robust prognostic markers validated through in vitro models, and define specific therapeutic options according to tumor genomic features.

Methods: We analyzed three PPGL miRNome datasets (n=443), validated candidate markers and assessed them in serum samples (n=36) to find a metastatic miRNA signature. An integrative study of miRNome, transcriptome and proteome was performed to find miRNA targets, which were further characterized in vitro.

Results: A signature of six miRNAs (miR-21-3p, miR-183-5p, miR-182-5p, miR-96-5p, miR-551b-3p, and miR-202-5p) was associated with metastatic risk and time to progression. A higher expression of five of these miRNAs was also detected in PPGL patients' liquid biopsies compared with controls. The combined expression of miR-21-3p/miR-183-5p showed the best power to predict metastasis (AUC=0.804, P=4.67·10^-18), and was found associated in vitro with pro-metastatic features, such as neuroendocrine-mesenchymal transition phenotype, and increased cell migration rate. A pan-cancer multi-omic integrative study correlated miR-21-3p levels with TSC2 expression, mTOR pathway activation, and a predictive signature for mTOR inhibitor-sensitivity in PPGLs and other cancers. Likewise, we demonstrated in vitro a TSC2 repression and an enhanced rapamycin sensitivity upon miR-21-3p expression.

Conclusions: Our findings support the assessment of miR-21-3p/miR-183-5p, in tumors and liquid biopsies, as biomarkers for risk stratification to improve the PPGL patients' management. We propose miR-21-3p to select mPPGL patients who may benefit from mTOR inhibitors.

Comprehensive Overview and Assessment of microRNA Target Prediction Tools in Homo sapiens and Drosophila melanogaster

Background:

MicroRNAs (miRNAs) are small non-coding RNAs that control gene expression at the post-transcriptional level through complementary base pairing with the target mRNA, leading to mRNA degradation and blocking translation process. Many dysfunctions of these small regulatory molecules have been linked to the development and progression of several diseases. Therefore, it is necessary to reliably predict potential miRNA targets.

Objective:

A large number of computational prediction tools have been developed which provide a faster way to find putative miRNA targets, but at the same time, their results are often inconsistent. Hence, finding a reliable, functional miRNA target is still a challenging task. Also, each tool is equipped with different algorithms, and it is difficult for the biologists to know which tool is the best choice for their study.

Methods:

We analyzed eleven miRNA target predictors on Drosophila melanogaster and Homo sapiens by applying significant empirical methods to evaluate and assess their accuracy and performance using experimentally validated high confident mature miRNAs and their targets. In addition, this paper also describes miRNA target prediction algorithms, and discusses common features of frequently used target prediction tools.

Results:

The results show that MicroT, microRNA and CoMir are the best performing tool on Drosopihla melanogaster; while TargetScan and miRmap perform well for Homo sapiens. The predicted results of each tool were combined in order to improve the performance in both the datasets, but any significant improvement is not observed in terms of true positives.

Conclusion:

The currently available miRNA target prediction tools greatly suffer from a large number of false positives. Therefore, computational prediction of significant targets with high statistical confidence is still an open challenge.

FACER: comprehensive molecular and functional characterization of epigenetic chromatin regulators

Epigenetic alterations, a well-recognized cancer hallmark, are driven by chromatin regulators (CRs). However, little is known about the extent of CR deregulation in cancer, and less is known about their common and specialized roles across various cancers. Here, we performed genome-wide analyses and constructed molecular signatures and network profiles of functional CRs in over 10 000 tumors across 33 cancer types. By integration of DNA mutation, genome-wide methylation, transcriptional/post-transcriptional regulation, and protein interaction networks with clinical outcomes, we identified CRs associated with cancer subtypes and clinical prognosis as potential oncogenic drivers. Comparative network analysis revealed principles of CR regulatory specificity and functionality. In addition, we identified common and specific CRs by assessing their prevalence across cancer types. Common CRs tend to be histone modifiers and chromatin remodelers with fundamental roles, whereas specialized CRs are involved in context-dependent functions. Finally, we have made a user-friendly web interface-FACER (Functional Atlas of Chromatin Epigenetic Regulators) available for exploring clinically relevant CRs for the development of CR biomarkers and therapeutic targets. Our integrative analysis reveals specific determinants of CRs across cancer types and presents a resource for investigating disease-associated CRs.

