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Int. J. Mol. Sci. 2015, 16(12), 30204-30222; doi:10.3390/ijms161226230

MicroRNA-Target Network Inference and Local Network Enrichment Analysis Identify Two microRNA Clusters with Distinct Functions in Head and Neck Squamous Cell Carcinoma

1
Institute of Computational Biology, Helmholtz Zentrum München, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany
2
Research Unit Radiation Cytogenetics, Helmholtz Zentrum München, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany
3
Clinical Cooperation Group “Personalized Radiotherapy in Head and Neck Cancer”, Helmholtz Zentrum München, Ingolstädter Landstr. 1, 85764 Neuherberg, Germany
4
Department of Mathematics, Technical University of Munich, Boltzmannstr. 3, 85748 Garching, Germany
These authors contributed equally to this work.
*
Authors to whom correspondence should be addressed.
Academic Editor: Martin Pichler
Received: 12 November 2015 / Revised: 8 December 2015 / Accepted: 9 December 2015 / Published: 18 December 2015
(This article belongs to the Collection Regulation by Non-Coding RNAs)
View Full-Text   |   Download PDF [4753 KB, uploaded 18 December 2015]   |  

Abstract

MicroRNAs represent ~22 nt long endogenous small RNA molecules that have been experimentally shown to regulate gene expression post-transcriptionally. One main interest in miRNA research is the investigation of their functional roles, which can typically be accomplished by identification of mi-/mRNA interactions and functional annotation of target gene sets. We here present a novel method “miRlastic”, which infers miRNA-target interactions using transcriptomic data as well as prior knowledge and performs functional annotation of target genes by exploiting the local structure of the inferred network. For the network inference, we applied linear regression modeling with elastic net regularization on matched microRNA and messenger RNA expression profiling data to perform feature selection on prior knowledge from sequence-based target prediction resources. The novelty of miRlastic inference originates in predicting data-driven intra-transcriptome regulatory relationships through feature selection. With synthetic data, we showed that miRlastic outperformed commonly used methods and was suitable even for low sample sizes. To gain insight into the functional role of miRNAs and to determine joint functional properties of miRNA clusters, we introduced a local enrichment analysis procedure. The principle of this procedure lies in identifying regions of high functional similarity by evaluating the shortest paths between genes in the network. We can finally assign functional roles to the miRNAs by taking their regulatory relationships into account. We thoroughly evaluated miRlastic on a cohort of head and neck cancer (HNSCC) patients provided by The Cancer Genome Atlas. We inferred an mi-/mRNA regulatory network for human papilloma virus (HPV)-associated miRNAs in HNSCC. The resulting network best enriched for experimentally validated miRNA-target interaction, when compared to common methods. Finally, the local enrichment step identified two functional clusters of miRNAs that were predicted to mediate HPV-associated dysregulation in HNSCC. Our novel approach was able to characterize distinct pathway regulations from matched miRNA and mRNA data. An R package of miRlastic was made available through: http://icb.helmholtz-muenchen.de/mirlastic. View Full-Text
Keywords: miRNA expression; mRNA expression; mi-/mRNA regulatory network; elastic net regression; local enrichment analysis; head and neck squamous cell carcinoma miRNA expression; mRNA expression; mi-/mRNA regulatory network; elastic net regression; local enrichment analysis; head and neck squamous cell carcinoma
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Sass, S.; Pitea, A.; Unger, K.; Hess, J.; Mueller, N.S.; Theis, F.J. MicroRNA-Target Network Inference and Local Network Enrichment Analysis Identify Two microRNA Clusters with Distinct Functions in Head and Neck Squamous Cell Carcinoma. Int. J. Mol. Sci. 2015, 16, 30204-30222.

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