BACKGROUND
colon rectal cancer  is the third most commonly diagnosed cancer worldwide.
it is the second leading cause of cancer death in the united states, and worldwide, nearly  <dig>  deaths are reported every year due to crc.
the crc incidence rate varies across the globe.
for example, low incidence rates for crc have been associated with asian and african populations.
dietary and environmental factors have also been known to contribute to crc patterns  <cit> .
therefore, we postulate that there are some common as well as some unique key gene signatures that can discriminate crc across populations.
due to the advent of high through-put technologies, a multitude of public domain expression datasets are now available for crc research.
these datasets are generated worldwide and deposited with the objective of identifying key molecules that play an important role in different stages of crc.
gene-expression profiling and meta-analysis have been extensively used to: a) understand the mechanisms that drive a normal cell to become a cancer cell, b) understand different metastatic levels  <cit> , and c) identify biomarkers  <cit> .
differentially expressed genes have been identified as biomarkers in leukemia, b-cell lymphoma, breast and lung cancers  <cit> .
gene signatures are a set of genes that might play an important role in a given disease.
using gene expression datasets, gene signatures were identified in different cancers  <cit> .
first attempts to identify gene signatures from gene expression were done in breast cancer  <cit> .
genes combine together and act as pathways to perform a biological function and genes in a given pathway are co-expressed  <cit> .
large-scale efforts are being made to identify the biomarkers associated with specific pathways and biological function using gene expression profiles  <cit> .
a single pathway can be deregulated by different mechanisms or combination of genes.
also, a set of genes can target one or many pathways.
gene signatures can help to identify these patterns in pathways and also the relationships among them  <cit> .
first attempts for identifying gene signatures were done for breast cancer  <cit>  and have since been used in various other cancers as well  <cit> .
network based approaches have been used to identify subnetwork markers  that are more reproducible than individual markers  <cit> .
functional modules extracted from networks are groups of genes with same functions  <cit> .
the genes in the subnetworks are co-expressed  and they share more interactions among them, than with other genes in the larger network  <cit> .
these functional modules can be used to identify both similar and unique biological characteristics among different species datasets  <cit>  and are also considered to be subnetworks  <cit> .
in protein-protein interaction networks, these functional modules are present as sub-graphs or tightly connected sub-graphs  <cit>  and can be analyzed with respect to their individual characteristics using either gene ontology similarities or pathway significance  <cit> .
identification of regulatory modules or gene subnetworks is important as they play critical roles in biological processes  <cit>  and their associated pathways can provide potential targets for drug intervention in cancers  <cit> .
though gene signatures can improve understanding of a disease, identification of these signatures across populations is difficult, as gene expression is known to vary between populations  <cit> .
even though modules have been effectively used for the identification of gene signatures, this approach is computationally complex because the modules are open subnetworks  <cit> , meaning that within a disease network, a very large number of modules will be identified  <cit> .
therefore, use of modules for comparing gene signatures across populations is computationally an intractable problem.
though attempts have recently been made to understand the difference in crc between african-americans and european-americans  <cit>  using a systems biology approach.
however, not much work has been done in the area of gene signature identification across populations with respect to crc.
due to the complexity of gene signature identification, we propose the use of cliques as an alternative to modules for the comparison of gene signatures across populations.
cliques are closed, fully-connected subnetworks.
the genes that are identified as part of these cliques are functionally related and highly co-expressed  <cit> .
since cliques are closed networks, they are both computationally tractable and more conserved in the biological networks  <cit> .
a clique consists of molecules that can be associated with one or many pathways and these molecules are related with their gene ontologies  <cit> .
a recent study reported the use of cliques in elucidating the mechanisms involved in breast cancer  <cit> .
in this paper we have attempted to understand crc gene signatures across four different populations: usa, germany , china , and saudi arabia .
the studies on each of these populations were conducted separately, and the data was downloaded from public repositories geo http://www.ncbi.nlm.nih.gov/geo.
for the study model, we hypothesized that tumors target biological modules that execute specific biological processes  <cit> .
since cliques are fully connected conserved subnetworks within biological networks, our hypothesis is that they are conserved across populations and can be understood as gene signatures.
therefore we propose to understand these cliques in crc across populations.
in this work we integrated the expression data along with network topological features and biological features.
cliques were then scored based on these features.
our work identified the common and unique cliques across populations that were important with respect to crc.
to identify the important cliques we analyzed the networks based on the following perspectives:  identification of genes from individual datasets based on p-value;  construction of gene networks for each population;  annotation of nodes and edges of networks with topological and biological features;  identification of cliques across networks;  comparison of the cliques in all the networks based on their strength and connectivity profiles; and,  evaluation of the cliques as gene signatures based on their biological significance in crc.
