An integrated network of microRNA and gene expression in ovarian cancer

Background

Ovarian cancer is a deadly female reproductive cancer. Understanding the biological mechanisms underlying ovarian cancer could help lead to quicker and more accurate diagnosis and more effective treatments. Both changes in microRNA(miRNA) expression and miRNA/mRNA dysregulation have been associated with ovarian cancer. With the availability of whole-genome miRNA and mRNA sequencing we now have new potentials to study these associations. In this study, we performed a comprehensive analysis of miRNA and mRNA expression in ovarian cancer using an integrative network approach combined with association analysis.

Results

We developed an integrative approach to construct a network that illustrates the complex interplay among miRNA and gene expression from a systems perspective. Our method is composed of expanding networks from eQTL associations, building network associations in eQTL analysis, and then combine the networks into an integrated network. This integrated network takes account of miRNA expression quantitative trait loci (eQTL) associations, miRNAs and their targets, protein-protein interactions, co-expressions among miRNAs and genes respectively. Applied to the ovarian cancer data set from The Cancer Genome Atlas (TCGA), we created an integrated network with 167 nodes containing 108 miRNA-target interactions and 145 from protein-protein interactions, starting from 44 initial eQTLs. This integrated network encompassed 26 genes and 14 miRNAs associated with cancer. In particular, 11 genes and 12 miRNAs in the integrated network are associated with ovarian cancer.

Conclusion

We demonstrated an integrated network approach that integrates multiple data sources at a systems level. We applied this approach to the TCGA ovarian cancer dataset, and constructed a network that provided a more inclusive view of miRNA and gene expression in ovarian cancer. This network included four separate types of interactions among miRNAs and genes. Simply analyzing each interaction component in isolation, such as the eQTL associations, the miRNA-target interactions or the protein-protein interactions, would create a much more limited network than the integrated one.

Nonconventional chemical inhibitors of microRNA: therapeutic scope

MicroRNAs (miRNAs) are a class of genomically encoded small RNA molecules (∼22nts in length), which regulate gene expression post transcriptionally. miRNAs are implicated in several diseases, thus modulation of miRNA is of prime importance. Small molecules offer a non-conventional alternative to do so.

Identification of module biomarkers from the dysregulated ceRNA–ceRNA interaction network in lung adenocarcinoma

The dysregulated ceRNA–ceRNA interaction network in lung adenocarcinoma.

Text Mining on Big and Complex Biomedical Literature

Driven by the rapidly advancing techniques and increasing interests in biology and medicine, about 2,000 to 4,000 references are added daily to MEDLINE, the US national biomedical bibliographic database. Even for a specific research topic, extracting useful and comprehensive information out of the huge literature data pool is challenging. Text mining techniques become extremely useful when dealing with the abundant biomedical information and they have been applied to various areas in the realm of biomedical research. Instead of providing a brief overview of all text mining techniques and every major biomedical text mining application, this chapter explores in-depth the microRNA profiling area and related text mining tools. As an illustrative example, one rule-based text mining system developed by the authors is discussed in detail. This chapter also includes the discussion of the challenges and potential research areas in biomedical text mining.

mirPub: a database for searching microRNA publications

Summary: Identifying, amongst millions of publications available in MEDLINE, those that are relevant to specific microRNAs (miRNAs) of interest based on keyword search faces major obstacles. References to miRNA names in the literature often deviate from standard nomenclature for various reasons, since even the official nomenclature evolves. For instance, a single miRNA name may identify two completely different molecules or two different names may refer to the same molecule. mirPub is a database with a powerful and intuitive interface, which facilitates searching for miRNA literature, addressing the aforementioned issues. To provide effective search services, mirPub applies text mining techniques on MEDLINE, integrates data from several curated databases and exploits data from its user community following a crowdsourcing approach. Other key features include an interactive visualization service that illustrates intuitively the evolution of miRNA data, tag clouds summarizing the relevance of publications to particular diseases, cell types or tissues and access to TarBase 6.0 data to oversee genes related to miRNA publications.

