BACKGROUND
chronic lymphocytic leukemia , also called b-cell cll, is the most common type of leukemia, which mainly affects adults.
nearly  <dig>  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  <cit> .
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  <cit> .
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  <cit> .
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  <cit> .
early determination of the cll subtype is central to the goal of providing evidence-based adaptive therapies  <cit> .
such adaptive therapies can decrease disease-related mortality and increase quality of life.
several biomarkers have proven helpful in supporting such disease staging  <cit> .
for example, the mutational status of igvh genes have been named in multiple studies as a biomarker for cll disease progression  <cit> .
however, testing igvh mutation status is costly and is not readily available in all clinical settings.
recently, cell membrane proteins such as zap <dig>  and cd <dig> have been proposed as biomarkers for cll prognosis  <cit> .
positive zap <dig> or cd <dig> tests have been shown to correlate with progressive cll.
while the identification of zap <dig> and its prognostic value represents progress toward more widespread and accessible cll staging, zap <dig> testing only yields definitive results when conducted during later, symptomatic phases of disease progression  <cit> .
and cd <dig> was later found to be an independent biomarker  <cit> .
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 recent reports on correlations between lag <dig> and lpl level and the mutation status of igvh genes in cll patients  <cit> .
given the preceding motivation to discover and utilize more timely, effective, and accessible cll biomarkers, we have investigated the use of gene co-expression network analysis to identify such prognostic factors.
gene co-expression networks are established by connecting genes with similar expression profiles across a group of subjects or in multiple studies.
the similarity of expression profiles is often measured by parameters such as the pearson correlation coefficients , with a pcc of  <dig> implying perfect correlation and pcc of - <dig> being completely negative correlation.
in a recent study, by using the well-known breast cancer biomarkers brca <dig> and brca <dig> as anchor genes, the authors were able to discover a new breast cancer biomarker, hmmr, whose expression profile highly correlates with those of the two anchor genes  <cit> .
in this project, we took a similar approach by studying genes co-expressed with zap <dig> in multiple datasets.
specifically, we selected  <dig> microarray datasets corresponding to multiple types of cancers from the gene expression omnibus  and used the codense algorithm to identify highly connected gene co-expression network spanning the entire genome.
we then narrowed down the gene list in the co-expression networks in which zap <dig> was involved by testing their capabilities for predicting the igvh status of the subjects using various machine learning feature selection methods.
the workflow for this approach is summarized in figure  <dig>  the biomarkers identified from the workflow were then subjected to validation by testing their prognostic power on another cll microarray dataset with patients clinical outcome information available.
RESULTS
identify genes in the co-expression network with zap <dig> using codense
using the codense algorithm with the settings described in the methods section, we identified  <dig> highly connected co-expression networks .
network  <dig>  includes the well known cll biomarker zap <dig>  interestingly, another cll biomarker cd <dig> is also included in this network.
four genes cd8a, cd3g, cd <dig> and cd3d, whose products are known to interact with zap <dig>  are also in this network.
the go-term enrichment analysis using ipa  revealed highly enriched biological functions related to leukemia, such as cell growth and proliferation, hematological system development and function, inflammatory response, and immunological disease..
identify genes in the zap <dig> co-expression network with differential expression levels between different igvh mutation groups in gds <dig> dataset
among these  <dig> genes, we further selected genes whose expression levels can predict igvh mutation status using the three steps outlined in the method section.
the p-values are the results of student’s t-test of comparing the igvh mutated vs. unmutated group, as well as comparing the cll patient vs. normal group.
predicting capability of individual genes on igvh mutational status
the preceding comparisons suggest that the co-expression network we have discovered is enriched with genes that differentially expressed between the igvh unmutated group and igvh mutated group.
in addition, since these genes are all related to zap <dig>  we focused on selecting candidate biomarkers from those listed in table  <dig>  we tested the predictive capacity of those  <dig> genes  relative to igvh mutational status using a linear classifier.
for each gene, the test was carried out using a cross-validation with 20% holdout of the samples, repeated  <dig> times.
the results of these analyses, including average accuracy, are shown in table  <dig>  for the three genes with the highest accuracy , we further explored the possibility of using aggregate features by testing two feature combinations.
for each aggregate feature, the same test was conducted with the same setting as done on individual genes.