RESULTS
jin order to decipher the gene signatures and identify the similarity-uniqueness among the four different populations of crc , we developed a methodology as described in figure  <dig>  our methodology involved identifying genes in each dataset that satisfied the two sample t-test, construction of the gene networks using human protein reference database   <cit> , obtaining the gene expression profiles , identifying cliques in each dataset and comparing them across the populations, and connecting the cliques in each network to identify a clique connectivity profile  and comparing them across populations.
data analysis
the gene expression in all the four datasets was first normalized using the r-package rma algorithm  <cit> .
the two-sample t-test was used to identify the differentially expressed genes in each dataset.
the genes satisfying the t-test  in each dataset were then used to construct the networks.
figure  <dig> shows the profile of gene expression across the population dataset.
network construction
to construct the gene network for each population, we used only those genes that coded for proteins present in the hprd database  <cit> .
the networks were compared with respect to their node similarity.
table  <dig> shows the node  similarity across the four populations.
as shown in table  <dig>  a large number of genes were common among usa, chn and ger, but there were fewer genes common with sa.
country
analysis of population specific networks
to analyze these population-specific networks with respect to their topological and biological features, these networks were first compared with the hprd network for their interactions, degree, diameter, and average path length.
table  <dig> shows the results of this comparison.
the average path-length is the overall ease with which the genes in the network communicate with each other.
though the degree and number of interactions differ for ger, usa, and sa with hprd, the diameter and the average path lengths of these networks is in accordance with hprd.
therefore these networks have the ability to generate functional complexes or modules and can be analyzed with respect to their biological processes.
hprd had the highest number of interactions.
this illustrates that all four population networks are sub-networks within hprd
for further analysis of the networks, pearson correlation coefficients  were computed for each edge, and correlations greater than  <dig>  and less than - <dig>  were considered.
this reduced the size and the complexity in the networks.
analysis of node strength based on topological properties
the node strength for each node in the population specific network was computed using the topological parameters - namely, eccentricity, closeness and betweenness , , , and ).
the common high scoring nodes identified in all the population were: tp <dig>  src, esr <dig>  smad <dig>  grb <dig>  ep <dig>  crebbp, egfr, smad <dig>  and csn2ka <dig>  of these, the transcription factors tp <dig>  esr <dig>  smad <dig>  and smad <dig> were identified as important in crc  <cit> .
grb <dig> overexpression has also been identified in crc  <cit>  it was reported that binding of grb <dig> with gab <dig> plays an important role in crc  <cit> .
gab <dig> additionally interacts with bcr-abl, activating bcr-abl-associated crc significant pathways - specifically, pi3k-mtor, ras/raf/mek/erk, and jak-stat  <cit> .
ep <dig> has also been identified to be involved in crc  <cit> .
there were also a few unique top scoring nodes: atxn <dig> for ger and prkca, prkaca and ubqln <dig> for sa.
not many references were available for ubqln <dig> and atxn <dig> in crc, although atxn <dig> has been associated with cancer pathways  <cit> .
the topological analysis revealed top scoring genes that are common  as well as unique  with their significance in crc across the four populations.
analysis of biological features for population specific networks
the biological feature analysis provided the edge strength for all the genes in all four population networks.
the three biological features considered for the analysis were: pcc, gene ontology distance, and pathway similarity score.
the pcc had already been computed during the network construction process as described earlier ).
the number of edges satisfying the pcc in each population network was:  <dig> ,  <dig> ,  <dig> , and  <dig> .
as it is known that, biological processes are essentially a series of events accomplished by one or more molecular functions.
each node in the network was associated with its biological processes.
gene ontology distance was computed across an edge between two nodes in the network ).
the number of unique gene ontology biological processes terms identified in each of the population networks were:  <dig> ,  <dig> ,  <dig> , and  <dig> .
the following gene ontology biological processes were associated with maximum number of genes in all networks: go:  <dig> , go:  <dig> , go:  <dig> , and go:  <dig> .
of these processes, signal transduction pathways are currently used as therapeutic targets in crc  <cit> , and immune response has been associated with crc progression  <cit> .
since these biological processes are known to be important in crc, we concluded that go biological process should be a key feature for computing the edgestrength ).
