Improving biocuration of microRNAs in diseases: a case study in idiopathic pulmonary fibrosis

MicroRNAs (miRNAs) are small and non-coding RNA molecules that inhibit gene expression posttranscriptionally. They play important roles in several biological processes, and in recent years there has been an interest in studying how they are related to the pathogenesis of diseases. Although there are already some databases that contain information for miRNAs and their relation with illnesses, their curation represents a significant challenge due to the amount of information that is being generated every day. In particular, respiratory diseases are poorly documented in databases, despite the fact that they are of increasing concern regarding morbidity, mortality and economic impacts. In this work, we present the results that we obtained in the BioCreative Interactive Track (IAT), using a semiautomatic approach for improving biocuration of miRNAs related to diseases. Our procedures will be useful to complement databases that contain this type of information. We adapted the OntoGene text mining pipeline and the ODIN curation system in a full-text corpus of scientific publications concerning one specific respiratory disease: idiopathic pulmonary fibrosis, the most common and aggressive of the idiopathic interstitial cases of pneumonia. We curated 823 miRNA text snippets and found a total of 246 miRNAs related to this disease based on our semiautomatic approach with the system OntoGene/ODIN. The biocuration throughput improved by a factor of 12 compared with traditional manual biocuration. A significant advantage of our semiautomatic pipeline is that it can be applied to obtain the miRNAs of all the respiratory diseases and offers the possibility to be used for other illnesses.

Database URL

http://odin.ccg.unam.mx/ODIN/bc2015-miRNA/.

MicroRNA-target cross-talks: Key players in glioblastoma multiforme

The role of microRNAs in brain cancer is still naive. Some act as oncogene and others as tumor suppressors. Discovery of efficient biomarkers is mandatory to debate that aggressive disease. Bioinformatically selected microRNAs and their targets were investigated to evaluate their putative signature as diagnostic and prognostic biomarkers in primary glioblastoma multiforme. Expression of a panel of seven microRNAs (hsa-miR-34a, hsa-miR-16, hsa-miR-17, hsa-miR-21, hsa-miR-221, hsa-miR-326, and hsa-miR-375) and seven target genes ( E2F3, PI3KCA, TOM34, WNT5A, PDCD4, DFFA, and EGFR) in 43 glioblastoma multiforme specimens were profiled compared to non-cancer tissues via quantitative reverse transcription-polymerase chain reaction. Immunohistochemistry staining for three proteins (VEGFA, BAX, and BCL2) was performed. Gene enrichment analysis identified the biological regulatory functions of the gene panel in glioma pathway. MGMT ( O-6-methylguanine-DNA methyltransferase) promoter methylation was analyzed for molecular subtyping of tumor specimens. Our data demonstrated a significant upregulation of five microRNAs (hsa-miR-16, hsa-miR-17, hsa-miR-21, hsa-miR-221, and hsa-miR-375), three genes ( E2F3, PI3KCA, and Wnt5a), two proteins (VEGFA and BCL2), and downregulation of hsa-miR-34a and three other genes ( DFFA, PDCD4, and EGFR) in brain cancer tissues. Receiver operating characteristic analysis revealed that miR-34a (area under the curve = 0.927) and miR-17 (area under the curve = 0.900) had the highest diagnostic performance, followed by miR-221 (area under the curve = 0.845), miR-21 (area under the curve = 0.836), WNT5A (area under the curve = 0.809), PDCD4 (area under the curve = 0.809), and PI3KCA (area under the curve = 0.800). MGMT promoter methylation status was associated with high miR-221 levels. Moreover, patients with VEGFA overexpression and downregulation of TOM34 and BAX had poor overall survival. Nevertheless, miR-17, miR-221, and miR-326 downregulation were significantly associated with high recurrence rate. Multivariate analysis by hierarchical clustering classified patients into four distinct groups based on gene panel signature. In conclusion, the explored microRNA-target dysregulation could pave the road toward developing potential therapeutic strategies for glioblastoma multiforme. Future translational and functional studies are highly recommended to better understand the complex bio-molecular signature of this difficult-to-treat tumor.

Tumor suppressive role of miR-194-5p in glioblastoma multiforme

Glioblastoma multiforme (GBM) is defined by the World Health Organization as the most aggressive form of grade IV glioma, characterized by unrestrained cellular proliferation. microRNAs (miRs) serve important roles in the pathogenesis of GBM. However, the function of miR-194-5p in GBM remains unknown. In the present study, the miR-194-5p levels in GBM tissues and cells were evaluated using the reverse transcription-quantitative polymerase chain reaction. Cellular proliferation was tested by MTT analysis. Cellular apoptosis was analyzed by fluorescence-activated cell sorting. The protein level of insulin-like growth factor 1 receptor, the target gene of miR-194-5p, was evaluated by western blotting. The interaction between miR-194-5p and the target gene was confirmed by the dual-luciferase reporter assay. It was demonstrated that miR-194-5p inhibited cell growth and promoted apoptosis. In conclusion, the results of the present study indicated the tumor suppressive role of miR-194-5p.

A graph regularized non-negative matrix factorization method for identifying microRNA-disease associations

MOTIVATION

MicroRNAs (miRNAs) play crucial roles in post-transcriptional regulations and various cellular processes. The identification of disease-related miRNAs provides great insights into the underlying pathogenesis of diseases at a system level. However, most existing computational approaches are biased towards known miRNA-disease associations, which is inappropriate for those new diseases or miRNAs without any known association information.

RESULTS

In this study, we propose a new method with graph regularized non-negative matrix factorization in heterogeneous omics data, called GRNMF, to discover potential associations between miRNAs and diseases, especially for new diseases and miRNAs or those diseases and miRNAs with sparse known associations. First, we integrate the disease semantic information and miRNA functional information to estimate disease similarity and miRNA similarity, respectively. Considering that there is no available interaction observed for new diseases or miRNAs, a preprocessing step is developed to construct the interaction score profiles that will assist in prediction. Next, a graph regularized non-negative matrix factorization framework is utilized to simultaneously identify potential associations for all diseases. The results indicated that our proposed method can effectively prioritize disease-associated miRNAs with higher accuracy compared with other recent approaches. Moreover, case studies also demonstrated the effectiveness of GRNMF to infer unknown miRNA-disease associations for those novel diseases and miRNAs.

AVAILABILITY AND IMPLEMENTATION

The code of GRNMF is freely available at https://github.com/XIAO-HN/GRNMF/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A non-negative matrix factorization based method for predicting disease-associated miRNAs in miRNA-disease bilayer network

MOTIVATION

Identification of disease-associated miRNAs (disease miRNAs) is critical for understanding disease etiology and pathogenesis. Since miRNAs exert their functions by regulating the expression of their target mRNAs, several methods based on the target genes were proposed to predict disease miRNA candidates. They achieved only limited success as they all suffered from the high false-positive rate of target prediction results. Alternatively, other prediction methods were based on the observation that miRNAs with similar functions tend to be associated with similar diseases and vice versa. The methods exploited the information about miRNAs and diseases, including the functional similarities between miRNAs, the similarities between diseases, and the associations between miRNAs and diseases. However, how to integrate the multiple kinds of information completely and consider the biological characteristic of disease miRNAs is a challenging problem.

