Module Network Inference from a Cancer Gene Expression Data Set Identifies MicroRNA Regulated Modules

被引:41
作者
Bonnet, Eric [1 ,2 ]
Tatari, Marianthi [3 ,4 ]
Joshi, Anagha [1 ,2 ]
Michoel, Tom [1 ,2 ]
Marchal, Kathleen [5 ]
Berx, Geert [3 ,4 ]
Van de Peer, Yves [1 ,2 ]
机构
[1] VIB, Dept Plant Syst Biol, Ghent, Belgium
[2] Univ Ghent, Dept Mol Genet, B-9000 Ghent, Belgium
[3] VIB, Dept Mol Biomed Res, Unit Mol & Cellular Oncol, Ghent, Belgium
[4] Univ Ghent, Dept Biomed Mol Biol, B-9000 Ghent, Belgium
[5] KULeuven, Dept Microbial & Mol Syst, CMPG, Louvain, Belgium
来源
PLOS ONE | 2010年 / 5卷 / 04期
关键词
C-FOS TRANSCRIPTION; MESENCHYMAL TRANSITION; MIR-200; FAMILY; MASTER REGULATOR; PROSTATE-CANCER; CHEMOKINE BRAK; CELLS; REPRESSION; RECEPTOR; BREAST;
D O I
10.1371/journal.pone.0010162
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Background: MicroRNAs (miRNAs) are small RNAs that recognize and regulate mRNA target genes. Multiple lines of evidence indicate that they are key regulators of numerous critical functions in development and disease, including cancer. However, defining the place and function of miRNAs in complex regulatory networks is not straightforward. Systems approaches, like the inference of a module network from expression data, can help to achieve this goal. Methodology/Principal Findings: During the last decade, much progress has been made in the development of robust and powerful module network inference algorithms. In this study, we analyze and assess experimentally a module network inferred from both miRNA and mRNA expression data, using our recently developed module network inference algorithm based on probabilistic optimization techniques. We show that several miRNAs are predicted as statistically significant regulators for various modules of tightly co-expressed genes. A detailed analysis of three of those modules demonstrates that the specific assignment of miRNAs is functionally coherent and supported by literature. We further designed a set of experiments to test the assignment of miR-200a as the top regulator of a small module of nine genes. The results strongly suggest that miR-200a is regulating the module genes via the transcription factor ZEB1. Interestingly, this module is most likely involved in epithelial homeostasis and its dysregulation might contribute to the malignant process in cancer cells. Conclusions/Significance: Our results show that a robust module network analysis of expression data can provide novel insights of miRNA function in important cellular processes. Such a computational approach, starting from expression data alone, can be helpful in the process of identifying the function of miRNAs by suggesting modules of co-expressed genes in which they play a regulatory role. As shown in this study, those modules can then be tested experimentally to further investigate and refine the function of the miRNA in the regulatory network.
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页数:10
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