another key biological feature that we use was the pathway similarity score between two nodes.
the pathways were identified using the kegg database  <cit> , and the number of unique pathways identified for the respective populations were:  <dig> ,  <dig> ,  <dig> , and  <dig> .
there were a total of  <dig> unique pathways across all the four populations: forty-nine pathways were common to all four countries, thirty-five were common to three countries, seven were common to two countries, and five were unique to one country.
the crc pathways identified across all the countries were: chemokine signaling pathways, wnt-signaling pathway, mapk signaling pathway, jak-stat pathway, calcium-signaling pathway, erbb signaling pathway, and pathways in cancer  <cit> .
the association of major crc pathways with the nodes thus justified the use of the pathway similarity score as an essential feature for computing edge strength.
each node in the network was annotated with its pathway, and the pathway similarity score was computed across an edge of two nodes in the network ).
through these various analyses, we obtained the topological and biological features for all four populations to compute the nodestrength and edgestrengths for their respective genes in the networks.
identification of cliques
genes with similar expression patterns across various networks perform similar functions  <cit> .
both functional modules and interacting modules have similar co-expressed genes  <cit> .
based on this understanding, we designed an algorithm that identified the cliques  in each population network.
figure  <dig> shows the distribution of the number of identified cliques of different sizes in each of the population-specific network.
figure  <dig>  shows the total number of unique cliques identified for all four populations.
the largest number of cliques  was identified for usa and minimum for sa respectively.
in this analysis, we considered only cliques of node size seven, as this size was found to be consistent across all four population networks, while cliques of higher sizes were not found across all the populations.
for the specified clique size of seven, a total of  <dig> cliques were identified across the four populations.
these cliques were then further analyzed with respect to their distribution in the different populations.
there were  <dig> cliques common to all populations, while  <dig>   <dig>  and  <dig> unique clique were identified in usa, ger, and chn, respectively.
figure  <dig>  shows the venn diagram for the distribution of size seven cliques across the four populations.
the total number of genes identified in these cliques within each population network was:  <dig> ,  <dig> ,  <dig> , and  <dig> .
we identified  <dig> genes in total, with  <dig> of those genes common among all cliques across the populations.
analysis of cliques common across the populations
to understand the significance of the cliques across the populations, we first analyzed all the cliques with respect to their go biological processes and pathway associations.
the numbers of go biological processes associated with cliques for each population network were:  <dig> ,  <dig> ,  <dig> , and  <dig> ,  <dig> .
go terms with hyper-geometric p-values <  <dig>  for each population were identified ); go:  <dig>  go:  <dig> and go:  <dig> were identified as the processes with the smallest hyper-geometric p-values across all cliques in all population networks.
go: <dig> was associated with signaling pathways, which have been identified as the targets for crc  <cit> .
go:  <dig> was associated with cell division cycle.
previous studies found genes involved in cell cycle, apoptosis, and invasion to play an important role in crc  <cit> .
go:  <dig> was associated with protein phosphorylation.
tgf-beta is a key pathway involved in crc, and progression of this pathway is known to be dependent on protein phosphorylation  <cit> .
the validation of these pathways with respect to involvement in crc supports that clique nodes are involved in important go biological processes, and these enriched go terms are key features by which crc can be evaluated across populations.
the identified cliques were further analyzed using go term finder  <cit> , and david level  <dig>  <cit> .
cliques identified as significant  were further evaluated based on literature.
table  <dig> shows the details of a few common cliques identified in all the population networks and their gene significance in crc.
it was also observed that some of the genes in these common cliques have been widely studied in terms of crc, while others have relatively sparse available literature.
go term finder and david level  <dig>  were considered for analysis.
the clique {egfr, esr <dig>  grb <dig>  pik3r <dig>  ptpn <dig>  shc <dig>  src} in table  <dig> was enriched in the following go biological processes: egfr signaling pathway, cellular response to growth, and signal transduction; most of the genes in this clique have been identified as significant in crc.
for example, targeted therapy using egfr is currently available for crc  <cit> .
egfr is a trans-membrane tyrosine kinase receptor belonging to her family of cell surface receptor; it is triggered by ligands and leads to the activation of many intracellular signal transduction pathways   <cit>  which are known to effect the activation of many transcription factors involved in cellular response .
esr <dig> is used as a epigenetic marker  <cit> , while activation of grb2/sos, leads to a cytoplasmic phosphorylation cascade involving kras  <cit> .