Availability and Implementation: mirPub is freely available at http://www.microrna.gr/mirpub/.

Contact:vergoulis@imis.athena-innovation.gr or dalamag@imis.athena-innovation.gr

Supplementary information:Supplementary data are available at Bioinformatics online.

Identification of a polyomavirus microRNA highly expressed in tumors

Polyomaviruses (PyVs) are associated with tumors including Merkel cell carcinoma (MCC). Several PyVs encode microRNAs (miRNAs) but to date no abundant PyV miRNAs have been reported in tumors. To better understand the function of the Merkel cell PyV (MCPyV) miRNA, we examined phylogenetically-related viruses for miRNA expression. We show that two primate PyVs and the more distantly-related raccoon PyV (RacPyV) encode miRNAs that share genomic position and partial sequence identity with MCPyV miRNAs. Unlike MCPyV miRNA in MCC, RacPyV miRNA is highly abundant in raccoon tumors. RacPyV miRNA negatively regulates reporters of early viral (T antigen) transcripts, yet robust viral miRNA expression is tolerated in tumors. We also identify raccoon miRNAs expressed in RacPyV-associated neuroglial brain tumors, including several likely oncogenic miRNAs (oncomiRs). This work describes the first PyV miRNA abundantly expressed in tumors and is consistent with a possible role for both host and viral miRNAs in RacPyV-associated tumors.

VIRmiRNA: a comprehensive resource for experimentally validated viral miRNAs and their targets

Viral microRNAs (miRNAs) regulate gene expression of viral and/or host genes to benefit the virus. Hence, miRNAs play a key role in host–virus interactions and pathogenesis of viral diseases. Lately, miRNAs have also shown potential as important targets for the development of novel antiviral therapeutics. Although several miRNA and their target repositories are available for human and other organisms in literature, but a dedicated resource on viral miRNAs and their targets are lacking. Therefore, we have developed a comprehensive viral miRNA resource harboring information of 9133 entries in three subdatabases. This includes 1308 experimentally validated miRNA sequences with their isomiRs encoded by 44 viruses in viral miRNA ‘VIRmiRNA’ and 7283 of their target genes in ‘VIRmiRtar’. Additionally, there is information of 542 antiviral miRNAs encoded by the host against 24 viruses in antiviral miRNA ‘AVIRmir’. The web interface was developed using Linux-Apache-MySQL-PHP (LAMP) software bundle. User-friendly browse, search, advanced search and useful analysis tools are also provided on the web interface. VIRmiRNA is the first specialized resource of experimentally proven virus-encoded miRNAs and their associated targets. This database would enhance the understanding of viral/host gene regulation and may also prove beneficial in the development of antiviral therapeutics.

Database URL: http://crdd.osdd.net/servers/virmirna

Human cancer databases (review)

Cancer is one of the four major non‑communicable diseases (NCD), responsible for ~14.6% of all human deaths. Currently, there are >100 different known types of cancer and >500 genes involved in cancer. Ongoing research efforts have been focused on cancer etiology and therapy. As a result, there is an exponential growth of cancer‑associated data from diverse resources, such as scientific publications, genome‑wide association studies, gene expression experiments, gene‑gene or protein‑protein interaction data, enzymatic assays, epigenomics, immunomics and cytogenetics, stored in relevant repositories. These data are complex and heterogeneous, ranging from unprocessed, unstructured data in the form of raw sequences and polymorphisms to well‑annotated, structured data. Consequently, the storage, mining, retrieval and analysis of these data in an efficient and meaningful manner pose a major challenge to biomedical investigators. In the current review, we present the central, publicly accessible databases that contain data pertinent to cancer, the resources available for delivering and analyzing information from these databases, as well as databases dedicated to specific types of cancer. Examples for this wealth of cancer‑related information and bioinformatic tools have also been provided.