RESULTS

We constructed a bilayer network to represent the complex relationships among miRNAs, among diseases and between miRNAs and diseases. We proposed a non-negative matrix factorization based method to rank, so as to predict, the disease miRNA candidates. The method integrated the miRNA functional similarity, the disease similarity and the miRNA-disease associations seamlessly, which exploited the complex relationships within the bilayer network and the consensus relationship between multiple kinds of information. Considering the correlation between the candidates related to various diseases, it predicted their respective candidates for all the diseases simultaneously. In addition, the sparseness characteristic of disease miRNAs was introduced to generate more reliable prediction model that excludes those noisy candidates. The results on 15 common diseases showed a superior performance of the new method for not only well-characterized diseases but also new ones. A detailed case study on breast neoplasms, colorectal neoplasms, lung neoplasms and 32 other diseases demonstrated the ability of the method for discovering potential disease miRNAs.

AVAILABILITY AND IMPLEMENTATION

The web service for the new method and the list of predicted candidates for all the diseases are available at http://www.bioinfolab.top.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A compilation of Web-based research tools for miRNA analysis

Since the discovery of microRNAs (miRNAs), a class of noncoding RNAs that regulate the gene expression posttranscriptionally in sequence-specific manner, there has been a release of number of tools useful for both basic and advanced applications. This is because of the significance of miRNAs in many pathophysiological conditions including cancer. Numerous bioinformatics tools that have been developed for miRNA analysis have their utility for detection, expression, function, target prediction and many other related features. This review provides a comprehensive assessment of web-based tools for the miRNA analysis that does not require prior knowledge of any computing languages.

A comprehensive review of web-based non-coding RNA resources for cancer research

Non-coding RNAs include many kinds of RNAs that did not encode proteins. Recent evidences reveal that ncRNAs play critical roles in initiation and progression of cancers. But it is not easy for cancer biologists and medical doctors to easily know the potential roles of ncRNAs in cancer and retrieve the information of ncRNAs under their investigations. To make the available web-based resources more accessible and understandable, we made a comprehensive review for 49 web-based resources of three types of ncRNAs, i.e., microRNAs (miRNAs), long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs). We also listed some preferred resources for 6 different types of analyses related to ncRNAs.

Modeling miRNA-mRNA interactions that cause phenotypic abnormality in breast cancer patients

Background

The dysregulation of microRNAs (miRNAs) alters expression level of pro-oncogenic or tumor suppressive mRNAs in breast cancer, and in the long run, causes multiple biological abnormalities. Identification of such interactions of miRNA-mRNA requires integrative analysis of miRNA-mRNA expression profile data. However, current approaches have limitations to consider the regulatory relationship between miRNAs and mRNAs and to implicate the relationship with phenotypic abnormality and cancer pathogenesis.

Methodology/Findings

We modeled causal relationships between genomic expression and clinical data using a Bayesian Network (BN), with the goal of discovering miRNA-mRNA interactions that are associated with cancer pathogenesis. The Multiple Beam Search (MBS) algorithm learned interactions from data and discovered that hsa-miR-21, hsa-miR-10b, hsa-miR-448, and hsa-miR-96 interact with oncogenes, such as, CCND2, ESR1, MET, NOTCH1, TGFBR2 and TGFB1 that promote tumor metastasis, invasion, and cell proliferation. We also calculated Bayesian network posterior probability (BNPP) for the models discovered by the MBS algorithm to validate true models with high likelihood.

Conclusion/Significance

The MBS algorithm successfully learned miRNA and mRNA expression profile data using a BN, and identified miRNA-mRNA interactions that probabilistically affect breast cancer pathogenesis. The MBS algorithm is a potentially useful tool for identifying interacting gene pairs implicated by the deregulation of expression.

Free-Energy-Driven Lock/Open Assembly-Based Optical DNA Sensor for Cancer-Related microRNA Detection with a Shortened Time-to-Result

Quantification of cancer biomarker microRNAs (miRs) by exquisitely designed biosensors with a short time-to-result is of great clinical significance. With immobilized capture probes (CPs) and fluorescent-labeled signal probes (SPs), surface-involved sandwich-type (SST) biosensors serve as powerful tools for rapid, highly sensitive, and selective detection of miR in complex matrices as opposed to the conventional techniques. One key challenge for such SST biosensors is the existence of false-negative signals when the amount of miRs exceeds SPs in solution phase for a surface with a limited number of CP. To meet this challenge, a dynamic lock/open DNA assembly was designed to rationally program the pathway for miR/SP hybrids. Based on secondary structure analysis and free-energy assessment, a "locker" strand that partially hybridizes with target miR by two separated short arms was designed to stabilize target miR, preventing possible false-negative signals. The strategy was demonstrated on a fiber-based fluorescent DNA-sensing platform. CP/miR/SP sandwiches formed on the fiber surface would generate fluorescent signals for quantitative analysis. The developed SST biosensor was able to detect miR Hsa let-7a with a detection limit of 24 pM. The applicability of this free-energy-driven lock/open assembly-based optical DNA sensor was further confirmed with spiked human urine and serum samples.

A New Network-Based Strategy for Predicting the Potential miRNA-mRNA Interactions in Tumorigenesis

MicroRNA (miRNA) plays an important role in the degradation and inhibition of mRNAs and is a kind of essential drug targets for cancer therapy. To facilitate the clinical cancer research, we proposed a network-based strategy to identify the cancer-related miRNAs and to predict their targeted genes based on the gene expression profiles. The strategy was validated by using the data sets of acute myeloid leukemia (AML), breast invasive carcinoma (BRCA), and kidney renal clear cell carcinoma (KIRC). The results showed that in the top 20 miRNAs ranked by their degrees, 90.0% (18/20), 70.0% (14/20), and 70.0% (14/20) miRNAs were found to be associated with the cancers for AML, BRCA, and KIRC, respectively. The KEGG pathways and GO terms enriched with the genes that were predicted as the targets of the cancer-related miRNAs were significantly associated with the biological processes of cancers. In addition, several genes, which were predicted to be regulated by more than three miRNAs, were identified to be the potential drug targets annotated by using the human protein atlas database. Our results demonstrated that the proposed strategy can be helpful for predicting the miRNA-mRNA interactions in tumorigenesis and identifying the cancer-related miRNAs as the potential drug targets.

Selected miRNA levels are associated with IKZF1 microdeletions in pediatric acute lymphoblastic leukemia

The clinical outcome of children with high-risk relapsed B-cell precursor acute lymphoblastic leukemia (BCP-ALL) is poor. The present study assessed the utility and prognostic value of selected microRNA (miRNA/miR) in BCP-ALL. The changes in the expression levels of these miRNAs regarding known gene lesions affecting lymphoid development [early B-cell factor 1 (EBF1), ETS variant 6 (ETV6), IKAROS family zinc finger 1 (IKZF1), paired box 5 (PAX5), cyclin dependent kinase inhibitor (CDKN) 2A/CDKN2B, retinoblastoma 1 (RB1), pseudoautosomal region 1 (PAR1), B-cell translocation gene 1 protein (BTG1)] were analyzed. The following miRNAs were analyzed: miR-24, miR-31, miR-128, miR-542, and miR-708. The present study focused on patients with deletions of the IKAROS transcriptional factor gene IKZF1, which is currently considered to be an independent negative prognostic factor for ALL outcome. It was demonstrated that the expression level of miR-128 was significantly lower in patients with IKZF1 deletion compared with patients without IKZF1 deletion. Additionally, low expression of miR-542 was associated with CDKN2A/B and miR-31deletions, and low expression of miR-24 was associated with miR-31 deletion. Low expression of miR-31, miR-24, miR-708 and miR-128 was associated with PAX5 deletion, high expression of miR-24 and miR-542 was associated with PAR1 deletion and high expression of miR-708 was associated with ETV6 deletion. The expression of the selected miRNAs was not associated with deletions of BTG1, EBF1 and RB1. These data, by emphasizing the association of miRNAs expression level with microdeletions, may assist to elucidate ALL biology and contribute to future studies on the possible applications of the miRNA profile for diagnosis.

