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Computational identification of hepatitis C virus associated microRNA-mRNA regulatory modules in human livers.

ABSTRACT: Hepatitis C virus (HCV) is a major cause of chronic liver disease by infecting over 170 million people worldwide. Recent studies have shown that microRNAs (miRNAs), a class of small non-coding regulatory RNAs, are involved in the regulation of HCV infection, but their functions have not been systematically studied. We propose an integrative strategy for identifying the miRNA-mRNA regulatory modules that are associated with HCV infection. This strategy combines paired expression profiles of miRNAs and mRNAs and computational target predictions. A miRNA-mRNA regulatory module consists of a set of miRNAs and their targets, in which the miRNAs are predicted to coordinately regulate the level of the target mRNA.We simultaneously profiled the expression of cellular miRNAs and mRNAs across 30 HCV positive or negative human liver biopsy samples using microarray technology. We constructed a miRNA-mRNA regulatory network, and using a graph theoretical approach, identified 38 miRNA-mRNA regulatory modules in the network that were associated with HCV infection. We evaluated the direct miRNA regulation of the mRNA levels of targets in regulatory modules using previously published miRNA transfection data. We analyzed the functional roles of individual modules at the systems level by integrating a large-scale protein interaction network. We found that various biological processes, including some HCV infection related canonical pathways, were regulated at the miRNA level during HCV infection.Our regulatory modules provide a framework for future experimental analyses. This report demonstrates the utility of our approach to obtain new insights into post-transcriptional gene regulation at the miRNA level in complex human diseases.

SUBMITTER: Peng X

PROVIDER: S-EPMC2907698 | biostudies-literature | 2009 Aug

REPOSITORIES: biostudies-literature

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Computational identification of hepatitis C virus associated microRNA-mRNA regulatory modules in human livers.

Peng Xinxia X Li Yu Y Walters Kathie-Anne KA Rosenzweig Elizabeth R ER Lederer Sharon L SL Aicher Lauri D LD Proll Sean S Katze Michael G MG

BMC genomics 20090811

<h4>Background</h4>Hepatitis C virus (HCV) is a major cause of chronic liver disease by infecting over 170 million people worldwide. Recent studies have shown that microRNAs (miRNAs), a class of small non-coding regulatory RNAs, are involved in the regulation of HCV infection, but their functions have not been systematically studied. We propose an integrative strategy for identifying the miRNA-mRNA regulatory modules that are associated with HCV infection. This strategy combines paired expressio ...[more]

PMID: 19671175

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Project description:The complex regulatory network between microRNAs and gene expression remains unclear domain of active research. We proposed to address in part this complex regulation with a novel approach for the genome-wide identification of biomodules derived from paired microRNA and mRNA profiles, which could reveal correlations associated with a complex network of de-regulation in human cancer. Two published expression datasets for 68 samples with 11 distinct types of epithelial cancers and 21 samples of normal tissues were used, containing microRNA expression (Lu et al. Nature Letters 2005) and gene expression (Ramaswarmy et al. PNAS 2001) profiles, respectively. As results, the microRNA expression used jointly with mRNA expression can provide better classifiers of epithelial cancers against normal epithelial tissue than either dataset alone (p=1×10(-10), F-Test). We identified a combination of six microRNA-mRNA biomodules that optimally classified epithelial cancers from normal epithelial tissue (total accuracy = 93.3%; 95% confidence intervals: 86% - 97%), using penalized logistic regression (PLR) algorithm and three-fold cross-validation. Three of these biomodules are individually sufficient to cluster epithelial cancers from normal tissue using mutual information distance. The biomodules contain 10 distinct microRNAs and 98 distinct genes, including well known tumor markers such as miR-15a, miR-30e, IRAK1, TGFBR2, DUSP16, CDC25B and PDCD2. In addition, there is a significant enrichment (Fisher's exact test p=3×10(-10)) between putative microRNA-target gene pairs reported in five microRNA target databases and the inversely correlated micro-RNA-mRNA pairs in the biomodules. Further, microRNAs and genes in the biomodules were found in abstracts mentioning epithelial cancers (Fisher Exact Test, unadjusted p<0.05). Taken together, these results strongly suggest that the discovered microRNA-mRNA biomodules correspond to regulatory mechanisms common to human epithelial cancer samples. In conclusion, we developed and evaluated a novel comprehensive method to systematically identify, on a genome scale, microRNA-mRNA expression biomodules common to distinct cancers of the same tissue. These biomodules also comprise novel microRNA and genes as well as an imputed regulatory network, which may accelerate the work of cancer biologists as large regulatory maps of cancers can be drawn efficiently for hypothesis generation.

Dataset Information

Computational identification of hepatitis C virus associated microRNA-mRNA regulatory modules in human livers.

Publications

Computational identification of hepatitis C virus associated microRNA-mRNA regulatory modules in human livers.

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