Deep sequencing of RNA from three different extracellular vesicle (EV) subtypes released from the human LIM1863 colon cancer cell line uncovers distinct miRNA-enrichment signatures

Secreted microRNAs (miRNAs) enclosed within extracellular vesicles (EVs) play a pivotal role in intercellular communication by regulating recipient cell gene expression and affecting target cell function. Here, we report the isolation of three distinct EV subtypes from the human colon carcinoma cell line LIM1863 – shed microvesicles (sMVs) and two exosome populations (immunoaffinity isolated A33-exosomes and EpCAM-exosomes). Deep sequencing of miRNA libraries prepared from parental LIM1863 cells/derived EV subtype RNA yielded 254 miRNA identifications, of which 63 are selectively enriched in the EVs - miR-19a/b-3p, miR-378a/c/d, and miR-577 and members of the let-7 and miR-8 families being the most prominent. Let-7a-3p*, let-7f-1-3p*, miR-451a, miR-574-5p*, miR-4454 and miR-7641 are common to all EV subtypes, and 6 miRNAs (miR-320a/b/c/d, miR-221-3p, and miR-200c-3p) discern LIM1863 exosomes from sMVs; miR-98-5p was selectively represented only in sMVs. Notably, A33-Exos contained the largest number (32) of exclusively-enriched miRNAs; 14 of these miRNAs have not been reported in the context of CRC tissue/biofluid analyses and warrant further examination as potential diagnostic markers of CRC. Surprisingly, miRNA passenger strands (star miRNAs) for miR-3613-3p*, -362-3p*, -625-3p*, -6842-3p* were the dominant strand in A33-Exos, the converse to that observed in parental cells. This finding suggests miRNA biogenesis may be interlinked with endosomal/exosomal processing.

Detecting miRNA Mentions and Relations in Biomedical Literature

INTRODUCTION

MicroRNAs (miRNAs) have demonstrated their potential as post-transcriptional gene expression regulators, participating in a wide spectrum of regulatory events such as apoptosis, differentiation, and stress response. Apart from the role of miRNAs in normal physiology, their dysregulation is implicated in a vast array of diseases. Dissection of miRNA-related associations are valuable for contemplating their mechanism in diseases, leading to the discovery of novel miRNAs for disease prognosis, diagnosis, and therapy.

MOTIVATION

Apart from databases and prediction tools, miRNA-related information is largely available as unstructured text. Manual retrieval of these associations can be labor-intensive due to steadily growing number of publications. Additionally, most of the published miRNA entity recognition methods are keyword based, further subjected to manual inspection for retrieval of relations. Despite the fact that several databases host miRNA-associations derived from text, lower sensitivity and lack of published details for miRNA entity recognition and associated relations identification has motivated the need for developing comprehensive methods that are freely available for the scientific community. Additionally, the lack of a standard corpus for miRNA-relations has caused difficulty in evaluating the available systems. We propose methods to automatically extract mentions of miRNAs, species, genes/proteins, disease, and relations from scientific literature. Our generated corpora, along with dictionaries, and miRNA regular expression are freely available for academic purposes. To our knowledge, these resources are the most comprehensive developed so far.

RESULTS

The identification of specific miRNA mentions reaches a recall of 0.94 and precision of 0.93.  Extraction of miRNA-disease and miRNA-gene relations lead to an F 1 score of up to 0.76. A comparison of the information extracted by our approach to the databases miR2Disease and miRSel for the extraction of Alzheimer's disease related relations shows the capability of our proposed methods in identifying correct relations with improved sensitivity. The published resources and described methods can help the researchers for maximal retrieval of miRNA-relations and generation of miRNA-regulatory networks.

AVAILABILITY

The training and test corpora, annotation guidelines, developed dictionaries, and supplementary files are available at http://www.scai.fraunhofer.de/mirna-corpora.html.

Detecting miRNA Mentions and Relations in Biomedical Literature

INTRODUCTION

MicroRNAs (miRNAs) have demonstrated their potential as post-transcriptional gene expression regulators, participating in a wide spectrum of regulatory events such as apoptosis, differentiation, and stress response. Apart from the role of miRNAs in normal physiology, their dysregulation is implicated in a vast array of diseases. Dissection of miRNA-related associations are valuable for contemplating their mechanism in diseases, leading to the discovery of novel miRNAs for disease prognosis, diagnosis, and therapy.