DES-ncRNA: A knowledgebase for exploring information about human micro and long noncoding RNAs based on literature-mining

Noncoding RNAs (ncRNAs), particularly microRNAs (miRNAs) and long ncRNAs (lncRNAs), are important players in diseases and emerge as novel drug targets. Thus, unraveling the relationships between ncRNAs and other biomedical entities in cells are critical for better understanding ncRNA roles that may eventually help develop their use in medicine. To support ncRNA research and facilitate retrieval of relevant information regarding miRNAs and lncRNAs from the plethora of published ncRNA-related research, we developed DES-ncRNA ( www.cbrc.kaust.edu.sa/des_ncrna ). DES-ncRNA is a knowledgebase containing text- and data-mined information from public scientific literature and other public resources. Exploration of mined information is enabled through terms and pairs of terms from 19 topic-specific dictionaries including, for example, antibiotics, toxins, drugs, enzymes, mutations, pathways, human genes and proteins, drug indications and side effects, mutations, diseases, etc. DES-ncRNA contains approximately 878,000 associations of terms from these dictionaries of which 36,222 (5,373) are with regards to miRNAs (lncRNAs). We provide several ways to explore information regarding ncRNAs to users including controlled generation of association networks as well as hypotheses generation. We show an example how DES-ncRNA can aid research on Alzheimer disease and suggest potential therapeutic role for Fasudil. DES-ncRNA is a powerful tool that can be used on its own or as a complement to the existing resources, to support research in human ncRNA. To our knowledge, this is the only knowledgebase dedicated to human miRNAs and lncRNAs derived primarily through literature-mining enabling exploration of a broad spectrum of associated biomedical entities, not paralleled by any other resource.

Inferring microRNA-disease associations by random walk on a heterogeneous network with multiple data sources

Since the discovery of the regulatory function of microRNA (miRNA), increased attention has focused on identifying the relationship between miRNA and disease. It has been suggested that computational method are an efficient way to identify potential disease-related miRNAs for further confirmation using biological experiments. In this paper, we first highlighted three limitations commonly associated with previous computational methods. To resolve these limitations, we established disease similarity subnetwork and miRNA similarity subnetwork by integrating multiple data sources, where the disease similarity is composed of disease semantic similarity and disease functional similarity, and the miRNA similarity is calculated using the miRNA-target gene and miRNA-lncRNA (long non-coding RNA) associations. Then, a heterogeneous network was constructed by connecting the disease similarity subnetwork and the miRNA similarity subnetwork using the known miRNA-disease associations. We extended random walk with restart to predict miRNA-disease associations in the heterogeneous network. The leave-one-out cross-validation achieved an average area under the curve (AUC) of 0:8049 across 341 diseases and 476 miRNAs. For five-fold cross-validation, our method achieved an AUC from 0:7970 to 0:9249 for 15 human diseases. Case studies further demonstrated the feasibility of our method to discover potential miRNA-disease associations. An online service for prediction is freely available at http://ifmda.aliapp.com.

Molecular profiling of locally-advanced rectal adenocarcinoma using microRNA expression (Review)

Treatment for locally-advanced rectal cancer (LARC) typically consists of neoadjuvant chemoradiation followed by total mesorectal excision. Recently, there has been growing interest in non-operative management for patients who are medically-inoperable or wish to avoid surgical morbidity and permanent colostomy. Approximately 50% of patients who receive pre-operative neoadjuvant chemoradiation develop some degree of pathologic response. Approximately 10-20% of patients are found to have a complete pathologic response, a finding which has frequently been shown to predict better clinical outcomes, including local-regional control, distant metastasis and survival. Many recent studies have evaluated the role of molecular biomarkers in predicting response to neoadjuvant therapy. MicroRNAs (miRNAs) are an emerging class of biomarkers that have the potential to predict which patients are most likely to benefit from pre-operative therapy and from a selective surgical approach. Here, we review the published literature on microRNAs as prognostic and predictive biomarkers in rectal cancer after pre-operative therapy. In the future, the development of prospectively validated miRNA signatures will allow clinical implementation of miRNAs as prognostic and predictive signatures in LARC.

RNA-based ovarian cancer research from 'a gene to systems biomedicine' perspective

Ovarian cancer remains the leading cause of death from a gynecologic malignancy, and treatment of this disease is harder than any other type of female reproductive cancer. Improvements in the diagnosis and development of novel and effective treatment strategies for complex pathophysiologies, such as ovarian cancer, require a better understanding of disease emergence and mechanisms of progression through systems medicine approaches. RNA-level analyses generate new information that can help in understanding the mechanisms behind disease pathogenesis, to identify new biomarkers and therapeutic targets and in new drug discovery. Whole RNA sequencing and coding and non-coding RNA expression array datasets have shed light on the mechanisms underlying disease progression and have identified mRNAs, miRNAs, and lncRNAs involved in ovarian cancer progression. In addition, the results from these analyses indicate that various signalling pathways and biological processes are associated with ovarian cancer. Here, we present a comprehensive literature review on RNA-based ovarian cancer research and highlight the benefits of integrative approaches within the systems biomedicine concept for future ovarian cancer research. We invite the ovarian cancer and systems biomedicine research fields to join forces to achieve the interdisciplinary caliber and rigor required to find real-life solutions to common, devastating, and complex diseases such as ovarian cancer.

ABBREVIATIONS

CAF: cancer-associated fibroblasts; COG: Cluster of Orthologous Groups; DEA: disease enrichment analysis; EOC: epithelial ovarian carcinoma; ESCC: oesophageal squamous cell carcinoma; GSI: gamma secretase inhibitor; GO: Gene Ontology; GSEA: gene set enrichment analyzes; HAS: Hungarian Academy of Sciences; lncRNAs: long non-coding RNAs; MAPK/ERK: mitogen-activated protein kinase/extracellular signal-regulated kinases; NGS: next-generation sequencing; ncRNAs: non-coding RNAs; OvC: ovarian cancer; PI3K/Akt/mTOR: phosphatidylinositol-3-kinase/protein kinase B/mammalian target of rapamycin; RT-PCR: real-time polymerase chain reaction; SNP: single nucleotide polymorphism; TF: transcription factor; TGF-β: transforming growth factor-β.

Mining Exosomal Genes for Pancreatic Cancer Targets

BACKGROUND

Exosomes, cell-derived vesicles encompassing lipids, DNA, proteins coding genes and noncoding RNAs (ncRNAs) are present in diverse body fluids. They offer novel biomarker and drug therapy potential for diverse diseases, including cancer.

MATERIALS AND METHODS

Using gene ontology, exosomal genes database and GeneCards metadata analysis tools, a database of cancer-associated protein coding genes and ncRNAs (n=2,777) was established. Variant analysis, expression profiling and pathway mapping were used to identify putative pancreatic cancer exosomal gene candidates.