kras pathways are the targeted pathways in crc  <cit> , ptpn <dig> mutation has not been identified specifically in crc, but it has been found in other cancers, including lymphoma and leukemia.
similarly, although shc <dig> has not been identified directly in crc, it has been identified in lung cancer  <cit> .
the clique {brca <dig>  crebbp, ep <dig>  esr <dig>  smad <dig>  smad <dig>  tp53} was enriched with the following go biological processes: regulation of transcription, response to lipid, and positive regulation of cellular metabolic process.
again, these genes have all been studied in terms of crc or other cancers.
crebbp and ep <dig> have been identified as prognostic markers for crc  <cit> , and ep <dig> is additionally been identified in lipid metabolism in crc  <cit> .
some studies have identified brca <dig>  <cit> , up-regulation of crebbp  <cit> , mutation of ep <dig>  loss of smad <dig> signaling  <cit>  in crc.
the tgf-beta signaling pathway is known to play an important role as tumor suppressor and tumor promoters in crc by activating the smad2/smad <dig> complex, which enters the nucleus to further regulate transcription  <cit> .
overexpression and mutation of tp <dig> has also been associated with crc  <cit> .
the clique {csn <dig>  csn <dig>  csn <dig>  csn <dig>  csn <dig>  csn7a, csn <dig>  and tp53} is a part of signalosome of csn <dig>  which acts as protein kinase.
the enriched go biological processes were protein deneddylation and cellular protein metabolism.
csn-mediated protein deneddylation has been identified in the literature to promote hedgehog-pathways, though it has not been reported specifically in crc  <cit> .
csn <dig> has been identified as essential for cell proliferation in hepatocellular carcinoma, while csn <dig> is known to be a regulator of tp <dig> and mdm2; additionally, csn <dig> is known to be important for regulating dna-damage-associated apoptosis and tumor genesis, as well as enhancing p53-mediated tumor suppression http://www.genedistiller.org.
mdm <dig> has been identified as a probable therapeutic target for crc  <cit> .
in the clique {dis <dig>  exosc <dig>  exosc <dig>  exosc <dig>  exosc <dig>  exosc <dig>  exosc <dig>  and mpp6}, the enriched go biological processes identified were: dna modification, dna deamination, and mrna catabolic process.
a form of dna modification that is used to identify many cancers is dna methylation in the promoter regions, which causes silencing of many genes  <cit> .
dis <dig>  which has been identified in cancer genomes, stabilizes rna and its translation into proteins  <cit> , while exosc <dig> is involved in ribosome biogenesis and is highly up-regulated in crc  <cit> .
though not much has been reported about the other genes in this clique with respect to crc, they have been identified in other cancers.
one of the unique cliques identified for usa was {lsm <dig>  lsm <dig>  lsm <dig>  lsm <dig>  lsm <dig>  lsm <dig>  smn1}.
lsm <dig> is mapped on chromosome 8p <dig> , which has been identified in both prostate cancer  <cit>  and crc.
although smn <dig> has not been identified in cancer directly, it has been proposed to interact with bcl- <dig>  which is associated with crc, and has a high prognostic value  <cit> .
from this analysis, it can be stated that common and unique cliques identified across population networks are involved in important biological processes in crc.
these cliques include genes that are both well-studied and less-studied in crc, as well as those known to play a role in other cancers, indicating their importance in crc networks and in better understanding crc across populations.
this analysis also demonstrates the importance of cliques in the crc disease and can be used to understand the four population-specific networks.
analysis of pathways associated with genes in cliques for all populations
cliques identified in the population-specific networks were further analyzed using the kegg database for their pathway similarity score.
figure  <dig> shows the profile of pathways associated with the maximum number of genes in each network.
this association varies across populations.
for example, pathways in cancer is associated with the highest number of genes in all the populations -  <dig> ,  <dig> ,  <dig> , and  <dig> ;many of the different pathways that belong to the domain of pathways in cancer in the kegg database were discussed in the previous section.
though jak-stat pathways were identified to be associated with clique-genes in all the population, the level of association, as defined by number of clique-genes identified, was higher in ger  than in sa .
similar observations of varying levels of association were made for many pathways, as can be seen in figure  <dig>
based on our analysis of the common cliques, unique cliques, and pathways associated with the cliques for all four populations, gene signatures for crc can then be developed from the genes identified in these cliques.
specific gene signatures for each individual population could also be developed using the unique cliques that were identified in each population.