MOTIVATION

Apart from databases and prediction tools, miRNA-related information is largely available as unstructured text. Manual retrieval of these associations can be labor-intensive due to steadily growing number of publications. Despite the fact that several databases host miRNA-associations derived from text, lower sensitivity has motivated the need for an improvised framework. Additionally, the lack of a standard corpus for miRNA-relations has caused difficulty in evaluating the available systems. We propose methods to automatically extract mentions of miRNAs, species, genes/proteins, disease, and relations from scientific literature. Our generated corpora, along with dictionaries, and miRNA regular expression are freely available for academic purposes. To our knowledge, these resources are the most comprehensive developed so far.

RESULTS

The identification of specific miRNA mentions reaches a recall of 0.94 and precision of 0.93.  Extraction of miRNA-disease and miRNA-gene relations lead to an F 1 score of up to 0.76. A comparison of the information extracted by our approach to the databases miR2Disease and miRSel for the extraction of Alzheimer's disease related relations shows the capability of our proposed methods in identifying correct relations with improved sensitivity. The published resources and described methods can help the researchers for maximal retrieval of miRNA-relations and generation of miRNA-regulatory networks.

AVAILABILITY

The training and test corpora, annotation guidelines, developed dictionaries, and supplementary files are available at http://www.scai.fraunhofer.de/mirna-corpora.html.

Detecting miRNA Mentions and Relations in Biomedical Literature

Introduction: MicroRNAs (miRNAs) have demonstrated their potential as post-transcriptional gene expression regulators, participating in a wide spectrum of regulatory events such as apoptosis, differentiation, and stress response. Apart from the role of miRNAs in normal physiology, their dysregulation is implicated in a vast array of diseases. Dissection of miRNA-related associations are valuable for contemplating their mechanism in diseases, leading to the discovery of novel miRNAs for disease prognosis, diagnosis, and therapy. Motivation: Apart from databases and prediction tools, miRNA-related information is largely available as unstructured text. Manual retrieval of these associations can be labor-intensive due to steadily growing number of publications. Additionally, most of the published miRNA entity recognition methods are keyword based, further subjected to manual inspection for retrieval of relations. Despite the fact that several databases host miRNA-associations derived from text, lower sensitivity and lack of published details for miRNA entity recognition and associated relations identification has motivated the need for developing comprehensive methods that are freely available for the scientific community. Additionally, the lack of a standard corpus for miRNA-relations has caused difficulty in evaluating the available systems. We propose methods to automatically extract mentions of miRNAs, species, genes/proteins, disease, and relations from scientific literature. Our generated corpora, along with dictionaries, and miRNA regular expression are freely available for academic purposes. To our knowledge, these resources are the most comprehensive developed so far. Results: The identification of specific miRNA mentions reaches a recall of 0.94 and precision of 0.93.  Extraction of miRNA-disease and miRNA-gene relations lead to an F 1 score of up to 0.76. A comparison of the information extracted by our approach to the databases miR2Disease and miRSel for the extraction of Alzheimer's disease related relations shows the capability of our proposed methods in identifying correct relations with improved sensitivity. The published resources and described methods can help the researchers for maximal retrieval of miRNA-relations and generation of miRNA-regulatory networks. Availability: The training and test corpora, annotation guidelines, developed dictionaries, and supplementary files are available at http://www.scai.fraunhofer.de/mirna-corpora.html.