RESULTS

Five hundred and seventy-five protein-coding genes, 26 RNA genes and one pseudogene directly associated with pancreatic cancer were identified in the study. Nine open reading frames (ORFs) encompassing enzymes, apoptosis and transcriptional regulators, and secreted factors and five cDNAs (enzymes) emerged from the analysis. Among the ncRNA class, 26 microRNAs (miRs), one pseudogene, one long noncoding RNA (LNC) and one antisense gene were identified. Furthermore, 22 exosome-associated protein-coding targets (a cytokine, enzymes, membrane glycoproteins, receptors, and a transporter) emerged as putative leads for pancreatic cancer therapy. Seven of these protein-coding targets are FDA-approved and the drugs-based on these could provide repurposing opportunities for pancreatic cancer.

CONCLUSION

The database of exosomal genes established in this study provides a framework for developing novel biomarkers and drug therapy targets for pancreatic cancer.

Novel miRNA-mRNA interactions conserved in essential cancer pathways

Cancer is a complex disease in which unrestrained cell proliferation results in tumour development. Extensive research into the molecular mechanisms underlying tumorigenesis has led to the characterization of oncogenes and tumour suppressors that are key elements in cancer growth and progression, as well as that of other important elements like microRNAs. These genes and miRNAs appear to be constitutively deregulated in cancer. To identify signatures of miRNA-mRNA interactions potentially conserved in essential cancer pathways, we have conducted an integrative analysis of transcriptomic data, also taking into account methylation and copy number alterations. We analysed 18,605 raw transcriptome samples from The Cancer Genome Atlas covering 15 of the most common types of human tumours. From this global transcriptome study, we recovered known cancer-associated miRNA-targets and importantly, we identified new potential targets from miRNA families, also analysing the phenotypic outcomes of these genes/mRNAs in terms of survival. Further analyses could lead to novel approaches in cancer therapy.

Identifying miRNA sponge modules using biclustering and regulatory scores

Background

MicroRNA (miRNA) sponges with multiple tandem miRNA binding sequences can sequester miRNAs from their endogenous target mRNAs. Therefore, miRNA sponge acting as a decoy is extremely important for long-term loss-of-function studies both in vivo and in silico. Recently, a growing number of in silico methods have been used as an effective technique to generate hypotheses for in vivo methods for studying the biological functions and regulatory mechanisms of miRNA sponges. However, most existing in silico methods only focus on studying miRNA sponge interactions or networks in cancer, the module-level properties of miRNA sponges in cancer is still largely unknown.

Results

We propose a novel in silico method, called miRSM (miRNA Sponge Module) to infer miRNA sponge modules in breast cancer. We apply miRSM to the breast invasive carcinoma (BRCA) dataset provided by The Cancer Genome Altas (TCGA), and make functional validation of the computational results. We discover that most miRNA sponge interactions are module-conserved across two modules, and a minority of miRNA sponge interactions are module-specific, existing only in a single module. Through functional annotation and differential expression analysis, we also find that the modules discovered using miRSM are functional miRNA sponge modules associated with BRCA. Moreover, the module-specific miRNA sponge interactions among miRNA sponge modules may be involved in the progression and development of BRCA. Our experimental results show that miRSM is comparable to the benchmark methods in recovering experimentally confirmed miRNA sponge interactions, and miRSM outperforms the benchmark methods in identifying interactions that are related to breast cancer.

Conclusions

Altogether, the functional validation results demonstrate that miRSM is a promising method to identify miRNA sponge modules and interactions, and may provide new insights for understanding the roles of miRNA sponges in cancer progression and development.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1467-5) contains supplementary material, which is available to authorized users.

miR-373-3p Targets DKK1 to Promote EMT-Induced Metastasis via the Wnt/-Catenin Pathway in Tongue Squamous Cell Carcinoma

MicroRNAs (miRNAs) regulate gene expression and at the same time mediate tumorigenesis. miR-373-3p has diverse effects in tumors, but its role in tongue squamous cell carcinoma (TSCC) remains unknown. The purpose of this study is to determine the function of miR-373-3p in the progression of TSCC. Our results brought to light that miR-373-3p is markedly upregulated in clinical TSCC tissues compared with paired adjacent normal tissues and has significant correlation with a more aggressive TSCC phenotype in patients. Gain-of-function and loss-of-function studies revealed that ectopic miR-373-3p overexpression promoted the metastasis of TSCC cells. Notably, Wnt/β-catenin signaling was hyperactivated in TSCC cells overexpressing miR-373-3p, and this pathway was responsible for the epithelial-mesenchymal transition (EMT) induced by miR-373-3p. Furthermore, miR-373-3p directly targeted and suppressed Dickkopf-1 (DKK1), a negative regulator of the Wnt/β-catenin signaling cascade. These results demonstrate that, by directly targeting DKK1, miR-373-3p constitutively activated Wnt/β-catenin signaling, thus promoting the EMT-induced metastasis of TSCC. Taken together, our findings reveal a new regulatory mechanism for miR-373-3p and suggest that miR-373-3p might be a potential target in TSCC therapy.

Integrated genomic analyses of de novo pathways underlying atypical meningiomas

Meningiomas are mostly benign brain tumours, with a potential for becoming atypical or malignant. On the basis of comprehensive genomic, transcriptomic and epigenomic analyses, we compared benign meningiomas to atypical ones. Here, we show that the majority of primary (de novo) atypical meningiomas display loss of NF2, which co-occurs either with genomic instability or recurrent SMARCB1 mutations. These tumours harbour increased H3K27me3 signal and a hypermethylated phenotype, mainly occupying the polycomb repressive complex 2 (PRC2) binding sites in human embryonic stem cells, thereby phenocopying a more primitive cellular state. Consistent with this observation, atypical meningiomas exhibit upregulation of EZH2, the catalytic subunit of the PRC2 complex, as well as the E2F2 and FOXM1 transcriptional networks. Importantly, these primary atypical meningiomas do not harbour TERT promoter mutations, which have been reported in atypical tumours that progressed from benign ones. Our results establish the genomic landscape of primary atypical meningiomas and potential therapeutic targets.

Meningiomas are mostly benign brain tumours with the potential for becoming atypical or malignant. Here, the authors show that primary atypical meningiomas are epigenetically and genetically distinct from benign and progressed tumours, highlighting possible therapeutic targets such as PRC2.

An approach to forecast human cancer by profiling microRNA expressions from NGS data

BACKGROUND

microRNAs are single-stranded non-coding RNA sequences of 18 - 24 nucleotides in length. They play an important role in post-transcriptional regulation of gene expression. Evidences of microRNA acting as promoter/suppressor of several diseases including cancer are being unveiled. Recent studies have shown that microRNAs are differentially expressed in disease states when compared with that of normal states. Profiling of microRNA is a good measure to estimate the differences in expression levels, which can be further utilized to understand the progression of any associated disease.

METHODS

Machine learning techniques, when applied to microRNA expression values obtained from NGS data, could be utilized for the development of effective disease prediction system. This paper discusses an approach for microRNA expression profiling, its normalization and a Support Vector based machine learning technique to develop a Cancer Prediction System. Presently, the system has been trained with data samples of hepatocellular carcinoma, carcinomas of the bladder and lung cancer. microRNAs related to specific types of cancer were used to build the classifier.

RESULTS

When the system is trained and tested with 10 fold cross validation, the prediction accuracy obtained is 97.56% for lung cancer, 97.82% for hepatocellular carcinoma and 95.0% for carcinomas of the bladder. The system is further validated with separate test sets, which show accuracies higher than 90%. A ranking based on differential expression marks the relative significance of each microRNA in the prediction process.