we also examined the sub-cellular locations of each individual gene.
interestingly, most of the genes code for membrane proteins, which is a potential advantage for candidate biomarkers, since they can facilitate detection via effective and fast procedures such as flow cytometry.
the names with bold letter indicate predicting accuracy above 65%.
a linear classifier was used.
the cross-validation was carried out with 20% holdout.
each test was carried out independently.
selecting gene features using mrmr
as indicated in the method section, we also used the mrmr webtool  to select genes which form a broad feature set to differentiate the two groups.
specifically, we input the expression values of all the  <dig> genes and selected the setting with mutual information difference scheme based upon ten features .
validate the prognostic capability of the identified biomarkers using new cll microarray dataset
discussion
as shown in figure  <dig>  except for klrk <dig>  all above biomarker candidates have been known to interact with each other and/or with zap <dig>  here we discuss the potential of the selected genes based on literature survey and our results:
lag <dig> : lag <dig> product is involved in t-cell-dependent b-cell activation.
it has been shown to be a potential biomarker using experimental methods in a recent study  <cit> .
this observation not only partially validated our approach for identifying prognostic biomarkers for cll, but also suggested that our method is able to identify even better biomarker, given that il2rb and cd8a have stronger predictive power than lag <dig>
il2rb : expression of the il <dig> receptor subunits il2rb and il2rg on b-cells has been known to be a sign of cll  <cit> .
various drugs have been designed to target il <dig> in cll, even though it is not clear why some patients show relapse after the treatment  <cit> .
however, currently we are not aware of any study relating il2rb with igvh mutation status.
our results suggest that il2rb has a great potential of being a prognostic biomarker for cll.
cd8aandcd247: both are t-cell surface antigens, but expression of cd8a on b-cells has been reported in cll patients  <cit> .
since the samples for the data in gds <dig> are generated from mononuclear cells including both t-cells and b-cells, it is not clear what the origin of these molecules is.
regardless, they demonstrate comparable capacity in predicting igvh mutation status as zap <dig> and are worthy of further investigation.
klrk <dig> : klrk <dig> is also called cd <dig>  it is a member of c-type lectin-like family of type ii cell surface glycoproteins, which is expressed by nk cells, cd8+ cells and certain types of t-cells  <cit> .
klrk <dig> is involved in transmitting activation signals into these types of cells, but it has never been associated with cll or its prognosis.
there is no known interaction between klrk <dig> and other known or prognostic biomarkers identified in this paper, as indicated by its absence from the network generated using ipa based on known interactions .
it is speculated that the expression level change in klrk <dig> is probably a secondary effect of one or more of the rest of the biomarkers candidates, therefore whether including it or not seems not to affect the prognosis results.
in this paper, we employed the codense algorithm to identify  <dig> gene co-expression networks using  <dig> cancer datasets.
we found that the co-expression network containing zap <dig> is enriched with genes that show differential expression between the igvh unmutated and mutated groups, even though there is no cll data included in the original  <dig> datasets from which the network was constructed.
this finding suggests that the co-expression networks identified in this study can serve as a set of generic building blocks for biomarker selection and gene interaction in cancer studies <cit> .
a key issue in biomarkers discovery is to choose the candidates for experimental validation from vast amount of potential genes.
here we show that gene co-expression network analysis is an effective method for narrowing down the list of candidates.
however, there are two limitations to this approach that should be noted: first, the effectiveness of this approach has not been determined by a prospective experimental study; and second, the approach is based on known biomarkers and may miss novel markers that involve in different mechanisms or regulation pathways.
therefore, currently we plan to expand our study on all the co-expression networks that have been identified using codense.
another direction for the future study is to explore aggregate biomarkers of a combined group of gene products.
we demonstrated the feasibility of this approach in table <dig>  however, a more rigorous and systematic screening for different combinations of genes is needed, which is part of our ongoing study.
CONCLUSIONS
using frequent gene co-expression analysis, we have identified a set of genes, il2rb, cd8a, cd <dig>  lag <dig> and klrk <dig>  which are potential cll prognostic biomarkers.
their prognostic capabilities were cross-validated by applying these biomarkers to classify patient survival groups using a cll microarray datasets with patient clinical outcome information.