An integrated analysis of the SOX2 microRNA response program in human pluripotent and nullipotent stem cell lines

Background

SOX2 is a core component of the transcriptional network responsible for maintaining embryonal carcinoma cells (ECCs) in a pluripotent, undifferentiated state of self-renewal. As such, SOX2 is an oncogenic transcription factor and crucial cancer stem cell (CSC) biomarker in embryonal carcinoma and, as more recently found, in the stem-like cancer cell component of many other malignancies. SOX2 is furthermore a crucial factor in the maintenance of adult stem cell phenotypes and has additional roles in cell fate determination. The SOX2-linked microRNA (miRNA) transcriptome and regulome has not yet been fully defined in human pluripotent cells or CSCs. To improve our understanding of the SOX2-linked miRNA regulatory network as a contribution to the phenotype of these cell types, we used high-throughput differential miRNA and gene expression analysis combined with existing genome-wide SOX2 chromatin immunoprecipitation (ChIP) data to map the SOX2 miRNA transcriptome in two human embryonal carcinoma cell (hECC) lines.

Results

Whole-microRNAome and genome analysis of SOX2-silenced hECCs revealed many miRNAs regulated by SOX2, including several with highly characterised functions in both cancer and embryonic stem cell (ESC) biology. We subsequently performed genome-wide differential expression analysis and applied a Monte Carlo simulation algorithm and target prediction to identify a SOX2-linked miRNA regulome, which was strongly enriched with epithelial-to-mesenchymal transition (EMT) markers. Additionally, several deregulated miRNAs important to EMT processes had SOX2 binding sites in their promoter regions.

Conclusion

In ESC-like CSCs, SOX2 regulates a large miRNA network that regulates and interlinks the expression of crucial genes involved in EMT.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-711) contains supplementary material, which is available to authorized users.

A quest for miRNA bio-marker: a track back approach from gingivo buccal cancer to two different types of precancers

Deregulation of miRNA expression may contribute to tumorigenesis and other patho-physiology associated with cancer. Using TLDA, expression of 762 miRNAs was checked in 18 pairs of gingivo buccal cancer-adjacent control tissues. Expression of significantly deregulated miRNAs was further validated in cancer and examined in two types of precancer (leukoplakia and lichen planus) tissues by primer-specific TaqMan assays. Biological implications of these miRNAs were assessed bioinformatically. Expression of hsa-miR-1293, hsa-miR-31, hsa-miR-31* and hsa-miR-7 were significantly up-regulated and those of hsa-miR-206, hsa-miR-204 and hsa-miR-133a were significantly down-regulated in all cancer samples. Expression of only hsa-miR-31 was significantly up-regulated in leukoplakia but none in lichen planus samples. Analysis of expression heterogeneity divided 18 cancer samples into clusters of 13 and 5 samples and revealed that expression of 30 miRNAs (including the above-mentioned 7 miRNAs), was significantly deregulated in the cluster of 13 samples. From database mining and pathway analysis it was observed that these miRNAs can significantly target many of the genes present in different cancer related pathways such as “proteoglycans in cancer”, PI3K-AKT etc. which play important roles in expression of different molecular features of cancer. Expression of hsa-miR-31 was significantly up-regulated in both cancer and leukoplakia tissues and, thus, may be one of the molecular markers of leukoplakia which may progress to gingivo-buccal cancer.

microRNAs: role in leukemia and their computational perspective

MicroRNAs (miRNAs) belong to the family of noncoding RNAs (ncRNAs) and had gained importance due to its role in complex biochemical pathways. Changes in the expression of protein coding genes are the major cause of leukemia. Role of miRNAs as tumor suppressors has provided a new insight in the field of leukemia research. Particularly, the miRNAs mediated gene regulation involves the modulation of multiple mRNAs and cooperative action of different miRNAs to regulate a particular gene expression. This highly complex array of regulatory pathway network indicates the great possibility in analyzing and identifying novel findings. Owing to the conventional, slow experimental identification process of miRNAs and their targets, the last decade has witnessed the development of a large amount of computational approaches to deal with the complex interrelations present within biological systems. This article describes the various roles played by miRNAs in regulating leukemia and the role of computational approaches in exploring new possibilities.

Computational prediction of disease microRNAs in domestic animals

BACKGROUND

The most important means of identifying diseases before symptoms appear is through the discovery of disease-associated biomarkers. Recently, microRNAs (miRNAs) have become highly useful biomarkers of infectious, genetic and metabolic diseases in human but they have not been well studied in domestic animals. It is probable that many of the animal homologs of human disease-associated miRNAs may be involved in domestic animal diseases. Here we describe a computational biology study in which human disease miRNAs were utilized to predict orthologous miRNAs in cow, chicken, pig, horse, and dog.