analysis of cliquestrength
the parameter cliquestrength was computed for all cliques in the population-specific networks based on both topological and biological features ).
cliques associated with high cliquestrength were considered important in these networks.
to assess the usefulness of the cliquestrength parameter, we analyzed two top-scored cliques that were common or unique across the populations.
table  <dig> shows the top scored cliques in each network, their associated biological processes , and the genes associated with each process.
table  <dig> additionally shows that the cliquestrength of a common clique varies across population, as can be seen by comparing scores for the first clique in usa  and chn ; this is due to the fact that the parameter is a function of topological and biological features, which, along with network size, is variable across populations.
the genes identified in the biological processes in the top scored cliques were either transcription factors , hub nodes, or those genes discussed in table  <dig> that are known to play an important role in crc.
the top scoring clique identified in sa network was {mcm <dig>  mcm <dig>  mcm <dig>  mcm <dig>  mcm <dig>  mcm <dig>  orc2l}.
interaction of highly expressed orc <dig> and mcm <dig> is responsible for the initiation of dna replication  <cit> .
moreover, orc2l is not yet identified in crc but is associated with breast cancer  <cit> .
these results suggest that the top scored cliques indeed are associated with genes of significance in crc.
ep <dig>  esr <dig>
sp <dig>  smad <dig>
chn
epidermal growth factor
cliquestrength of each clique was represented in brackets with respect to the population in which it was identified
cell cycle
signal transduction
pathways in cancer
the high scoring cliques were further considered as a seed to identify the clique connectivity profiles  for gene-signature identification.
the overall connectivity using the top scoring clique can help to identify the crc gene signature profile for a specific population.
discovering clique connectivity profile
cliques cannot carry out biological processes in isolation, but rather, they interact with other cliques in order to perform a biological process.
these interactions can also help to identify the interacting pathways between cliques.
identifying a clique's connectivity profile  is important for better understanding the biological processes and pathways.
to decipher how these cliques interacted in the network, we analyzed them based on their connectivity profile.
in our algorithm, we considered the connectivity of cliques based on two parameters:  identification of common  genes, and  cliquestrength ).
the initial condition of the ccp algorithm was the identification of common genes across cliques.
for this analysis, the connectivity between two cliques was computed based on the following two conditions:  maximum number of common genes across cliques and highest cliquestrength , and  minimum number of common genes across cliques and highest cliquestrength , .
analysis based on maxcliques condition
the clique with maximum cliquestrength was selected as a seed, and the ccp was determined based on maximum common nodes and highest cliquestrength until no new cliques could be added.
for each iteration, the cliqueconnectivityscore was computed as described in the algorithm ).
figure  <dig> depicts the connectivity profile for one of the top common scoring cliques identified in usa, ger and chn.
these populations had cliques that were common and unique to all three connectivity profiles.
although the three ccps shown in figure  <dig> originated from the same seed, their connectivity profiles diverged at cliques # <dig> {ep <dig>  esr <dig>  smad <dig>  smad <dig>  cbp, ar, ctnnb1}, # <dig> {brca <dig>  ep <dig>  esr <dig>  smad <dig>  smad, tp <dig>  sp1}, and #gc <dig> {egfr, grb <dig>  pik3r <dig>  ptpn <dig>  shc, src, esr1}.
clique# <dig> was the first divergent point where the profiles differed for chn when compared to usa and ger.
expression of ar, which was included in clique# <dig>  has been associated with brca <dig> mutations in breast cancer  <cit> .
clique# <dig> includes brca <dig>  whose mutations are associated with early-onset of colorectal cancer  <cit> .
transcription factors  significant in crc  <cit>  were also identified both as hub nodes in our analysis and in these connectivity profiles.
using goterm finder and david-level  <dig>  the biological processes for these ccps were identified, and pathways associated with these ccps were obtained from the kegg database.
table  <dig> shows the enrichment with respect to go biological processes and pathway analysis for these clique connectivity profiles.
the biological processes enriched in all three clique connectivity profiles  included cellular process, and cell differentiation.
these were analyzed in earlier section of this paper and proved to be significant in crc.
the total numbers of pathways identified for genes present in the ccps for each population were:  <dig> ,  <dig> , and  <dig> .
all pathways identified in usa and ger were also present in chn.
in usa, the pathway with lowest e-value was the wnt signaling pathway; however, for ger, the erbb signaling pathway was the lowest.
wnt signaling was identified in chn along with the mapk and chemokine signaling pathways.