CONCLUSIONS

Results from experiments proved that microRNA expression profiling is an effective mechanism for disease identification, provided sufficiently large database is available.

Colorectal Carcinoma: A General Overview and Future Perspectives in Colorectal Cancer

Colorectal cancer (CRC) is the third most common cancer and the fourth most common cause of cancer-related death. Most cases of CRC are detected in Western countries, with its incidence increasing year by year. The probability of suffering from colorectal cancer is about 4%–5% and the risk for developing CRC is associated with personal features or habits such as age, chronic disease history and lifestyle. In this context, the gut microbiota has a relevant role, and dysbiosis situations can induce colonic carcinogenesis through a chronic inflammation mechanism. Some of the bacteria responsible for this multiphase process include Fusobacterium spp, Bacteroides fragilis and enteropathogenic Escherichia coli. CRC is caused by mutations that target oncogenes, tumour suppressor genes and genes related to DNA repair mechanisms. Depending on the origin of the mutation, colorectal carcinomas can be classified as sporadic (70%); inherited (5%) and familial (25%). The pathogenic mechanisms leading to this situation can be included in three types, namely chromosomal instability (CIN), microsatellite instability (MSI) and CpG island methylator phenotype (CIMP). Within these types of CRC, common mutations, chromosomal changes and translocations have been reported to affect important pathways (WNT, MAPK/PI3K, TGF-β, TP53), and mutations; in particular, genes such as c-MYC, KRAS, BRAF, PIK3CA, PTEN, SMAD2 and SMAD4 can be used as predictive markers for patient outcome. In addition to gene mutations, alterations in ncRNAs, such as lncRNA or miRNA, can also contribute to different steps of the carcinogenesis process and have a predictive value when used as biomarkers. In consequence, different panels of genes and mRNA are being developed to improve prognosis and treatment selection. The choice of first-line treatment in CRC follows a multimodal approach based on tumour-related characteristics and usually comprises surgical resection followed by chemotherapy combined with monoclonal antibodies or proteins against vascular endothelial growth factor (VEGF) and epidermal growth receptor (EGFR). Besides traditional chemotherapy, alternative therapies (such as agarose tumour macrobeads, anti-inflammatory drugs, probiotics, and gold-based drugs) are currently being studied to increase treatment effectiveness and reduce side effects.

Differential correlation for sequencing data

BACKGROUND

Several methods have been developed to identify differential correlation (DC) between pairs of molecular features from -omics studies. Most DC methods have only been tested with microarrays and other platforms producing continuous and Gaussian-like data. Sequencing data is in the form of counts, often modeled with a negative binomial distribution making it difficult to apply standard correlation metrics. We have developed an R package for identifying DC called Discordant which uses mixture models for correlations between features and the Expectation Maximization (EM) algorithm for fitting parameters of the mixture model. Several correlation metrics for sequencing data are provided and tested using simulations. Other extensions in the Discordant package include additional modeling for different types of differential correlation, and faster implementation, using a subsampling routine to reduce run-time and address the assumption of independence between molecular feature pairs.

RESULTS

With simulations and breast cancer miRNA-Seq and RNA-Seq data, we find that Spearman's correlation has the best performance among the tested correlation methods for identifying differential correlation. Application of Spearman's correlation in the Discordant method demonstrated the most power in ROC curves and sensitivity/specificity plots, and improved ability to identify experimentally validated breast cancer miRNA. We also considered including additional types of differential correlation, which showed a slight reduction in power due to the additional parameters that need to be estimated, but more versatility in applications. Finally, subsampling within the EM algorithm considerably decreased run-time with negligible effect on performance.

CONCLUSIONS

A new method and R package called Discordant is presented for identifying differential correlation with sequencing data. Based on comparisons with different correlation metrics, this study suggests Spearman's correlation is appropriate for sequencing data, but other correlation metrics are available to the user depending on the application and data type. The Discordant method can also be extended to investigate additional DC types and subsampling with the EM algorithm is now available for reduced run-time. These extensions to the R package make Discordant more robust and versatile for multiple -omics studies.

A novel information fusion strategy based on a regularized framework for identifying disease-related microRNAs

This is the overall flowchart of RLSSLP. RLSSLP is a novel information fusion strategy based on regularized framework for revealing potential miRNA-disease associations.

A network similarity integration method for predicting microRNA-disease associations

The NSIM integrates the disease similarity network, miRNA similarity network, and known miRNA-disease association network on the basis of cousin similarity to predict not only novel miRNA-disease associations but also isolated diseases.

Computational and Experimental Identification of Tissue-Specific MicroRNA Targets

In this chapter we discuss computational methods for the prediction of microRNA (miRNA) targets. More specifically, we consider machine learning-based approaches and explain why these methods have been relatively unsuccessful in reducing the number of false positive predictions. Further we suggest approaches designed to improve their performance by considering tissue-specific target regulation. We argue that the miRNA targetome differs depending on the tissue type and introduce a novel algorithm that predicts miRNA targets specifically for colorectal cancer. We discuss features of miRNAs and target sites that affect target recognition, and how next-generation sequencing data can support the identification of novel miRNAs, differentially expressed miRNAs and their tissue-specific mRNA targets. In addition, we introduce some experimental approaches for the validation of miRNA targets as well as web-based resources sharing predicted and validated miRNA target interactions.

Integrative clustering of multi-level omics data for disease subtype discovery using sequential double regularization

With the rapid advances in technologies of microarray and massively parallel sequencing, data of multiple omics sources from a large patient cohort are now frequently seen in many consortium studies. Effective multi-level omics data integration has brought new statistical challenges. One important biological objective of such integrative analysis is to cluster patients in order to identify clinically relevant disease subtypes, which will form basis for tailored treatment and personalized medicine. Several methods have been proposed in the literature for this purpose, including the popular iCluster method used in many cancer applications. When clustering high-dimensional omics data, effective feature selection is critical for better clustering accuracy and biological interpretation. It is also common that a portion of "scattered samples" has patterns distinct from all major clusters and should not be assigned into any cluster as they may represent a rare disease subcategory or be in transition between disease subtypes. In this paper, we firstly propose to improve feature selection of the iCluster factor model by an overlapping sparse group lasso penalty on the omics features using prior knowledge of inter-omics regulatory flows. We then perform regularization over samples to allow clustering with scattered samples and generate tight clusters. The proposed group structured tight iCluster method will be evaluated by two real breast cancer examples and simulations to demonstrate its improved clustering accuracy, biological interpretation, and ability to generate coherent tight clusters.

A novel method for identifying potential disease-related miRNAs via a disease–miRNA–target heterogeneous network

MicroRNAs (miRNAs), as a kind of important small endogenous single-stranded non-coding RNA, play critical roles in a large number of human diseases.