RESULTS

We identified 287 human disease-associated miRNAs which had at least one 100% identical animal homolog. The 287 miRNAs were associated with 359 human diseases referenced in 2,863 Pubmed articles. Multiple sequence analysis indicated that over 60% of known horse mature miRNAs found perfect matches in human disease-associated miRNAs, followed by dog (50%). As expected, chicken had the least number of perfect matches (5%). Phylogenetic analysis of miRNA precursors indicated that 85% of human disease pre-miRNAs were highly conserved in animals, showing less than 5% nucleotide substitution rates over evolutionary time. As an example we demonstrated conservation of human hsa-miR-143-3p which is associated with type 2 diabetes and targets AKT1 gene which is highly conserved in pig, horse and dog. Functional analysis of AKT1 gene using Gene Ontology (GO) showed that it is involved in glucose homeostasis, positive regulation of glucose import, positive regulation of glycogen biosynthetic process, glucose transport and response to food.

CONCLUSIONS

This data provides the animal and veterinary research community with a resource to assist in generating hypothesis-driven research for discovering animal disease-related miRNA from their datasets and expedite development of prophylactic and disease-treatment strategies and also influence research efforts to identify novel disease models in large animals. Integrated data is available for download at http://agbase.hpc.msstate.edu/cgi-bin/animal_mirna.cgi.

Text mining of cancer-related information: review of current status and future directions

PURPOSE

This paper reviews the research literature on text mining (TM) with the aim to find out (1) which cancer domains have been the subject of TM efforts, (2) which knowledge resources can support TM of cancer-related information and (3) to what extent systems that rely on knowledge and computational methods can convert text data into useful clinical information. These questions were used to determine the current state of the art in this particular strand of TM and suggest future directions in TM development to support cancer research.

METHODS

A review of the research on TM of cancer-related information was carried out. A literature search was conducted on the Medline database as well as IEEE Xplore and ACM digital libraries to address the interdisciplinary nature of such research. The search results were supplemented with the literature identified through Google Scholar.

RESULTS

A range of studies have proven the feasibility of TM for extracting structured information from clinical narratives such as those found in pathology or radiology reports. In this article, we provide a critical overview of the current state of the art for TM related to cancer. The review highlighted a strong bias towards symbolic methods, e.g. named entity recognition (NER) based on dictionary lookup and information extraction (IE) relying on pattern matching. The F-measure of NER ranges between 80% and 90%, while that of IE for simple tasks is in the high 90s. To further improve the performance, TM approaches need to deal effectively with idiosyncrasies of the clinical sublanguage such as non-standard abbreviations as well as a high degree of spelling and grammatical errors. This requires a shift from rule-based methods to machine learning following the success of similar trends in biological applications of TM. Machine learning approaches require large training datasets, but clinical narratives are not readily available for TM research due to privacy and confidentiality concerns. This issue remains the main bottleneck for progress in this area. In addition, there is a need for a comprehensive cancer ontology that would enable semantic representation of textual information found in narrative reports.

