Open Access Open Badges Research article

Multivariate meta-analysis of proteomics data from human prostate and colon tumours

Lina Hultin Rosenberg1, Bo Franzén2, Gert Auer3, Janne Lehtiö1 and Jenny Forshed1*

Author Affiliations

1 Clinical Proteomics, Department of Oncology-Pathology, Karolinska Institute/Karolinska University Hospital, Stockholm, Sweden

2 Molecular Pharmacology, AstraZeneca R&D Södertälje, Sweden

3 Department of Oncology-Pathology, Karolinska Institute/Karolinska University Hospital, Stockholm, Sweden

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BMC Bioinformatics 2010, 11:468  doi:10.1186/1471-2105-11-468

Published: 17 September 2010



There is a vast need to find clinically applicable protein biomarkers as support in cancer diagnosis and tumour classification. In proteomics research, a number of methods can be used to obtain systemic information on protein and pathway level on cells and tissues. One fundamental tool in analysing protein expression has been two-dimensional gel electrophoresis (2DE). Several cancer 2DE studies have reported partially redundant lists of differently expressed proteins. To be able to further extract valuable information from existing 2DE data, the power of a multivariate meta-analysis will be evaluated in this work.


We here demonstrate a multivariate meta-analysis of 2DE proteomics data from human prostate and colon tumours. We developed a bioinformatic workflow for identifying common patterns over two tumour types. This included dealing with pre-processing of data and handling of missing values followed by the development of a multivariate Partial Least Squares (PLS) model for prediction and variable selection. The variable selection was based on the variables performance in the PLS model in combination with stability in the validation. The PLS model development and variable selection was rigorously evaluated using a double cross-validation scheme. The most stable variables from a bootstrap validation gave a mean prediction success of 93% when predicting left out test sets on models discriminating between normal and tumour tissue, common for the two tumour types. The analysis conducted in this study identified 14 proteins with a common trend between the tumour types prostate and colon, i.e. the same expression profile between normal and tumour samples.


The workflow for meta-analysis developed in this study enabled the finding of a common protein profile for two malign tumour types, which was not possible to identify when analysing the data sets separately.