Differential miRNA expression in pleural effusions derived from extracellular vesicles of patients with lung cancer, pulmonary tuberculosis, or pneumonia

MicroRNAs (miRNAs) have been found to play important regulatory roles in various physiological and pathological processes. MiRNAs also exhibit high stability and are present at high concentrations in human bodily fluids. Consequently, miRNAs may represent attractive and novel diagnostic biomarkers for certain clinical conditions. Recently, the capacity for extracellular vesicles, including microvesicles and exosomes, to carry miRNAs that participate in cell-to-cell communication has been described. In the present study, the miRNA expression patterns for three kinds of pleural effusions that were obtained from patients with pneumonia (group A), pulmonary tuberculosis (group B), and lung cancer (group C) were detected with high-throughput sequencing. When the expression levels of these miRNAs were compared among the three groups, three differentially expressed miRNAs were detected between groups A and B, while 27 differentially expressed miRNAs were detected between groups A and C. Notably, miR-378i was significantly elevated only in group B, while miR-205-5p and miR-200b were markedly increased only in group C (p < 0.01). Further studies are needed to confirm whether these differentially expressed miRNAs may serve as prospective diagnostic markers for pulmonary diseases.

dbDEMC 2.0: updated database of differentially expressed miRNAs in human cancers

MicroRNAs (miRNAs) are often deregulated in cancer and are thought to play an important role in cancer development. Large amount of differentially expressed miRNAs have been identified in various cancers by using high-throughput methods. It is therefore quite important to make a comprehensive collection of these miRNAs and to decipher their roles in oncogenesis and tumor progression. In 2010, we presented the first release of dbDEMC, representing a database for collection of differentially expressed miRNAs in human cancers obtained from microarray data. Here we describe an update of the database. dbDEMC 2.0 documents 209 expression profiling data sets across 36 cancer types and 73 subtypes, and a total of 2224 differentially expressed miRNAs were identified. An easy-to-use web interface was constructed that allows users to make a quick search of the differentially expressed miRNAs in certain cancer types. In addition, a new function of ‘meta-profiling’ was added to view differential expression events according to user-defined miRNAs and cancer types. We expect this database to continue to serve as a valuable source for cancer investigation and potential clinical application related to miRNAs. dbDEMC 2.0 is freely available at http://www.picb.ac.cn/dbDEMC.

RNA Systems Biology for Cancer: From Diagnosis to Therapy

It is due to the advances in high-throughput omics data generation that RNA species have re-entered the focus of biomedical research. International collaborate efforts, like the ENCODE and GENCODE projects, have spawned thousands of previously unknown functional non-coding RNAs (ncRNAs) with various but primarily regulatory roles. Many of these are linked to the emergence and progression of human diseases. In particular, interdisciplinary studies integrating bioinformatics, systems biology, and biotechnological approaches have successfully characterized the role of ncRNAs in different human cancers. These efforts led to the identification of a new tool-kit for cancer diagnosis, monitoring, and treatment, which is now starting to enter and impact on clinical practice. This chapter is to elaborate on the state of the art in RNA systems biology, including a review and perspective on clinical applications toward an integrative RNA systems medicine approach. The focus is on the role of ncRNAs in cancer.

Prioritization, clustering and functional annotation of MicroRNAs using latent semantic indexing of MEDLINE abstracts

Background

The amount of scientific information about MicroRNAs (miRNAs) is growing exponentially, making it difficult for researchers to interpret experimental results. In this study, we present an automated text mining approach using Latent Semantic Indexing (LSI) for prioritization, clustering and functional annotation of miRNAs.

Results

For approximately 900 human miRNAs indexed in miRBase, text documents were created by concatenating titles and abstracts of MEDLINE citations which refer to the miRNAs. The documents were parsed and a weighted term-by-miRNA frequency matrix was created, which was subsequently factorized via singular value decomposition to extract pair-wise cosine values between the term (keyword) and miRNA vectors in reduced rank semantic space. LSI enables derivation of both explicit and implicit associations between entities based on word usage patterns. Using miR2Disease as a gold standard, we found that LSI identified keyword-to-miRNA relationships with high accuracy. In addition, we demonstrate that pair-wise associations between miRNAs can be used to group them into categories which are functionally aligned. Finally, term ranking by querying the LSI space with a group of miRNAs enabled annotation of the clusters with functionally related terms.

Conclusions

LSI modeling of MEDLINE abstracts provides a robust and automated method for miRNA related knowledge discovery. The latest collection of miRNA abstracts and LSI model can be accessed through the web tool miRNA Literature Network (miRLiN) at http://bioinfo.memphis.edu/mirlin.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1223-2) contains supplementary material, which is available to authorized users.

Cadherin-6 is a putative tumor suppressor and target of epigenetically dysregulated miR-429 in cholangiocarcinoma

Cholangiocarcinoma (CC) is a rare malignancy of the extrahepatic or intrahepatic biliary tract with an outstanding poor prognosis. Non-surgical therapeutic regimens result in minimally improved survival of CC patients. Global genomic analyses identified a few recurrently mutated genes, some of them in genes involved in epigenetic patterning. In a previous study, we demonstrated global DNA methylation changes in CC, indicating major contribution of epigenetic alterations to cholangiocarcinogenesis. Here, we aimed at the identification and characterization of CC-related, differentially methylated regions (DMRs) in potential microRNA promoters and of genes targeted by identified microRNAs. Twenty-seven hypermethylated and 13 hypomethylated potential promoter regions of microRNAs, known to be associated with cancer-related pathways like Wnt, ErbB, and PI3K-Akt signaling, were identified. Selected DMRs were confirmed in 2 independent patient cohorts. Inverse correlation between promoter methylation and expression suggested miR-129-2 and members of the miR-200 family (miR-200a, miR-200b, and miR-429) as novel tumor suppressors and oncomiRs, respectively, in CC. Tumor suppressor genes deleted in liver cancer 1 (DLC1), F-box/WD-repeat-containing protein 7 (FBXW7), and cadherin-6 (CDH6) were identified as presumed targets in CC. Tissue microarrays of a representative and well-characterized cohort of biliary tract cancers (n=212) displayed stepwise downregulation of CDH6 and association with poor patient outcome. Ectopic expression of CDH6 on the other hand, delayed growth in the CC cell lines EGI-1 and TFK-1, together suggesting a tumor suppressive function of CDH6. Our work represents a valuable repository for the study of epigenetically altered miRNAs in cholangiocarcinogenesis and novel putative, CC-related tumor suppressive miRNAs and oncomiRs.

MicroRNA expression and gene regulation drive breast cancer progression and metastasis in PyMT mice

Background

MicroRNAs (miRNAs) are small non-coding RNA molecules of about 22 nucleotides which function to silence the expression of their target genes. Numerous studies have shown that miRNAs are not only key regulators in important cellular processes but are also drivers in the development of many diseases, especially cancer. Estrogen receptor positive luminal B is the second most common but the least studied subtype of breast cancer. Only a few studies have examined the expression profiles of miRNAs in luminal B breast cancer, and their regulatory roles in cancer progression have yet to be investigated.

Methods

In this study, using polyoma middle T antigen (PyMT) mice, a widely used luminal B breast cancer model, we profiled microRNA (miRNA) expression at four time points that represent different key developmental stages of cancer progression. We considered the expression of both miRNAs and messenger RNAs (mRNAs) at these time points to improve the identification of regulatory targets of miRNAs. By combining gene functional and pathway annotation with miRNA-mRNA interactions, we created a PyMT-specific tripartite miRNA-mRNA-pathway network and identified novel functional regulatory programs (FRPs).

Results

We identified 151 differentially expressed miRNAs with a strict dual nature of either upregulation or downregulation during the whole course of disease progression. Among 82 newly discovered breast-cancer-related miRNAs, 35 can potentially regulate 271 protein-coding genes based on their sequence complementarity and expression profiles. We also identified miRNA-mRNA regulatory modules driving specific cancer-related biological processes.

