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

• Published in: BMC bioinformatics Vol. 11 Suppl 9; no. S9; p. S5
• Main Authors: Zhang, Jie, Xiang, Yang, Ding, Liya, Keen-Circle, Kristin, Borlawsky, Tara B, Ozer, Hatice Gulcin, Jin, Ruoming, Payne, Philip, Huang, Kun
• Format: Journal Article
• Language: English
• Published: England BioMed Central 28.10.2010; BioMed Central Ltd; BMC
• Subjects: Algorithms; Biomarkers; Biomarkers - analysis; Computer science; Databases, Genetic; Gene Expression; Gene Regulatory Networks; Humans; Immunoglobulin Heavy Chains - immunology; Immunoglobulin Variable Region - chemistry; Immunoglobulin Variable Region - immunology; Kinases; Leukemia, Lymphocytic, Chronic, B-Cell - genetics; Leukemia, Lymphocytic, Chronic, B-Cell - metabolism; Mortality; Studies; ZAP-70 Protein-Tyrosine Kinase
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/1471-2105-11-S9-S5

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. 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. 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.