A computational approach to identifying gene-microRNA modules in cancer

• Published in: PLoS computational biology Vol. 11; no. 1; p. e1004042
• Main Authors: Jin, Daeyong, Lee, Hyunju
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 01.01.2015; Public Library of Science (PLoS)
• Subjects: Bayes Theorem; Cancer metastasis; Cancer research; Computational Biology - methods; Datasets; Gene expression; Gene Expression Profiling - methods; Gene Regulatory Networks - genetics; Genes; Genetic aspects; Genetic research; Health aspects; Humans; MicroRNA; MicroRNAs; MicroRNAs - analysis; MicroRNAs - genetics; MicroRNAs - metabolism; Neoplasms - classification; Neoplasms - genetics; Neoplasms - metabolism; Ovarian cancer; Regulation; Studies; Transcription factors
• ISSN: 1553-7358; 1553-734X; 1553-7358
• DOI: 10.1371/journal.pcbi.1004042

MicroRNAs (miRNAs) play key roles in the initiation and progression of various cancers by regulating genes. Regulatory interactions between genes and miRNAs are complex, as multiple miRNAs can regulate multiple genes. In addtion, these interactions vary from patient to patient and even among patients with the same cancer type, as cancer development is a heterogeneous process. These relationships are more complicated because transcription factors and other regulatory molecules can also regulate miRNAs and genes. Hence, it is important to identify the complex relationships between genes and miRNAs in cancer. In this study, we propose a computational approach to constructing modules that represent these relationships by integrating the expression data of genes and miRNAs with gene-gene interaction data. First, we used a biclustering algorithm to construct modules consisting of a subset of genes and a subset of samples to incorporate the heterogeneity of cancer cells. Second, we combined gene-gene interactions to include genes that play important roles in cancer-related pathways. Then, we selected miRNAs that are closely associated with genes in the modules based on a Gaussian Bayesian network and Bayesian Information Criteria. When we applied our approach to ovarian cancer and glioblastoma (GBM) data sets, 33 and 54 modules were constructed, respectively. In these modules, 91% and 94% of ovarian cancer and GBM modules, respectively, were explained either by direct regulation between genes and miRNAs or by indirect relationships via transcription factors. In addition, 48.4% and 74.0% of modules from ovarian cancer and GBM, respectively, were enriched with cancer-related pathways, and 51.7% and 71.7% of miRNAs in modules were ovarian cancer-related miRNAs and GBM-related miRNAs, respectively. Finally, we extensively analyzed significant modules and showed that most genes in these modules were related to ovarian cancer and GBM.