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Module network inference from a cancer gene expression data set identifies microRNA regulated modules

Eric Bonnet (UGent) , Marianthi Tatari, Anagha Madhusudan Joshi (UGent) , Tom Michoel (UGent) , Kathleen Marchal (UGent) , Geert Berx (UGent) and Yves Van de Peer (UGent)
(2010) PLOS ONE. 5(4).
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Abstract
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.
Keywords
B-CELLS, DIFFERENTIATION, CHEMOKINE BRAK, PROSTATE-CANCER, MASTER REGULATOR, MIR-200 FAMILY, MESENCHYMAL TRANSITION, REPRESSION, RECEPTOR, C-FOS TRANSCRIPTION

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Please use this url to cite or link to this publication:

Chicago
Bonnet, Eric, Marianthi Tatari, Anagha Madhusudan Joshi, Tom Michoel, Kathleen Marchal, Geert Berx, and Yves Van de Peer. 2010. “Module Network Inference from a Cancer Gene Expression Data Set Identifies microRNA Regulated Modules.” Plos One 5 (4).
APA
Bonnet, E., Tatari, M., Joshi, A. M., Michoel, T., Marchal, K., Berx, G., & Van de Peer, Y. (2010). Module network inference from a cancer gene expression data set identifies microRNA regulated modules. PLOS ONE, 5(4).
Vancouver
1.
Bonnet E, Tatari M, Joshi AM, Michoel T, Marchal K, Berx G, et al. Module network inference from a cancer gene expression data set identifies microRNA regulated modules. PLOS ONE. 2010;5(4).
MLA
Bonnet, Eric, Marianthi Tatari, Anagha Madhusudan Joshi, et al. “Module Network Inference from a Cancer Gene Expression Data Set Identifies microRNA Regulated Modules.” PLOS ONE 5.4 (2010): n. pag. Print.
@article{947492,
  abstract     = {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.},
  articleno    = {e10162},
  author       = {Bonnet, Eric and Tatari, Marianthi and Joshi, Anagha Madhusudan and Michoel, Tom and Marchal, Kathleen and Berx, Geert and Van de Peer, Yves},
  issn         = {1932-6203},
  journal      = {PLOS ONE},
  keyword      = {B-CELLS,DIFFERENTIATION,CHEMOKINE BRAK,PROSTATE-CANCER,MASTER REGULATOR,MIR-200 FAMILY,MESENCHYMAL TRANSITION,REPRESSION,RECEPTOR,C-FOS TRANSCRIPTION},
  language     = {eng},
  number       = {4},
  pages        = {10},
  title        = {Module network inference from a cancer gene expression data set identifies microRNA regulated modules},
  url          = {http://dx.doi.org/10.1371/journal.pone.0010162},
  volume       = {5},
  year         = {2010},
}

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