 chronic lymphocytic leukemia, also called b-cell cll, is the most common type of leukemia, which mainly affects adults. nearly 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. 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. 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. 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. early determination of the cll subtype is central to the goal of providing evidence-based adaptive therapies. such adaptive therapies can decrease disease-related mortality and increase quality of life. several biomarkers have proven helpful in supporting such disease staging. for example, the mutational status of igvh genes have been named in multiple studies as a biomarker for cll disease progression. however, testing igvh mutation status is costly and is not readily available in all clinical settings. recently, cell membrane proteins such as zap and cd have been proposed as biomarkers for cll prognosis. positive zap or cd tests have been shown to correlate with progressive cll. while the identification of zap and its prognostic value represents progress toward more widespread and accessible cll staging, zap testing only yields definitive results when conducted during later, symptomatic phases of disease progression. and cd was later found to be an independent biomarker. 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 recent reports on correlations between lag and lpl level and the mutation status of igvh genes in cll patients. given the preceding motivation to discover and utilize more timely, effective, and accessible cll biomarkers, we have investigated the use of gene co-expression network analysis to identify such prognostic factors. gene co-expression networks are established by connecting genes with similar expression profiles across a group of subjects or in multiple studies. the similarity of expression profiles is often measured by parameters such as the pearson correlation coefficients, with a pcc of implying perfect correlation and pcc of - being completely negative correlation. in a recent study, by using the well-known breast cancer biomarkers brca and brca as anchor genes, the authors were able to discover a new breast cancer biomarker, hmmr, whose expression profile highly correlates with those of the two anchor genes. in this project, we took a similar approach by studying genes co-expressed with zap in multiple datasets. specifically, we selected microarray datasets corresponding to multiple types of cancers from the gene expression omnibus and used the codense algorithm to identify highly connected gene co-expression network spanning the entire genome. we then narrowed down the gene list in the co-expression networks in which zap was involved by testing their capabilities for predicting the igvh status of the subjects using various machine learning feature selection methods. the workflow for this approach is summarized in figure the biomarkers identified from the workflow were then subjected to validation by testing their prognostic power on another cll microarray dataset with patients clinical outcome information available.  identify genes in the co-expression network with zap using codense using the codense algorithm with the settings described in the methods section, we identified highly connected co-expression networks. network includes the well known cll biomarker zap interestingly, another cll biomarker cd is also included in this network. four genes cd8a, cd3g, cd and cd3d, whose products are known to interact with zap are also in this network. the go-term enrichment analysis using ipa revealed highly enriched biological functions related to leukemia, such as cell growth and proliferation, hematological system development and function, inflammatory response, and immunological disease.. identify genes in the zap co-expression network with differential expression levels between different igvh mutation groups in gds dataset among these genes, we further selected genes whose expression levels can predict igvh mutation status using the three steps outlined in the method section. the p-values are the results of student’s t-test of comparing the igvh mutated vs. unmutated group, as well as comparing the cll patient vs. normal group. predicting capability of individual genes on igvh mutational status the preceding comparisons suggest that the co-expression network we have discovered is enriched with genes that differentially expressed between the igvh unmutated group and igvh mutated group. in addition, since these genes are all related to zap we focused on selecting candidate biomarkers from those listed in table we tested the predictive capacity of those genes relative to igvh mutational status using a linear classifier. for each gene, the test was carried out using a cross-validation with 20% holdout of the samples, repeated times. the results of these analyses, including average accuracy, are shown in table for the three genes with the highest accuracy, we further explored the possibility of using aggregate features by testing two feature combinations. for each aggregate feature, the same test was conducted with the same setting as done on individual genes. we also examined the sub-cellular locations of each individual gene. interestingly, most of the genes code for membrane proteins, which is a potential advantage for candidate biomarkers, since they can facilitate detection via effective and fast procedures such as flow cytometry. the names with bold letter indicate predicting accuracy above 65%. a linear classifier was used. the cross-validation was carried out with 20% holdout. each test was carried out independently. selecting gene features using mrmr as indicated in the method section, we also used the mrmr webtool to select genes which form a broad feature set to differentiate the two groups. specifically, we input the expression values of all the genes and selected the setting with mutual information difference scheme based upon ten