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

Zhang J, Xiang Y, Ding L, Keen-Circle K, Borlawsky TB, Ozer HG, Jin R, Payne P, Huang K - BMC Bioinformatics (2010)

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Biomedical Informatics, The Ohio State University, OH, USA. jie.zhang@osumc.edu

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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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.
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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.

Mentions: Figure 4 shows the Kaplan-Meier curves of patient time-to-treatment (TTT) from microarray data (GSE10138) of 61 CLL patients, using ZAP70 and all above identified biomarker candidates as features to categorize the patients into two risk groups. TTT is the time point when the disease evolves from indolent stage into progressive stage, signifying the switch from low to higher risk group, therefore is a suitable parameter to test our biomarkers for prognosis. The above biomarker candidates clearly separated the patients into two risk groups using the K-means algorithm (K=2), with the log-rank test p-value as low as 0.033. However, if only using ZAP70 as the feature to separate the patients, the difference of TTT between the two groups is not significant (log-rank test p > 0.05, figure not shown). Interestingly, with or without KLRK1, the patient grouping results and p-values stay the same.


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

Zhang J, Xiang Y, Ding L, Keen-Circle K, Borlawsky TB, Ozer HG, Jin R, Payne P, Huang K - BMC Bioinformatics (2010)

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.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2967746&req=5

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.
Mentions: Figure 4 shows the Kaplan-Meier curves of patient time-to-treatment (TTT) from microarray data (GSE10138) of 61 CLL patients, using ZAP70 and all above identified biomarker candidates as features to categorize the patients into two risk groups. TTT is the time point when the disease evolves from indolent stage into progressive stage, signifying the switch from low to higher risk group, therefore is a suitable parameter to test our biomarkers for prognosis. The above biomarker candidates clearly separated the patients into two risk groups using the K-means algorithm (K=2), with the log-rank test p-value as low as 0.033. However, if only using ZAP70 as the feature to separate the patients, the difference of TTT between the two groups is not significant (log-rank test p > 0.05, figure not shown). Interestingly, with or without KLRK1, the patient grouping results and p-values stay the same.

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Biomedical Informatics, The Ohio State University, OH, USA. jie.zhang@osumc.edu

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.

Show MeSH
Related in: MedlinePlus