This article is part of the supplement: Selected Proceedings of the 2010 AMIA Summit on Translational Bioinformatics

Open Access Proceedings

Using gene co-expression network analysis to predict biomarkers for chronic lymphocytic leukemia

Jie Zhang12, Yang Xiang12, Liya Ding1, Kristin Keen-Circle3, Tara B Borlawsky14, Hatice Gulcin Ozer12, Ruoming Jin5, Philip Payne124 and Kun Huang12*

Author Affiliations

1 Department of Biomedical Informatics, The Ohio State University, OH, USA

2 Comprehensive Cancer Center, BISR, The Ohio State University, OH, USA

3 Nationalwide Children’s Hospital, OH, USA

4 Center for Clinical and Translational Science, The Ohio State University, OH, USA

5 Department of Computer Science, Kent State University, OH, USA

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BMC Bioinformatics 2010, 11(Suppl 9):S5  doi:10.1186/1471-2105-11-S9-S5

Published: 28 October 2010

Abstract

Background

Chronic lymphocytic leukemia (CLL) is the most common adult leukemia. It is a highly heterogeneous disease, and can be divided roughly into indolent and progressive stages based on classic clinical markers. Immunoglobin heavy chain variable region (IgVH) mutational status was found to be associated with patient survival outcome, and biomarkers linked to the IgVH status has been a focus in the CLL prognosis research field. However, biomarkers highly correlated with IgVH mutational status which can accurately predict the survival outcome are yet to be discovered.

Results

In this paper, we investigate the use of gene co-expression network analysis to identify potential biomarkers for CLL. Specifically we focused on the co-expression network involving ZAP70, a well characterized biomarker for CLL. We selected 23 microarray datasets corresponding to multiple types of cancer from the Gene Expression Omnibus (GEO) and used the frequent network mining algorithm CODENSE to identify highly connected gene co-expression networks spanning the entire genome, then evaluated the genes in the co-expression network in which ZAP70 is involved. We then applied a set of feature selection methods to further select genes which are capable of predicting IgVH mutation status from the ZAP70 co-expression network.

Conclusions

We have identified a set of genes that are potential CLL prognostic biomarkers IL2RB, CD8A, CD247, LAG3 and KLRK1, which can predict CLL patient IgVH mutational status with high accuracies. Their prognostic capabilities were cross-validated by applying these biomarker candidates to classify patients into different outcome groups using a CLL microarray datasets with clinical information.