Gene-microRNA network module analysis for ovarian cancer. Issue 4 (December 2016)

• Record Type: Journal Article
• Authors: Zhang, Shuqin; Ng, Michael
• Is Part Of: BMC systems biology. Volume 10:Issue 4(2016)
• Page Start: 445
• Page End: 455
• Publication Date: 2016-12
• Subjects: Gene-miRNA network -- Module identification -- Data integration; Biological systems -- Periodicals; Biology -- Research -- Periodicals; Cell physiology -- Periodicals; Genes -- Analysis -- Periodicals; 571
• DOI: 10.1186/s12918-016-0357-1
• Languages: English
• ISSNs: 1752-0509

Abstract

Abstract Background MicroRNAs (miRNAs) are involved in many biological processes by regulating post-transcriptional gene expression. The alterations of the regulatory pathways can cause different diseases including cancer. Although many works have been done to study the gene-miRNA regulatory network, the intertwined relationship is far from being fully understood. The objective of this study is to integrate both gene expression data and miRNA data so as to explore the complex relationships among them. Methods By integrating the networks consisting of gene coexpression, miRNA coexpression, gene-miRNA coexpression, and the known gene-miRNA interactions, we aim to find the most connected network modules so as to study their functions and properties. In this paper, we proposed an optimization model for identification of the modules in the integrated networks. This model tries to find both the modules in the gene-gene and miRNA-miRNA coexpression networks and the densely connected gene-miRNA subneworks. An approximation computational method was developed to solve the optimization problem. Results We applied the method to 556 human ovarian cancer samples with both gene expression data and miRNA expression data. The identified modules are significantly enriched by miRNA clusters, GO-BPs, and KEGG pathways. We compared our method with some existing methods and showed the better performance of our method. We also showed that the miRNAs and genes in our identified modules are associated with cancers, especially ovarian cancer. Conclusions This study provides strong support that the subnetworks consisting of genes and miRNAs with close interactions contribute the cancers. The proposed computational method can be applied to other studies that are related to different types of networks.