Good guy or bad guy: the opposing roles of microRNA 125b in cancer

MicroRNAs (miRNAs) are a class of non-coding RNAs that post-transcriptionally silence target mRNAs. Dysregulation of miRNAs is a frequent event in several diseases, including cancer. One miRNA that has gained special interest in the field of cancer research is miRNA-125b (miR-125b). MiR-125b is a ubiquitously expressed miRNA that is aberrantly expressed in a great variety of tumors. In some tumor types, e.g. colon cancer and hematopoietic tumors, miR-125b is upregulated and displays oncogenic potential, as it induces cell growth and proliferation, while blocking the apoptotic machinery. In contrast, in other tumor entities, e.g. mammary tumors and hepatocellular carcinoma, miR-125b is heavily downregulated. This downregulation is accompanied by de-repression of cellular proliferation and anti-apoptotic programs, contributing to malignant transformation. The reasons for these opposing roles are poorly understood. We summarize the current knowledge of miR-125b and its relevant targets in different tumor types and offer several hypotheses for the opposing roles of miR-125b: miR-125b targets multiple mRNAs, which have diverse functions in individual tissues. These target mRNAs are tissue and tumor specifically expressed, suggesting that misregulation by miR-125b depends on the levels of target gene expression. Moreover, we provide several examples that miR-125b upregulation dictates oncogenic characteristics, while downregulation of miR-125b corresponds to the loss of tumor suppressive functions. Thus, in different tumor entities increased or decreased miR-125b expression may contribute to carcinogenesis.

MIROR: a method for cell-type specific microRNA occupancy rate prediction

This work provides a novel method to quantitatively predict miRNA–mRNA interactions in a specific cell type.

Analysis of organ-enriched microRNAs in plasma as an approach to development of Universal Screening Test: feasibility study

Background

Early disease detection with a minimally invasive screening test will significantly increase effectiveness and decrease the cost of treatment. Here we propose a framework of a novel approach – Universal Screening Test (UST) for the detection of pathological processes in a particular organ system, organ, or tissue by RT-qPCR analysis of circulating cell-free miRNAs in plasma. As the first step towards assessing the feasibility of this concept, the present study was designed to analyze whether the same microRNAs (miRNAs) can detect various diseases of a particular organ system.

Methods

RNA was extracted from plasma using Trizol treatment and silica binding. Levels of miRNAs were measured by single target RT-qPCR. The following innovations have been tested and proven effective: (i) the use of organ system/organ/tissue-enriched miRNAs; (ii) the use of miRNAs associated with broad disease categories, such as cancer and inflammation, in combination with the organ-enriched miRNAs; and (iii) the use of “miRNA pairs” for selecting miRNA combinations with the highest sensitivity and specificity.

Results

Here we report biomarker miRNA pairs effectively differentiating (i) patients with pulmonary system diseases (asthma, pneumonia and non-small cell lung cancer) and gastrointestinal (GI) system diseases (Crohn’s disease, stages I/II esophageal, gastric and colon cancers) from controls, each with 95% accuracy; (ii) patients with a pathology of the pulmonary system from patients with a pathology of the GI system with 94% accuracy; and (iii) cancer patients (stages I/II esophageal, gastric, colon cancers, or non-small cell lung cancer) from patients with inflammatory diseases (asthma, pneumonia, or Crohn’s disease) with 93%-95% accuracy.

Conclusions

The results obtained in the present study, along with the data reported by us and others previously, are encouraging and lay the ground for further investigation of the described approach for UST development.

Single nucleotide polymorphisms in microRNA binding sites of oncogenes: implications in cancer and pharmacogenomics

Cancer, a complex genetic disease involving uncontrolled cell proliferation, is caused by inactivation of tumor suppressor genes and activation of oncogenes. A vast majority of these cancer causing genes are known targets of microRNAs (miRNAs) that bind to complementary sequences in 3' untranslated regions (UTR) of messenger RNAs and repress them from translation. Single Nucleotide Polymorphisms (SNPs) occurring naturally in such miRNA binding regions can alter the miRNA:mRNA interaction and can significantly affect gene expression. We hypothesized that 3'UTR SNPs in miRNA binding sites of proto-oncogenes could abrogate their post-transcriptional regulation, resulting in overexpression of oncogenic proteins, tumor initiation, progression, and modulation of drug response in cancer patients. Therefore, we developed a systematic computational pipeline that integrates data from well-established databases, followed stringent selection criteria and identified a panel of 30 high-confidence SNPs that may impair miRNA target sites in the 3' UTR of 54 mRNA transcripts of 24 proto-oncogenes. Further, 8 SNPs amidst them had the potential to determine therapeutic outcome in cancer patients. Functional annotation suggested that altogether these SNPs occur in proto-oncogenes enriched for kinase activities. We provide detailed in silico evidence for the functional effect of these candidate SNPs in various types of cancer.