Conclusions

In this study we profiled the expression of miRNAs during breast cancer progression in the PyMT mouse model. By integrating miRNA and mRNA expression profiles, we identified differentially expressed miRNAs and their target genes involved in several hallmarks of cancer. We applied a novel clustering method to an annotated miRNA-mRNA regulatory network and identified network modules involved in specific cancer-related biological processes.

Electronic supplementary material

The online version of this article (doi:10.1186/s13058-016-0735-z) contains supplementary material, which is available to authorized users.

Oligonucleotide-based label-free detection with optical microresonators: strategies and challenges

This review targets diversified oligonucleotide-based biodetection techniques, focusing on the use of microresonators of whispering gallery mode (WGM) type as optical biosensors mostly integrated with lab-on-a-chip systems. On-chip and microfluidics combined devices along with optical microresonators provide rapid, robust, reproducible and multiplexed biodetection abilities in considerably small volumes. We present a detailed overview of the studies conducted so far, including biodetection of various oligonucleotide biomarkers as well as deoxyribonucleic acids (DNAs), ribonucleic acids (RNAs) and proteins. We particularly advert to chemical surface modifications for specific and selective biosensing.

Comprehensive analysis of high-throughput screens with HiTSeekR

High-throughput screening (HTS) is an indispensable tool for drug (target) discovery that currently lacks user-friendly software tools for the robust identification of putative hits from HTS experiments and for the interpretation of these findings in the context of systems biology. We developed HiTSeekR as a one-stop solution for chemical compound screens, siRNA knock-down and CRISPR/Cas9 knock-out screens, as well as microRNA inhibitor and -mimics screens. We chose three use cases that demonstrate the potential of HiTSeekR to fully exploit HTS screening data in quite heterogeneous contexts to generate novel hypotheses for follow-up experiments: (i) a genome-wide RNAi screen to uncover modulators of TNFα, (ii) a combined siRNA and miRNA mimics screen on vorinostat resistance and (iii) a small compound screen on KRAS synthetic lethality. HiTSeekR is publicly available at http://hitseekr.compbio.sdu.dk It is the first approach to close the gap between raw data processing, network enrichment and wet lab target generation for various HTS screen types.

Cancer Exosomes as miRNA Factories

miRNAs modulate gene expression while exosomes are extracellular cargo vessels that transport miRNAs and other materials to surrounding cells. When exosomes are taken up by recipient cells, the released miRNAs can modulate immune responses, inhibit apoptosis, and promote angiogenesis to maintain tumor growth. Central to this regulation is the processing of the primary transcripts into active miRNAs, which occurs exclusively within mammalian cells. Challenging this dogma is the discovery that Dicer and Ago2, key components of miRNA processing, are also present inside exosomes. While the exact nature of this processing requires extensive proof, it is an exciting notion that exogenous miRNA factories could exist outside the canonical boundaries of mammalian cells.

MiR-125b regulates endometrial receptivity by targeting MMP26 in women undergoing IVF-ET with elevated progesterone on HCG priming day

On the women undergoing IVF-ET with elevated progesterone on human chorionic gonadotrophin priming, the assisted reproductive technology outcome is poor. But, due to the unknown mechanism of this process, no effective method has been found to overcome this difficulty. Here, we investigated the roles of miR-125b and its target gene, MMP26, in endometrial receptivity (ER) in these women. The expression of miR-125b was significantly up-regulated in EECs in women with elevated progesterone during the window of implantation, and it showed a progesterone-dependent effect in vitro. Similarly, the expression of miR-125b was significantly up-regulated in the preimplantation period, and was down-regulated in the implantation period and the post-implantation period in mouse EECs. In addition, miR-125b showed a greater decrease at implantation sites than it did at interimplantation sites. The luciferase report assay demonstrated that MMP26 is a target gene of miR-125b. And the expression profile of MMP26 showed an inverse relationship with miR-125b in vivo and in vitro. Overexpression of miR-125b in human EECs inhibited cell migration and invasion. Gain-of-function of miR-125b induced a significant decrease in the number of implantation sites. In conclusion, these data shed new light on how miR-125b triggers ER decline through the regulation of MMP26 function.

Targeting oncomiRNAs and mimicking tumor suppressor miRNAs: Νew trends in the development of miRNA therapeutic strategies in oncology (Review)

MicroRNA (miRNA or miR) therapeutics in cancer are based on targeting or mimicking miRNAs involved in cancer onset, progression, angiogenesis, epithelial-mesenchymal transition and metastasis. Several studies conclusively have demonstrated that miRNAs are deeply involved in tumor onset and progression, either behaving as tumor-promoting miRNAs (oncomiRNAs and metastamiRNAs) or as tumor suppressor miRNAs. This review focuses on the most promising examples potentially leading to the development of anticancer, miRNA-based therapeutic protocols. The inhibition of miRNA activity can be readily achieved by the use of miRNA inhibitors and oligomers, including RNA, DNA and DNA analogues (miRNA antisense therapy), small molecule inhibitors, miRNA sponges or through miRNA masking. On the contrary, the enhancement of miRNA function (miRNA replacement therapy) can be achieved by the use of modified miRNA mimetics, such as plasmid or lentiviral vectors carrying miRNA sequences. Combination strategies have been recently developed based on the observation that i) the combined administration of different antagomiR molecules induces greater antitumor effects and ii) some anti-miR molecules can sensitize drug-resistant tumor cell lines to therapeutic drugs. In this review, we discuss two additional issues: i) the combination of miRNA replacement therapy with drug administration and ii) the combination of antagomiR and miRNA replacement therapy. One of the solid results emerging from different independent studies is that miRNA replacement therapy can enhance the antitumor effects of the antitumor drugs. The second important conclusion of the reviewed studies is that the combination of anti-miRNA and miRNA replacement strategies may lead to excellent results, in terms of antitumor effects.

miRiaD: A Text Mining Tool for Detecting Associations of microRNAs with Diseases

Background

MicroRNAs are increasingly being appreciated as critical players in human diseases, and questions concerning the role of microRNAs arise in many areas of biomedical research. There are several manually curated databases of microRNA-disease associations gathered from the biomedical literature; however, it is difficult for curators of these databases to keep up with the explosion of publications in the microRNA-disease field. Moreover, automated literature mining tools that assist manual curation of microRNA-disease associations currently capture only one microRNA property (expression) in the context of one disease (cancer). Thus, there is a clear need to develop more sophisticated automated literature mining tools that capture a variety of microRNA properties and relations in the context of multiple diseases to provide researchers with fast access to the most recent published information and to streamline and accelerate manual curation.

Methods

We have developed miRiaD (microRNAs in association with Disease), a text-mining tool that automatically extracts associations between microRNAs and diseases from the literature. These associations are often not directly linked, and the intermediate relations are often highly informative for the biomedical researcher. Thus, miRiaD extracts the miR-disease pairs together with an explanation for their association. We also developed a procedure that assigns scores to sentences, marking their informativeness, based on the microRNA-disease relation observed within the sentence.

Results

miRiaD was applied to the entire Medline corpus, identifying 8301 PMIDs with miR-disease associations. These abstracts and the miR-disease associations are available for browsing at http://biotm.cis.udel.edu/miRiaD. We evaluated the recall and precision of miRiaD with respect to information of high interest to public microRNA-disease database curators (expression and target gene associations), obtaining a recall of 88.46–90.78. When we expanded the evaluation to include sentences with a wide range of microRNA-disease information that may be of interest to biomedical researchers, miRiaD also performed very well with a F-score of 89.4. The informativeness ranking of sentences was evaluated in terms of nDCG (0.977) and correlation metrics (0.678-0.727) when compared to an annotator’s ranked list.

