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. 2010 Oct 28;11 Suppl 9(Suppl 9):S5.
doi: 10.1186/1471-2105-11-S9-S5.

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

Jie Zhang  1 , Yang Xiang, Liya Ding, Kristin Keen-Circle, Tara B Borlawsky, Hatice Gulcin Ozer, Ruoming Jin, Philip Payne, Kun Huang
Affiliations

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

Jie Zhang et al. BMC Bioinformatics. .

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.

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Figures

Figure 1
Figure 1
The workflow to identify genes co-expressed with ZAP70 in multiple cancer datasets using co-expression network analysis.
Figure 2
Figure 2
The connectivity graph for Network 17. The connectivity ratio r for this network is 0.4142. The names with circle are the genes which product known to interact with ZAP70.
Figure 3
Figure 3
The top 10 enriched biological functions of Network 17 genes using IPA.
Figure 4
Figure 4
The Kaplan-Meier curves of the two groups of CLL patients in the dataset GSE10138 using unsupervised K-mean clustering. The biomarkers used to generate the survival curves are: ZAP70, LAG3, IL2RB, CD247, CD8A and KLRK1.
Figure 5
Figure 5
The known interactions among potential prognostic biomarkers and ZAP70. The interactions were extracted from Ingenuity Pathway Knowledge database. The abbreviations for interaction types: A: activation; L: proteolysis; M: biochemical modification; P: phosphorylation/dephosphorylation; LO: localization; MB: group/complex membership; PP: protein-protein binding; RB: regulation of binding.
See this image and copyright information in PMC

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