MiRNAs which target CD3 subunits could be potential biomarkers for cancers

Background

T-cells play an important role in the immune response and are activated in response to the presentation of antigens bound to major histocompatibility complex (MHC) molecules participating with the T-cell receptor (TCR). T-cell receptor complexes also contain four CD3 (cluster of differentiation 3) subunits. The TCR-CD3 complex is vital for T-cell development and plays an important role in intervening cell recognition events. Since microRNAs (miRNAs) are highly stable in blood serum, some of which may target CD3 molecules, they could serve as good biomarkers for early cancer detection. The aim of this study was to see whether there is a relationship between cancers and the amount of miRNAs -targeted CD3 molecules.

Methods

Bioinformatics tools were used in order to predict the miRNA targets for these genes. Subsequently, these highly conserved miRNAs were evaluated to see if they are implicated in various kinds of cancers. Consequently, human disease databases were used. According to the latest research, this study attempted to investigate the possible down- or upregulation of miRNAs cancer patients.

Results

We identified miRNAs which target genes producing CD3 subunit molecules. The most conserved miRNAs were identified for the CD3G gene, while CD247 and CD3EAP genes had the least number and there were no conserved miRNA associated with the CD3D gene. Some of these miRNAs were found to be responsible for different cancers, following a certain pattern.

Conclusions

It is highly likely that miRNAs affect the CD3 molecules, impairing the immune system, recognizing and destroying cancer tumor; hence, they can be used as suitable biomarkers in distinguishing cancer in the very early stages of its development.

Circulating cell-free microRNA as biomarkers for screening, diagnosis and monitoring of neurodegenerative diseases and other neurologic pathologies

Many neurodegenerative diseases, such as Alzheimer's disease, Parkinson disease, vascular and frontotemporal dementias, as well as other chronic neurological pathologies, are characterized by slow development with a long asymptomatic period followed by a stage with mild clinical symptoms. As a consequence, these serious pathologies are diagnosed late in the course of a disease, when massive death of neurons has already occurred and effective therapeutic intervention is problematic. Thus, the development of screening tests capable of detecting neurodegenerative diseases during early, preferably asymptomatic, stages is a high unmet need. Since such tests are to be used for screening of large populations, they should be non-invasive and relatively inexpensive. Further, while subjects identified by screening tests can be further tested with more invasive and expensive methods, e.g., analysis of cerebrospinal fluid or imaging techniques, to be of practical utility screening tests should have high sensitivity and specificity. In this review, we discuss advantages and disadvantages of various approaches to developing screening tests based on analysis of circulating cell-free microRNA (miRNA). Applications of circulating miRNA-based tests for diagnosis of acute and chronic brain pathologies, for research of normal brain aging, and for disease and treatment monitoring are also discussed.

TUMIR: an experimentally supported database of microRNA deregulation in various cancers

BACKGROUND

MicroRNAs were found to play an important role in cancers and several literatures exist to describe the relationship between microRNA and cancer, but the expression pattern was still faintly. There is a need for a comprehensive collection and summary of the interactions under experimental support.

DESCRIPTION

TUMIR (http://www.ncrnalab.com/TUMIR/), a manually extracted database of experimentally supported microRNA-cancer relationship, aims at providing a large, high-quality, validated comprehensive resource of microRNA deregulation in various cancers. The current version includes a systematic literature search to May-1-2012 using PubMed database, contains data extracted from 205 literatures and 1163 entries describing a regulatory interaction between human microRNAs and cancers. Each entry in the database contains the details of microRNA name, the disease name, case number, control number, p value, the experimentally validated targets, sample type, and a brief description of patients' clinic pathologic parameters mentioned in the same paper. The website has several extensive external links to the related websites and any requests can be made by emailing to tumir_pumc@163.com.

CONCLUSION

TUMIR is an open access website and will be an accurate clue for the researchers who are interested in better understanding the relationship between miRNAs and cancer.