Computational Resources for Prediction and Analysis of Functional miRNA and Their Targetome

microRNAs are evolutionarily conserved, endogenously produced, noncoding RNAs (ncRNAs) of approximately 19-24 nucleotides (nts) in length known to exhibit gene silencing of complementary target sequence. Their deregulated expression is reported in various disease conditions and thus has therapeutic implications. In the last decade, various computational resources are published in this field. In this chapter, we have reviewed bioinformatics resources, i.e., miRNA-centered databases, algorithms, and tools to predict miRNA targets. First section has enlisted more than 75 databases, which mainly covers information regarding miRNA registries, targets, disease associations, differential expression, interactions with other noncoding RNAs, and all-in-one resources. In the algorithms section, we have compiled about 140 algorithms from eight subcategories, viz. for the prediction of precursor (pre-) and mature miRNAs. These algorithms are developed on various sequence, structure, and thermodynamic based features incorporated into different machine learning techniques (MLTs). In addition, computational identification of miRNAs from high-throughput next generation sequencing (NGS) data and their variants, viz. isomiRs, differential expression, miR-SNPs, and functional annotation, are discussed. Prediction and analysis of miRNAs and their associated targets are also evaluated under miR-targets section providing knowledge regarding novel miRNA targets and complex host-pathogen interactions. In conclusion, we have provided comprehensive review of in silico resources published in miRNA research to help scientific community be updated and choose the appropriate tool according to their needs.

Pseudogene RACGAP1P activates RACGAP1/Rho/ERK signalling axis as a competing endogenous RNA to promote hepatocellular carcinoma early recurrence

Accumulating evidence has indicated crucial roles for pseudogenes in human cancers. However, the roles played by pseudogenes in the pathogenesis of HCC, particularly HCC early recurrence, still incompletely elucidated. Herein, we identify a novel early recurrence related pseudogene RACGAP1P which was significantly upregulated in HCC and was associated with larger tumour size, advanced clinical stage, abnormal AFP level and shorter survival time. In vitro and in vivo experiments have shown that RACGAP1P is a prerequisite for the development of malignant characteristics of HCC cells, including cell growth and migration. Mechanistic investigations indicated that RACGAP1P elicits its oncogenic activity as a ceRNA to sequestrate miR-15-5p from its endogenous target RACGAP1, thereby leading to the upregulation of RACGAP1 and the activation of RhoA/ERK signalling. These results may provide new insights into the functional crosstalk of the pseudogene/miRNA/parent-gene genetic network during HCC early relapse and may contribute to improving the clinical intervention for this subset of HCC patients.

The effect and mechanism of miR-210 in down-regulating the autophagy of lung cancer cells

This project aims to investigate the roles of miR-210 in autophagy of lung cancer cells and the related mechanism. The expressions of miR-210 and ATG7 in 30 cancer tissues and the adjacent tissues in patients with lung cancer were compared using RT-qPCR methods, Western Blot assay was carried out to test the expression of ATG7 in protein. Moreover, the dual luciferase reporter gene assay system was used to confirm ATG7 is a target gene of miR-210. Furthermore, lung cancer cell line A549 was transfected with either miR-210 mimics or inhibitors and RT-qPCR methods was used to detect the expression of miR-210 and ATG7. Next, MTT assay was used to examine the effect of miR-210 on the growth of the lung cancer cells, and finally, the expression of autophagy related genes, ATG7, LC3-II/LC3-I and Beclin-1 were detected by Western Blot and ICC assay. We observed that miR-210 was significantly increased and ATG7 was markedly decreased in cancer tissue of patients with lung cancer compared with normal tissue. Moreover, results of dual luciferase reporter assay indicated that ATG7 is a direct target of miR-210. Next, transfection of miR-210 mimics in lung cancer cells induced significant increase in cell proliferation, and transfection of miR-210 inhibitors lead to inhibited cell proliferation. Furthermore, over-expression of miR-210 induced marked decrease in the expression of ATG7, LC3-II/LC3-I and Beclin-1, while transfection of miR-210 inhibitors induced significant increase in the expression of ATG7, LC3-II/LC3-I and beclin-1. Our results suggested that miR-210 plays a great role in autophagy of lung cancer cell by targeting ATG7.

OncomiR: an online resource for exploring pan-cancer microRNA dysregulation

Summary

Dysregulation of microRNAs (miRNAs) is extensively associated with cancer development and progression. miRNAs have been shown to be biomarkers for predicting tumor formation and outcome. However, identification of the relationships between miRNA expression and tumor characteristics can be difficult and time-consuming without appropriate bioinformatics expertise. To address this issue, we present OncomiR, an online resource for exploring miRNA dysregulation in cancer. Using combined miRNA-seq, RNA-seq and clinical data from The Cancer Genome Atlas, we systematically performed statistical analyses to identify dysregulated miRNAs that are associated with tumor development and progression in most major cancer types. Additional analyses further identified potential miRNA-gene target interactions in tumors. These results are stored in a backend database and presented through a web server interface. Moreover, through a backend bioinformatics pipeline, OncomiR can also perform dynamic analysis with custom miRNA selections for in-depth characterization of miRNAs in cancer.

Availability and implementation

The OncomiR website is freely accessible at www.oncomir.org.

Contact

xiaowei.wang@wustl.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.