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. 2011 Dec 21:12:488.
doi: 10.1186/1471-2105-12-488.

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

Stephan Gade  1 , Christine Porzelius, Maria Fälth, Jan C Brase, Daniela Wuttig, Ruprecht Kuner, Harald Binder, Holger Sültmann, Tim Beissbarth
Affiliations

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

Stephan Gade et al. BMC Bioinformatics. .

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.

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Figures

Figure 1
Figure 1
Comparison of Prediction Error Curves. The figure shows the prediction error curves of CoxBoost models trained on the mRNA and miRNA data. The prediction model was trained with and without the bipartite graph describing the relations between the features. The incorporation of the graph resulted in a reduction of the prediction error. The .632 estimation of the prediction error was used in this plot, averaging over the 500 bootstrap samples. As a reference the prediction error of the Kaplan-Meier estimator is shown.
Figure 2
Figure 2
Workflow Diagram. Workflow of the integration of miRNA and mRNA expression data.
See this image and copyright information in PMC

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