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Prediction of a gene regulatory network linked to prostate cancer from gene expression, microRNA and clinical data

Eric Bonnet (UGent) , Tom Michoel (UGent) and Yves Van de Peer (UGent)
(2010) BIOINFORMATICS. 26(18). p.i638-i644
Author
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Abstract
Motivation: Cancer is a complex disease, triggered by mutations in multiple genes and pathways. There is a growing interest in the application of systems biology approaches to analyze various types of cancer-related data to understand the overwhelming complexity of changes induced by the disease. Results: We reconstructed a regulatory module network using gene expression, microRNA expression and a clinical parameter, all measured in lymphoblastoid cell lines derived from patients having aggressive or non-aggressive forms of prostate cancer. Our analysis identified several modules enriched in cell cycle-related genes as well as novel functional categories that might be linked to prostate cancer. Almost one-third of the regulators predicted to control the expression levels of the modules are microRNAs. Several of them have already been characterized as causal in various diseases, including cancer. We also predicted novel microRNAs that have never been associated to this type of tumor. Furthermore, the condition-dependent expression of several modules could be linked to the value of a clinical parameter characterizing the aggressiveness of the prostate cancer. Taken together, our results help to shed light on the consequences of aggressive and non-aggressive forms of prostate cancer.
Keywords
PROLIFERATION, CELL-LINES, SYSTEMS BIOLOGY, MODULE NETWORKS, PROGRESSION, MORTALITY, DISEASE, FOXM1, P53

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MLA
Bonnet, Eric, et al. “Prediction of a Gene Regulatory Network Linked to Prostate Cancer from Gene Expression, MicroRNA and Clinical Data.” BIOINFORMATICS, vol. 26, no. 18, 2010, pp. i638–44, doi:10.1093/bioinformatics/btq395.
APA
Bonnet, E., Michoel, T., & Van de Peer, Y. (2010). Prediction of a gene regulatory network linked to prostate cancer from gene expression, microRNA and clinical data. BIOINFORMATICS, 26(18), i638–i644. https://doi.org/10.1093/bioinformatics/btq395
Chicago author-date
Bonnet, Eric, Tom Michoel, and Yves Van de Peer. 2010. “Prediction of a Gene Regulatory Network Linked to Prostate Cancer from Gene Expression, MicroRNA and Clinical Data.” BIOINFORMATICS 26 (18): i638–44. https://doi.org/10.1093/bioinformatics/btq395.
Chicago author-date (all authors)
Bonnet, Eric, Tom Michoel, and Yves Van de Peer. 2010. “Prediction of a Gene Regulatory Network Linked to Prostate Cancer from Gene Expression, MicroRNA and Clinical Data.” BIOINFORMATICS 26 (18): i638–i644. doi:10.1093/bioinformatics/btq395.
Vancouver
1.
Bonnet E, Michoel T, Van de Peer Y. Prediction of a gene regulatory network linked to prostate cancer from gene expression, microRNA and clinical data. BIOINFORMATICS. 2010;26(18):i638–44.
IEEE
[1]
E. Bonnet, T. Michoel, and Y. Van de Peer, “Prediction of a gene regulatory network linked to prostate cancer from gene expression, microRNA and clinical data,” BIOINFORMATICS, vol. 26, no. 18, pp. i638–i644, 2010.
@article{1061935,
  abstract     = {{Motivation: Cancer is a complex disease, triggered by mutations in multiple genes and pathways. There is a growing interest in the application of systems biology approaches to analyze various types of cancer-related data to understand the overwhelming complexity of changes induced by the disease.
Results: We reconstructed a regulatory module network using gene expression, microRNA expression and a clinical parameter, all measured in lymphoblastoid cell lines derived from patients having aggressive or non-aggressive forms of prostate cancer. Our analysis identified several modules enriched in cell cycle-related genes as well as novel functional categories that might be linked to prostate cancer. Almost one-third of the regulators predicted to control the expression levels of the modules are microRNAs. Several of them have already been characterized as causal in various diseases, including cancer. We also predicted novel microRNAs that have never been associated to this type of tumor. Furthermore, the condition-dependent expression of several modules could be linked to the value of a clinical parameter characterizing the aggressiveness of the prostate cancer. Taken together, our results help to shed light on the consequences of aggressive and non-aggressive forms of prostate cancer.}},
  author       = {{Bonnet, Eric and Michoel, Tom and Van de Peer, Yves}},
  issn         = {{1367-4803}},
  journal      = {{BIOINFORMATICS}},
  keywords     = {{PROLIFERATION,CELL-LINES,SYSTEMS BIOLOGY,MODULE NETWORKS,PROGRESSION,MORTALITY,DISEASE,FOXM1,P53}},
  language     = {{eng}},
  location     = {{Ghent, Belgium}},
  number       = {{18}},
  pages        = {{i638--i644}},
  title        = {{Prediction of a gene regulatory network linked to prostate cancer from gene expression, microRNA and clinical data}},
  url          = {{http://doi.org/10.1093/bioinformatics/btq395}},
  volume       = {{26}},
  year         = {{2010}},
}
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