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Rough sets for in silico identification of differentially expressed miRNAs.

ABSTRACT: The microRNAs, also known as miRNAs, are the class of small noncoding RNAs. They repress the expression of a gene posttranscriptionally. In effect, they regulate expression of a gene or protein. It has been observed that they play an important role in various cellular processes and thus help in carrying out normal functioning of a cell. However, dysregulation of miRNAs is found to be a major cause of a disease. Various studies have also shown the role of miRNAs in cancer and the utility of miRNAs for the diagnosis of cancer and other diseases. Unlike with mRNAs, a modest number of miRNAs might be sufficient to classify human cancers. However, the absence of a robust method to identify differentially expressed miRNAs makes this an open problem. In this regard, this paper presents a novel approach for in silico identification of differentially expressed miRNAs from microarray expression data sets. It integrates judiciously the theory of rough sets and merit of the so-called B.632+ bootstrap error estimate. While rough sets select relevant and significant miRNAs from expression data, the B.632+ error rate minimizes the variability and bias of the derived results. The effectiveness of the proposed approach, along with a comparison with other related approaches, is demonstrated on several miRNA microarray expression data sets, using the support vector machine.

SUBMITTER: Paul S

PROVIDER: S-EPMC3790281 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Rough sets for in silico identification of differentially expressed miRNAs.

Paul Sushmita S Maji Pradipta P

International journal of nanomedicine 20130916

The microRNAs, also known as miRNAs, are the class of small noncoding RNAs. They repress the expression of a gene posttranscriptionally. In effect, they regulate expression of a gene or protein. It has been observed that they play an important role in various cellular processes and thus help in carrying out normal functioning of a cell. However, dysregulation of miRNAs is found to be a major cause of a disease. Various studies have also shown the role of miRNAs in cancer and the utility of miRNA ...[more]

PMID: 24098080

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Project description:BACKGROUND: The miRNAs, a class of short approximately 22-nucleotide non-coding RNAs, often act post-transcriptionally to inhibit mRNA expression. In effect, they control gene expression by targeting mRNA. They also help in carrying out normal functioning of a cell as they play an important role in various cellular processes. However, dysregulation of miRNAs is found to be a major cause of a disease. It has been demonstrated that miRNA expression is altered in many human cancers, suggesting that they may play an important role as disease biomarkers. Multiple reports have also noted the utility of miRNAs for the diagnosis of cancer. Among the large number of miRNAs present in a microarray data, a modest number might be sufficient to classify human cancers. Hence, the identification of differentially expressed miRNAs is an important problem particularly for the data sets with large number of miRNAs and small number of samples. RESULTS: In this regard, a new miRNA selection algorithm, called ?HEM, is presented based on rough hypercuboid approach. It selects a set of miRNAs from a microarray data by maximizing both relevance and significance of the selected miRNAs. The degree of dependency of sample categories on miRNAs is defined, based on the concept of hypercuboid equivalence partition matrix, to measure both relevance and significance of miRNAs. The effectiveness of the new approach is demonstrated on six publicly available miRNA expression data sets using support vector machine. The.632+ bootstrap error estimate is used to minimize the variability and biasedness of the derived results. CONCLUSIONS: An important finding is that the ?HEM algorithm achieves lowest B.632+ error rate of support vector machine with a reduced set of differentially expressed miRNAs on four expression data sets compare to some existing machine learning and statistical methods, while for other two data sets, the error rate of the ?HEM algorithm is comparable with the existing techniques. The results on several microarray data sets demonstrate that the proposed method can bring a remarkable improvement on miRNA selection problem. The method is a potentially useful tool for exploration of miRNA expression data and identification of differentially expressed miRNAs worth further investigation.

Project description:MicroRNAs (miRNAs) control multiple biological processes including the innate immune responses by negative post-transcriptional regulation of gene expression. As there were no studies on the role(s) of miRNAs in viral diseases in Atlantic salmon, we aimed to identify miRNAs responding to salmonid alphavirus (SAV) infection. Their expression were studied at different time points post infection with SAV isolates associated with different mortalities. Furthermore, the genome sequences of the identified miRNAs were analysed to reveal putative cis-regulatory elements, and, finally, their putative target genes were predicted.Twenty differentially expressed miRNAs (DE miRNAs) were identified. The expression of the majority of these increased post infection with maximum levels reached after the viral load were stabilized or decreasing. On the other hand, some miRNAs (e.g. the miRNA-21 family) showed decreased expression at the early time points post infection. There were significant differences in the temporal expression of individual miRNA associated with different SAV isolates. Target gene prediction in SAV responsive immune network genes showed that seventeen of the DE miRNAs could target 24 genes (e.g. IRF3, IRF7). Applying the Atlantic salmon transcriptome as input 28 more immune network genes were revealed as putative targets (e.g. IRF5, IRF4). The majority of the predicted target genes promote inflammatory response. The upstream sequences of the miRNA genes revealed a high density of cis-regulatory sequences known as binding sites for immune network transcription factors (TFs). A high expression in the late phase could therefore be due to increased transcription promoted by immune response activated TFs. Based on the in silico target predictions, we discuss their putative roles as early promotors or late inhibitors of inflammation. We propose that the differences in expressions associated with different SAV isolates could contribute to their differences in mortality rates.This study represents the first steps in exploring miRNAs important in viral-host interaction in Atlantic salmon. We identified several miRNAs responding to SAV infection. Some likely to prohibit harmful inflammation while other may promote an early immune response. Their predicted functions need to be validated and further studied in functional assays to fully understand their roles in immune homeostasis.

Project description:Introduction: Ulcerative colitis (UC) is a chronic inflammatory disease of the intestine whose cause and underlying mechanisms are not fully understood. The aim of this study was to use bioinformatics analysis to identify differentially expressed genes (DEGs) with diagnostic and therapeutic potential in UC. Materials and methods: Three UC datasets (GSE179285, GSE75214, GSE48958) were downloaded from the Gene Expression Omnibus (GEO) database. DEGs between normal and UC tissues were identified using the GEO2R online tool. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the DEGs were performed using Metascape. Protein-protein interaction network (PPI) analysis and visualization using STRING and Cytoscape. Finally, the miRNA gene regulatory network was constructed by Cytoscape to predict potential microRNAs (miRNAs) associated with DEGs. Results: A total of 446 DEGs were identified, consisting of 309 upregulated genes and 137 downregulated genes. The enriched functions and pathways of the DEGs include extracellular matrix, regulation of cell adhesion, inflammatory response, response to cytokine, monocarboxylic acid metabolic process, response to toxic substance. The analysis of KEGG pathway indicates that the DEGs were significantly enriched in Complement and coagulation cascades, Amoebiasis, TNF signaling pathway, bile secretion, and Mineral absorption. Combining the results of the PPI network and CytoHubba, 9 hub genes including CXCL8, ICAM1, CXCR4, CD44, IL1B, MMP9, SPP1, TIMP1, and HIF1A were selected. Based on the DEG-miRNAs network construction, 7 miRNAs including miR-335-5p, mir-204-5p, miR-93-5p, miR106a-5p, miR-21-5p, miR-146a-5p, and miR-155-5p were identified as potential critical miRNAs. Conclusion: In summary, we identified DEGs that may be involved in the progression or occurrence of UC. A total of 446 DEGs,9 hub genes and 7 miRNAs were identified, which may be considered as biomarkers of UC. Further studies, however, are needed to elucidate the biological functions of these genes in UC.

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Rough sets for in silico identification of differentially expressed miRNAs.

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Rough sets for in silico identification of differentially expressed miRNAs.

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