Conclusions

miRiaD, a high performance system that can capture a wide variety of microRNA-disease related information, extends beyond the scope of existing microRNA-disease resources. It can be incorporated into manual curation pipelines and serve as a resource for biomedical researchers interested in the role of microRNAs in disease. In our ongoing work we are developing an improved miRiaD web interface that will facilitate complex queries about microRNA-disease relationships, such as “In what diseases does microRNA regulation of apoptosis play a role?” or “Is there overlap in the sets of genes targeted by microRNAs in different types of dementia?”.”

Electronic supplementary material

The online version of this article (doi:10.1186/s13326-015-0044-y) contains supplementary material, which is available to authorized users.

Regulation of expression of microRNAs by DNA methylation in lung cancer

Differential expression of miRNAs has been linked with lung carcinogenesis. Recent studies have indicated that DNA hypermethylation can lead to silencing of tumor suppressor miRNA-encoding genes. Restoration of tumor suppressor miRNAs using inhibitors of DNA methyltransferases has been shown to suppress cell proliferation, angiogenesis, invasion and metastasis implying that modulation of methylation of specific miRNAs can be used as novel therapeutic targets in lung cancer. In this review, we highlight tremendous progress which has been made in the identification of methylation-mediated silencing of miRNAs and their contribution in lung carcinogenesis along with the clinical utility of methylated miRNAs.

Web-based tools for microRNAs involved in human cancer

MicroRNAs (miRNAs/miRs) are a family of small, endogenous and evolutionarily-conserved non-coding RNAs that are involved in the regulation of several cellular and functional processes. miRNAs can act as oncogenes or tumor suppressors in all types of cancer, and could be used as prognostic and diagnostic biomarkers. Databases and computational algorithms are behind the majority of the research performed on miRNAs. These tools assemble and curate the relevant information on miRNAs and present it in a user-friendly manner. The current review presents 14 online databases that address every aspect of miRNA cancer research. Certain databases focus on miRNAs and a particular type of cancer, while others analyze the behavior of miRNAs in different malignancies at the same time. Additional databases allow researchers to search for mutations in miRNAs or their targets, and to review the naming history of a particular miRNA. All these databases are open-access, and are a valuable tool for those researchers working with these molecules, particularly those who lack access to an advanced computational infrastructure.

microRNAs Databases: Developmental Methodologies, Structural and Functional Annotations

microRNA (miRNA) is an endogenous and evolutionary conserved non-coding RNA, involved in post-transcriptional process as gene repressor and mRNA cleavage through RNA-induced silencing complex (RISC) formation. In RISC, miRNA binds in complementary base pair with targeted mRNA along with Argonaut proteins complex, causes gene repression or endonucleolytic cleavage of mRNAs and results in many diseases and syndromes. After the discovery of miRNA lin-4 and let-7, subsequently large numbers of miRNAs were discovered by low-throughput and high-throughput experimental techniques along with computational process in various biological and metabolic processes. The miRNAs are important non-coding RNA for understanding the complex biological phenomena of organism because it controls the gene regulation. This paper reviews miRNA databases with structural and functional annotations developed by various researchers. These databases contain structural and functional information of animal, plant and virus miRNAs including miRNAs-associated diseases, stress resistance in plant, miRNAs take part in various biological processes, effect of miRNAs interaction on drugs and environment, effect of variance on miRNAs, miRNAs gene expression analysis, sequence of miRNAs, structure of miRNAs. This review focuses on the developmental methodology of miRNA databases such as computational tools and methods used for extraction of miRNAs annotation from different resources or through experiment. This study also discusses the efficiency of user interface design of every database along with current entry and annotations of miRNA (pathways, gene ontology, disease ontology, etc.). Here, an integrated schematic diagram of construction process for databases is also drawn along with tabular and graphical comparison of various types of entries in different databases. Aim of this paper is to present the importance of miRNAs-related resources at a single place.

Characterization of long non-coding RNA-associated ceRNA network to reveal potential prognostic lncRNA biomarkers in human ovarian cancer

Accumulating evidence has underscored the important roles of long non-coding RNAs (lncRNAs) acting as competing endogenous RNAs (ceRNAs) in cancer initiation and progression. In this study, we used an integrative computational method to identify miRNA-mediated ceRNA crosstalk between lncRNAs and mRNAs, and constructed global and progression-related lncRNA-associated ceRNA networks (LCeNETs) in ovarian cancer (OvCa) based on "ceRNA hypothesis". The constructed LCeNETs exhibited small world, modular architecture and high functional specificity for OvCa. Known OvCa-related genes tended to be hubs and occurred preferentially in the functional modules. Ten lncRNA ceRNAs were identified as potential candidates associated with stage progression in OvCa using ceRNA-network driven method. Finally, we developed a ten-lncRNA signature which classified patients into high- and low-risk subgroups with significantly different survival outcomes. Our study will provide novel insight for better understanding of ceRNA-mediated gene regulation in progression of OvCa and facilitate the identification of novel diagnostic and therapeutic lncRNA ceRNAs for OvCa.

Mutation pattern is an influential factor on functional mutation rates in cancer

BACKGROUND

Mutation rates are consistently varied in cancer genome and play an important role in tumorigenesis, however, little has been known about their function potential and impact on the distribution of functional mutations. In this study, we investigated genomic features which affect mutation pattern and the function importance of mutation pattern in cancer.

METHODS

Somatic mutations of clear-cell renal cell carcinoma, liver cancer, lung cancer and melanoma and single nucleotide polymorphisms (SNPs) were intersected with 54 distinct genomic features. Somatic mutation and SNP densities were then computed for each feature type. We constructed 2856 1-Mb windows, in which each row (1-Mb window) contains somatic mutation, SNP densities and 54 feature vectors. Correlation analyses were conducted between somatic mutation, SNP densities and each feature vector. We also built two random forest models, namely somatic mutation model (CSM) and SNP model to predict somatic mutation and SNP densities on a 1-Kb scale. The relation of CSM and SNP scores was further analyzed with the distributions of deleterious coding variants predicted by SIFT and Mutation Assessor, non-coding functional variants evaluated with FunSeq 2 and GWAVA and disease-causing variants from HGMD and ClinVar databases.

RESULTS

We observed a wide range of genomic features which affect local mutation rates, such as replication time, transcription levels, histone marks and regulatory elements. Repressive histone marks, replication time and promoter contributed most to the CSM models, while, recombination rate and chromatin organizations were most important for the SNP model. We showed low mutated regions preferentially have higher densities of deleterious coding mutations, higher average scores of non-coding variants, higher fraction of functional regions and higher enrichment of disease-causing variants as compared to high mutated regions.

CONCLUSIONS

Somatic mutation densities vary largely across cancer genome, mutation frequency is a major indication of function and influence on the distribution of functional mutations in cancer.

Bioinformatics Resources for MicroRNA Discovery

Biomarker identification is often associated with the diagnosis and evaluation of various diseases. Recently, the role of microRNA (miRNA) has been implicated in the development of diseases, particularly cancer. With the advent of next-generation sequencing, the amount of data on miRNA has increased tremendously in the last decade, requiring new bioinformatics approaches for processing and storing new information. New strategies have been developed in mining these sequencing datasets to allow better understanding toward the actions of miRNAs. As a result, many databases have also been established to disseminate these findings. This review focuses on several curated databases of miRNAs and their targets from both predicted and validated sources.