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Open Access Highly Accessed Research article

Constructing higher-order miRNA-mRNA interaction networks in prostate cancer via hypergraph-based learning

Soo-Jin Kim12, Jung-Woo Ha23 and Byoung-Tak Zhang123*

Author Affiliations

1 Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul 151-742, Korea

2 Center for Biointelligence Technology (CBIT), Seoul National University, Seoul 151-742, Korea

3 School of Computer Science and Engineering, Seoul National University, Seoul 151-742, Korea

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BMC Systems Biology 2013, 7:47  doi:10.1186/1752-0509-7-47

Published: 19 June 2013

Abstract

Background

Dysregulation of genetic factors such as microRNAs (miRNAs) and mRNAs has been widely shown to be associated with cancer progression and development. In particular, miRNAs and mRNAs cooperate to affect biological processes, including tumorigenesis. The complexity of miRNA-mRNA interactions presents a major barrier to identifying their co-regulatory roles and functional effects. Thus, by computationally modeling these complex relationships, it may be possible to infer the gene interaction networks underlying complicated biological processes.

Results

We propose a data-driven, hypergraph structural method for constructing higher-order miRNA-mRNA interaction networks from cancer genomic profiles. The proposed model explicitly characterizes higher-order relationships among genetic factors, from which cooperative gene activities in biological processes may be identified. The proposed model is learned by iteration of structure and parameter learning. The structure learning efficiently constructs a hypergraph structure by generating putative hyperedges representing complex miRNA-mRNA modules. It adopts an evolutionary method based on information-theoretic criteria. In the parameter learning phase, the constructed hypergraph is refined by updating the hyperedge weights using the gradient descent method. From the model, we produce biologically relevant higher-order interaction networks showing the properties of primary and metastatic prostate cancer, as candidates of potential miRNA-mRNA regulatory circuits.

Conclusions

Our approach focuses on potential cancer-specific interactions reflecting higher-order relationships between miRNAs and mRNAs from expression profiles. The constructed miRNA-mRNA interaction networks show oncogenic or tumor suppression characteristics, which are known to be directly associated with prostate cancer progression. Therefore, the hypergraph-based model can assist hypothesis formulation for the molecular pathogenesis of cancer.

Keywords:
miRNA-mRNA interaction networks; Hypergraph-based model; Higher-order gene modules; Evolutionary learning; Cancer genomics data analysis