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Open Access Highly Accessed Research article

Graph based fusion of miRNA and mRNA expression data improves clinical outcome prediction in prostate cancer

Stephan Gade1*, Christine Porzelius2, Maria Fälth1, Jan C Brase1, Daniela Wuttig1, Ruprecht Kuner1, Harald Binder24, Holger Sültmann1 and Tim Beißbarth3*

Author Affiliations

1 German Cancer Research Center, Cancer Genome Research, Im Neuenheimer Feld 460, 69120 Heidelberg, Germany

2 Institute of Medical Biometry and Medical Informatics, University Medical Center Freiburg, 79104 Freiburg, Germany

3 University Medical Center Göttingen, Medical Statistics, 37099 Göttingen, Germany

4 Institute of Medical Biometry, Epidemiology and Informatics (IMBEI), Working Group Medical Biometry, University Medical Center Johannes Gutenberg University Mainz, 55101 Mainz, Germany

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BMC Bioinformatics 2011, 12:488  doi:10.1186/1471-2105-12-488

Published: 21 December 2011

Abstract

Background

One of the main goals in cancer studies including high-throughput microRNA (miRNA) and mRNA data is to find and assess prognostic signatures capable of predicting clinical outcome. Both mRNA and miRNA expression changes in cancer diseases are described to reflect clinical characteristics like staging and prognosis. Furthermore, miRNA abundance can directly affect target transcripts and translation in tumor cells. Prediction models are trained to identify either mRNA or miRNA signatures for patient stratification. With the increasing number of microarray studies collecting mRNA and miRNA from the same patient cohort there is a need for statistical methods to integrate or fuse both kinds of data into one prediction model in order to find a combined signature that improves the prediction.

Results

Here, we propose a new method to fuse miRNA and mRNA data into one prediction model. Since miRNAs are known regulators of mRNAs we used the correlations between them as well as the target prediction information to build a bipartite graph representing the relations between miRNAs and mRNAs. This graph was used to guide the feature selection in order to improve the prediction. The method is illustrated on a prostate cancer data set comprising 98 patient samples with miRNA and mRNA expression data. The biochemical relapse was used as clinical endpoint. It could be shown that the bipartite graph in combination with both data sets could improve prediction performance as well as the stability of the feature selection.

Conclusions

Fusion of mRNA and miRNA expression data into one prediction model improves clinical outcome prediction in terms of prediction error and stable feature selection. The R source code of the proposed method is available in the supplement.