these pathways are all known to be associated with biological processes in crc  <cit> .
this analysis depicts the importance of cliques and their connectivity profiles with respect to the important biological processes, and pathways and it helps to demonstrate the divergence of them across the four populations.
analysis based on mincliques condition
using the same seed as given in figure  <dig>  we found the ccps for mincliques as shown in figure  <dig>  the usa ccps identifed a new clique that contains the egfr gene, whereas for ger and chn, the ccp identified the same divergent clique as shown in figure  <dig> {egfr, grb <dig>  pik3r <dig>  ptpn <dig>  shc, src, esr1}.
the ccp then diverged for ger and chn.
for chn, the new connected clique contained the genes {zap <dig>  vav <dig>  fyn, and crk} while the ger clique had {stat <dig>  ptpn <dig>  egfr, jak <dig>  stat5b, stat5a, and stat3}.
zap  <dig> and vav <dig> are known to be over-expressed in crc  <cit> , and stat <dig>  jak <dig>  and others are related to the jak-stat pathways associated with crc.
figure  <dig> depicts the advantages and disadvantages of using a threshold for overlapping nodes to identify ccp - the altering the number of overlapping nodes.
when all clique sizes were considered for the maxcliques algorithm, our study identified cliques with taf <dig>  taf <dig>  jun, and fos in all the populations.
taf <dig> is a regulator of apoptosis in cancer  <cit>  and has been identified to be up-regulated in nci- <dig> cell lines  <cit> .
kras, which was present in a size three clique, was identified in usa, sa, and china populations.
kras clique had its ccps connected with the clique of bcl <dig> .
kras pathways are known to be associated with crc  <cit> .
the clique {mcm <dig>  mcm <dig>  mcm <dig>  mcm <dig>  mcm <dig>  mcm <dig>  orc2l} identified in table  <dig> was associated with cliques of cdkn1a  in all the populations and down-regulation of cdkn1a plays a role in crc  <cit> .
the clique {dis <dig>  exosc <dig>  exosc <dig>  exosc <dig>  exosc <dig>  exosc <dig>  exosc <dig>  mpp6} identified in table  <dig> does not have any ccp with any other size cliques in any population networks.
genes present in cliques can form signatures for crc specific to population.
the ccp algorithm, if preceded with either maxcliques or mincliques, will still identify the important genes associated with crc.
analysis of cliques of all sizes can identify the divergence in ccps across population.
as the definition of cliques is more stringent than that of modules, networks have fewer cliques than modules, allowing for more manageable analysis.
our analysis showed that ccps can identify the commonality and divergence across populations.
the ability of both cliques and ccps to identify commonalities and divergences allows for them to be considered as gene signatures for crc and can be evaluated further in the laboratory.
CONCLUSIONS
in this paper we developed a methodology for identification of commonalities and variations in crc across populations by evaluating cliques and their connectivity profiles.
in this study, we considered four distinct populations across the world.
we used both topological and biological features - specifically co-expression, go distances for biological process, and pathway similarity scores - in our network analysis.
we additionally introduced the concept of using cliques to capture gene signatures for crc across populations.
the methodology developed for joining cliques is powerful for finding the commonalities and divergences among populations with respect to their gene signatures.
using the ccp, we were able to capture important network components, including biological processes, pathways, and genes, and use these to elucidate the gene signature of crc.
the advantage of using cliques as opposed to functional modules is that although there are fewer cliques in a network, they are still able to capture the key gene signatures of a disease.
though the current study only applied the use of clique analysis to small datasets, we plan to validate the procedure in larger datasets.
we additionally plan to make our ccp algorithm more stringent with respect to overlapping nodes.
as our methodology is scalable with respect to annotation, different features such as static and dynamic profiles, literature score, and phenotypes can give in-depth stratification of crc across populations.
comparison of all cliques  as gene signatures across populations may ultimately aid the advancement of personalized medicine and the identification of efficient drug targets.

