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MicroRNA–mRNA interaction analysis to detect potential dysregulation in complex diseases

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Abstract

It is now recognized that genetic interactions (epistasis) are important sources of the hidden genetic variations and may play an important role in complex diseases. Identifying genetic interactions not only helps to explain part of the heritability of complex diseases, but also provides the clue to understand the underlying pathogenesis of complex diseases. Advances in high-throughput technologies enable simultaneous measurements of multiple genomic features from the same samples on a genome-wide scale, and different omics features are not acting in isolation but interact/crosstalk at multiple (within and across individual omics features) levels in complex networks. Therefore, genetic interaction needs to be accounted for across different omics features, potentially allowing an explanation of phenotype variation that single omics data cannot capture. In this study, we propose an analysis framework to detect the miRNA–mRNA interaction enrichment by incorporating principal components analysis and canonical correlation analysis. We demonstrate the advantages of our method by applying to miRNA and mRNA data on glioblastoma (GBM) generated by The Cancer Genome Atlas project. The results show that there are enrichments of the interactions between co-expressed miRNAs and gene pathways which are associated with GBM status. The biological functions of those identified genes and miRNAs have been confirmed to be associated with glioblastoma by independent studies. The proposed approach provides new insights in the regulatory mechanisms and an example for detecting interactions of multi-omics data on complex diseases.

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Authors and Affiliations

  1. Center for Bioinformatics and Genomics, School of Public Health and Tropical Medicine, Tulane University, New Orleans, LA, 70112, USA

    Wenlong Tang, Chao Xu, Yu-Ping Wang, Hong-Wen Deng & Ji-Gang Zhang

  2. Department of Biomedical Engineering, Tulane University, New Orleans, LA, 70118, USA

    Wenlong Tang & Yu-Ping Wang

  3. Department of Biostatistics and Bioinformatics, Tulane University, 1440 Canal Street, Suite 2001, New Orleans, LA, 70112, USA

    Wenlong Tang, Chao Xu, Hong-Wen Deng & Ji-Gang Zhang

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  1. Wenlong Tang
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  2. Chao Xu
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  3. Yu-Ping Wang
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  4. Hong-Wen Deng
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  5. Ji-Gang Zhang
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Correspondence to Ji-Gang Zhang.

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Tang, W., Xu, C., Wang, YP. et al. MicroRNA–mRNA interaction analysis to detect potential dysregulation in complex diseases. Netw Model Anal Health Inform Bioinforma 4, 1 (2015). https://doi.org/10.1007/s13721-014-0074-x

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  • DOI: https://doi.org/10.1007/s13721-014-0074-x

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