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

BMC Bioinformatics

Research Article

Maria Fälth¹  Ruprecht Kuner¹  Holger Sültmann¹  Jan C Brase¹  Stephan Gade¹  Daniela Wuttig¹  Christine Porzelius²  Harald Binder³  Tim Beißbarth⁴ 

[1] German Cancer Research Center, Cancer Genome Research, Im Neuenheimer Feld 460, 69120, Heidelberg, Germany;Institute of Medical Biometry and Medical Informatics, University Medical Center Freiburg, 79104, Freiburg, Germany;Institute of Medical Biometry, Epidemiology and Informatics (IMBEI), Working Group Medical Biometry, University Medical Center Johannes Gutenberg University Mainz, 55101, Mainz, Germany;Institute of Medical Biometry and Medical Informatics, University Medical Center Freiburg, 79104, Freiburg, Germany;Medical Statistics, University Medical Center Göttingen, 37099, Göttingen, Germany;

关键词: Feature Selection;    Prediction Error;    Bipartite Graph;    Lasso;    Bootstrap Sample;   

DOI  :  10.1186/1471-2105-12-488

 received in 2011-09-23, accepted in 2011-12-21,  发布年份 2011

来源: Springer

【 摘 要 】

BackgroundOne 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.ResultsHere, 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.ConclusionsFusion 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.

【 授权许可】

CC BY   
© Gade et al.; licensee BioMed Central Ltd. 2011