 colon rectal cancer is the third most commonly diagnosed cancer worldwide. it is the second leading cause of cancer death in the united states, and worldwide, nearly deaths are reported every year due to crc. the crc incidence rate varies across the globe. for example, low incidence rates for crc have been associated with asian and african populations. dietary and environmental factors have also been known to contribute to crc patterns. therefore, we postulate that there are some common as well as some unique key gene signatures that can discriminate crc across populations. due to the advent of high through-put technologies, a multitude of public domain expression datasets are now available for crc research. these datasets are generated worldwide and deposited with the objective of identifying key molecules that play an important role in different stages of crc. gene-expression profiling and meta-analysis have been extensively used to: a) understand the mechanisms that drive a normal cell to become a cancer cell, b) understand different metastatic levels, and c) identify biomarkers. differentially expressed genes have been identified as biomarkers in leukemia, b-cell lymphoma, breast and lung cancers. gene signatures are a set of genes that might play an important role in a given disease. using gene expression datasets, gene signatures were identified in different cancers. first attempts to identify gene signatures from gene expression were done in breast cancer. genes combine together and act as pathways to perform a biological function and genes in a given pathway are co-expressed. large-scale efforts are being made to identify the biomarkers associated with specific pathways and biological function using gene expression profiles. a single pathway can be deregulated by different mechanisms or combination of genes. also, a set of genes can target one or many pathways. gene signatures can help to identify these patterns in pathways and also the relationships among them. first attempts for identifying gene signatures were done for breast cancer and have since been used in various other cancers as well. network based approaches have been used to identify subnetwork markers that are more reproducible than individual markers. functional modules extracted from networks are groups of genes with same functions. the genes in the subnetworks are co-expressed and they share more interactions among them, than with other genes in the larger network. these functional modules can be used to identify both similar and unique biological characteristics among different species datasets and are also considered to be subnetworks. in protein-protein interaction networks, these functional modules are present as sub-graphs or tightly connected sub-graphs and can be analyzed with respect to their individual characteristics using either gene ontology similarities or pathway significance. identification of regulatory modules or gene subnetworks is important as they play critical roles in biological processes and their associated pathways can provide potential targets for drug intervention in cancers. though gene signatures can improve understanding of a disease, identification of these signatures across populations is difficult, as gene expression is known to vary between populations. even though modules have been effectively used for the identification of gene signatures, this approach is computationally complex because the modules are open subnetworks, meaning that within a disease network, a very large number of modules will be identified. therefore, use of modules for comparing gene signatures across populations is computationally an intractable problem. though attempts have recently been made to understand the difference in crc between african-americans and european-americans using a systems biology approach. however, not much work has been done in the area of gene signature identification across populations with respect to crc. due to the complexity of gene signature identification, we propose the use of cliques as an alternative to modules for the comparison of gene signatures across populations. cliques are closed, fully-connected subnetworks. the genes that are identified as part of these cliques are functionally related and highly co-expressed. since cliques are closed networks, they are both computationally tractable and more conserved in the biological networks. a clique consists of molecules that can be associated with one or many pathways and these molecules are related with their gene ontologies. a recent study reported the use of cliques in elucidating the mechanisms involved in breast cancer. in this paper we have attempted to understand crc gene signatures across four different populations: usa, germany, china, and saudi arabia. the studies on each of these populations were conducted separately, and the data was downloaded from public repositories geo http://www.ncbi.nlm.nih.gov/geo. for the study model, we hypothesized that tumors target biological modules that execute specific biological processes. since cliques are fully connected conserved subnetworks within biological networks, our hypothesis is that they are conserved across populations and can be understood as gene signatures. therefore we propose to understand these cliques in crc across populations. in this work we integrated the expression data along with network topological features and biological features. cliques were then scored based on these features. our work identified the common and unique cliques across populations that were important with respect to crc. to identify the important cliques we analyzed the networks based on the following perspectives: identification of genes from individual datasets based on p-value; construction of gene networks for each population; annotation of nodes and edges of networks with topological and biological features; identification of cliques across networks; comparison of the cliques in all the networks based on their strength and connectivity profiles; and, evaluation of the cliques as gene signatures based on their biological significance in crc.  jin order to decipher the gene signatures and identify the similarity-uniqueness among the four different populations of crc, we developed a methodology as described in figure our methodology involved identifying genes in each dataset that satisfied the two sample t-test, construction of the gene networks using human protein reference database, obtaining the gene expression profiles, identifying cliques in each dataset and comparing them across the populations, and connecting the cliques in each network to identify a clique connectivity profile and comparing them across populations. data analysis the gene expression in all the four datasets was first normalized using the r-package rma algorithm. the