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

BMC Bioinformatics, Oct 2010

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

Jie Zhang 0 2 Yang Xiang 0 2 Liya Ding 0 Kristin Keen-Circle 1 Tara B Borlawsky 0 4 Hatice Gulcin Ozer 0 2 Ruoming Jin 3 Philip Payne 0 2 4 Kun Huang 0 2 0 Department of Biomedical Informatics, The Ohio State University , OH , USA 1 Nationalwide Children's Hospital , OH , USA 2 Comprehensive Cancer Center, BISR, The Ohio State University , OH , USA 3 Department of Computer Science, Kent State University , OH , USA 4 Center for Clinical and Translational Science, The Ohio State University , OH , USA 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 coexpression 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. - From 2010 AMIA Summit on Translational Bioinformatics San Francisco, CA, USA. 10-12 March 2010 Background Chronic lymphocytic leukemia (CLL), also called B-cell CLL, is the most common type of leukemia, which mainly affects adults. Nearly 100,000 Americans live with CLL, most of them over fifty years old. Rates of CLL incidence are increasing, and there is no known cure for the disease [1]. For patients diagnosed with CLL, staging or classification systems such as the widely adopted Rai and Binet staging systems can categorize the patients into classes with different risk levels [2]. However, currently these systems still have difficulty in discriminating indolent and progressive CLL. Specifically, some patients remain in the beginning or indolent stage of the disease and do not require treatment, which involves numerous undesirable side effect, for time periods of up to ten or more years [3,4]. In contrast, some patients experience very aggressive disease in a short time period, characterized by rapid white blood cell doubling time, and requiring immediate treatment. These differences delineate two distinct groups of patients: indolent and progressive CLL. Those with the non-progressive manifestation of the disease rarely need treatment until the disease transforms into an aggressive state and they become increasingly symptomatic [5]. Early determination of the CLL subtype is central to the goal of providing evidence-based adaptive therapies [6]. Such adaptive therapies can decrease disease-related mortality and increase quality of life. Several biomarkers have proven helpful in supporting such disease staging [4]. For example, the mutational status of IgVH genes have been named in multiple studies as a biomarker for CLL disease progression [5,7,8]. However, testing IgVH mutation status is costly and is not readily available in all clinical settings. Recently, cell membrane proteins such as ZAP70 (Zeta-chain-associated protein kinase 70) and CD38 have been proposed as biomarkers for CLL prognosis [5,9,10]. Positive ZAP70 or CD38 tests have been shown to correlate with progressive CLL. While the identification of ZAP70 and its prognostic value represents progress toward more widespread and accessible CLL staging, ZAP70 testing only yields definitive results when conducted during later, symptomatic phases of disease progression [11]. And CD38 was later found to be an independent biomarker [12]. A more desirable method would be to determine biomarkers or phenotypic parameters that are able to definitively determine the likelihood with which a patient may develop rapid disease progression early in the pathophysiologic development of CLL. Thus researchers are still searching for new CLL biomarkers as illustrated in (...truncated)


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Jie Zhang, Yang Xiang, Liya Ding, Kristin Keen-Circle, Tara B Borlawsky, Hatice Ozer, Ruoming Jin, Philip Payne, Kun Huang. Using gene co-expression network analysis to predict biomarkers for chronic lymphocytic leukemia, BMC Bioinformatics, 2010, pp. S5, 11, DOI: 10.1186/1471-2105-11-S9-S5