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

Eric Bonnet, Tom Michoel, Yves Van de Peer

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)i638-i644
Number of pages7
JournalBioinformatics
Volume26
Issue number18
DOIs
Publication statusPublished - Sep 2010
EventEuropean Conference on Computational biology (ECCB) - Ghent, Belgium
Duration: 26 Sep 201029 Sep 2010

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