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

Linking common non-coding RNAs of human lung cancer and M. tuberculosis

Lung cancer and pulmonary tuberculosis caused by Mycobacterium are two major causes of deaths worldwide. Tuberculosis linked lung cancer is known. However, the precise molecular mechanism of Mycobacterium associated increased risk of lung cancer is not understood. We report 45 common human miRNAs deregulated in both pulmonary tuberculosis and lung cancer. We show that sRNA_1096 and sRNA_1414 from M. tuberculosis have sequence homology with human mir-21. Hence, the potential role of these three small non-coding RNAs in rifampicin resistance in pulmonary tuberculosis is implied. Further, the linking of sRNA_1096 and sRNA_1414 from M. tuberculosis with the host lung tumorigenesis is inferred. Nonetheless, further analysis and validation is required to associate these three non-coding RNAs with Mycobacterium associated increased risk of lung cancer.

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.

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.

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.

Single-Nucleotide Polymorphisms Within MicroRNAs Sequences and Their 3' UTR Target Sites May Regulate Gene Expression in Gastrointestinal Tract Cancers

Background:

Esophageal, stomach, and colorectal cancers are commonly lethal gastrointestinal tract (GIT) neoplasms, causing almost two million deaths worldwide each year. some environmental risk factors are acknowledged; however, genetic defects can significantly contribute to predisposition to GIT cancers. Accordingly, recent works have shown that single-nucleotide polymorphisms (SNPs) within miRNAs coding sequence (miR-SNPs) and miRNA target sites (target-SNPs) may further contribute to increased risk of developing cancer.

Objectives:

In this study, we comprehensively identified miRNA-target gene pairs implicated in GIT cancers and catalogued the presence of potentially functional miR-SNPs and target-SNPs that impair the correct functional recognition.

Materials and Methods:

Using bioinformatics tools, manual literature review, and a highly accurate dataset of experimentally validated miRNA-target gene interactions, we compiled a list of miRNA-target genes pairs related to GIT cancers and prioritized them into different groups based on the levels of experimental support. Functional annotations (gene ontology) were applied to these pairs in each group to gain further information.

Results:

We identified 97 pairs in which both miRNAs and target genes were implicated in GIT cancers. Several pairs, denoted as highly polymorphic pairs, had both miR-SNPs and target-SNPs. In addition, more than 5000 miRNA-target gene pairs were identified in which, according to the previous reports, either the miRNAs or the target genes had a direct involvement in GIT cancers. More than 800 target-SNPs are located in regulatory regions that were extracted from the ENCODE project through the RegulomeDB database. Of these, 20 were classified as expression quantitative trait loci (eQTLs).

Conclusions:

Our work provided a comprehensive source of prioritized and annotated candidate polymorphisms inside miRNAs and their target sites in GIT cancers, which would facilitate the process of choosing right candidate miRNA-target genes and related polymorphisms for future association or functional studies.

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.