features. validate the prognostic capability of the identified biomarkers using new cll microarray dataset discussion as shown in figure except for klrk all above biomarker candidates have been known to interact with each other and/or with zap here we discuss the potential of the selected genes based on literature survey and our results: lag: lag product is involved in t-cell-dependent b-cell activation. it has been shown to be a potential biomarker using experimental methods in a recent study. this observation not only partially validated our approach for identifying prognostic biomarkers for cll, but also suggested that our method is able to identify even better biomarker, given that il2rb and cd8a have stronger predictive power than lag il2rb: expression of the il receptor subunits il2rb and il2rg on b-cells has been known to be a sign of cll. various drugs have been designed to target il in cll, even though it is not clear why some patients show relapse after the treatment. however, currently we are not aware of any study relating il2rb with igvh mutation status. our results suggest that il2rb has a great potential of being a prognostic biomarker for cll. cd8aandcd247: both are t-cell surface antigens, but expression of cd8a on b-cells has been reported in cll patients. since the samples for the data in gds are generated from mononuclear cells including both t-cells and b-cells, it is not clear what the origin of these molecules is. regardless, they demonstrate comparable capacity in predicting igvh mutation status as zap and are worthy of further investigation. klrk: klrk is also called cd it is a member of c-type lectin-like family of type ii cell surface glycoproteins, which is expressed by nk cells, cd8+ cells and certain types of t-cells. klrk is involved in transmitting activation signals into these types of cells, but it has never been associated with cll or its prognosis. there is no known interaction between klrk and other known or prognostic biomarkers identified in this paper, as indicated by its absence from the network generated using ipa based on known interactions. it is speculated that the expression level change in klrk is probably a secondary effect of one or more of the rest of the biomarkers candidates, therefore whether including it or not seems not to affect the prognosis results. in this paper, we employed the codense algorithm to identify gene co-expression networks using cancer datasets. we found that the co-expression network containing zap is enriched with genes that show differential expression between the igvh unmutated and mutated groups, even though there is no cll data included in the original datasets from which the network was constructed. this finding suggests that the co-expression networks identified in this study can serve as a set of generic building blocks for biomarker selection and gene interaction in cancer studies. a key issue in biomarkers discovery is to choose the candidates for experimental validation from vast amount of potential genes. here we show that gene co-expression network analysis is an effective method for narrowing down the list of candidates. however, there are two limitations to this approach that should be noted: first, the effectiveness of this approach has not been determined by a prospective experimental study; and second, the approach is based on known biomarkers and may miss novel markers that involve in different mechanisms or regulation pathways. therefore, currently we plan to expand our study on all the co-expression networks that have been identified using codense. another direction for the future study is to explore aggregate biomarkers of a combined group of gene products. we demonstrated the feasibility of this approach in table however, a more rigorous and systematic screening for different combinations of genes is needed, which is part of our ongoing study.  using frequent gene co-expression analysis, we have identified a set of genes, il2rb, cd8a, cd lag and klrk which are potential cll prognostic biomarkers. their prognostic capabilities were cross-validated by applying these biomarkers to classify patient survival groups using a cll microarray datasets with patient clinical outcome information. 
we selected microarray datasets corresponding to multiple types of cancer from the gene expression omnibus 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 zap is involved. 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. however, biomarkers highly correlated with igvh mutational status which can accurately predict the survival outcome are yet to be discovered. we have identified a set of genes that are potential cll prognostic biomarkers il2rb, cd8a, cd lag and klrk which can predict cll patient igvh mutational status with high accuracies. we then applied a set of feature selection methods to further select genes which are capable of predicting igvh mutation status from the zap co-expression network. in this paper, we investigate the use of gene co-expression network analysis to identify potential biomarkers for cll. immunoglobin heavy chain variable region 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. specifically we focused on the co-expression network involving zap a well characterized biomarker for cll. chronic lymphocytic leukemia is the most common adult leukemia.
 chronic lymphocytic leukemia 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 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 zap a well characterized biomarker for cll. we selected microarray datasets corresponding to multiple types of cancer from the gene expression omnibus 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 zap 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 zap co-expression network.  we have identified a set of genes that are potential cll prognostic biomarkers il2rb, cd8a, cd lag and klrk 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. 10– march amia summit on translational bioinformatics san francisco, ca, usa 