two-sample t-test was used to identify the differentially expressed genes in each dataset. the genes satisfying the t-test in each dataset were then used to construct the networks. figure shows the profile of gene expression across the population dataset. network construction to construct the gene network for each population, we used only those genes that coded for proteins present in the hprd database. the networks were compared with respect to their node similarity. table shows the node similarity across the four populations. as shown in table a large number of genes were common among usa, chn and ger, but there were fewer genes common with sa. country analysis of population specific networks to analyze these population-specific networks with respect to their topological and biological features, these networks were first compared with the hprd network for their interactions, degree, diameter, and average path length. table shows the results of this comparison. the average path-length is the overall ease with which the genes in the network communicate with each other. though the degree and number of interactions differ for ger, usa, and sa with hprd, the diameter and the average path lengths of these networks is in accordance with hprd. therefore these networks have the ability to generate functional complexes or modules and can be analyzed with respect to their biological processes. hprd had the highest number of interactions. this illustrates that all four population networks are sub-networks within hprd for further analysis of the networks, pearson correlation coefficients were computed for each edge, and correlations greater than and less than - were considered. this reduced the size and the complexity in the networks. analysis of node strength based on topological properties the node strength for each node in the population specific network was computed using the topological parameters - namely, eccentricity, closeness and betweenness,,, and ). the common high scoring nodes identified in all the population were: tp src, esr smad grb ep crebbp, egfr, smad and csn2ka of these, the transcription factors tp esr smad and smad were identified as important in crc. grb overexpression has also been identified in crc it was reported that binding of grb with gab plays an important role in crc. gab additionally interacts with bcr-abl, activating bcr-abl-associated crc significant pathways - specifically, pi3k-mtor, ras/raf/mek/erk, and jak-stat. ep has also been identified to be involved in crc. there were also a few unique top scoring nodes: atxn for ger and prkca, prkaca and ubqln for sa. not many references were available for ubqln and atxn in crc, although atxn has been associated with cancer pathways. the topological analysis revealed top scoring genes that are common as well as unique with their significance in crc across the four populations. analysis of biological features for population specific networks the biological feature analysis provided the edge strength for all the genes in all four population networks. the three biological features considered for the analysis were: pcc, gene ontology distance, and pathway similarity score. the pcc had already been computed during the network construction process as described earlier ). the number of edges satisfying the pcc in each population network was:,,, and. as it is known that, biological processes are essentially a series of events accomplished by one or more molecular functions. each node in the network was associated with its biological processes. gene ontology distance was computed across an edge between two nodes in the network ). the number of unique gene ontology biological processes terms identified in each of the population networks were:,,, and. the following gene ontology biological processes were associated with maximum number of genes in all networks: go:, go:, go:, and go:. of these processes, signal transduction pathways are currently used as therapeutic targets in crc, and immune response has been associated with crc progression. since these biological processes are known to be important in crc, we concluded that go biological process should be a key feature for computing the edgestrength ). another key biological feature that we use was the pathway similarity score between two nodes. the pathways were identified using the kegg database, and the number of unique pathways identified for the respective populations were:,,, and. there were a total of unique pathways across all the four populations: forty-nine pathways were common to all four countries, thirty-five were common to three countries, seven were common to two countries, and five were unique to one country. the crc pathways identified across all the countries were: chemokine signaling pathways, wnt-signaling pathway, mapk signaling pathway, jak-stat pathway, calcium-signaling pathway, erbb signaling pathway, and pathways in cancer. the association of major crc pathways with the nodes thus justified the use of the pathway similarity score as an essential feature for computing edge strength. each node in the network was annotated with its pathway, and the pathway similarity score was computed across an edge of two nodes in the network ). through these various analyses, we obtained the topological and biological features for all four populations to compute the nodestrength and edgestrengths for their respective genes in the networks. identification of cliques genes with similar expression patterns across various networks perform similar functions. both functional modules and interacting modules have similar co-expressed genes. based on this understanding, we designed an algorithm that identified the cliques in each population network. figure shows the distribution of the number of identified cliques of different sizes in each of the population-specific network. figure shows the total number of unique cliques identified for all four populations. the largest number of cliques was identified for usa and minimum for sa respectively. in this analysis, we considered only cliques of node size seven, as this size was found to be consistent across all four population networks, while cliques of higher sizes were not found across all the populations. for the specified clique size of seven, a total of cliques were identified across the four populations. these cliques were then further analyzed with respect to their distribution in the different populations. there were cliques common to all populations, while and unique clique were identified in usa, ger, and chn, respectively. figure shows the venn diagram for the distribution of size seven cliques across the four populations. the total number of genes identified in these cliques within each population network was:,,, and. we identified genes in total, with of those genes common among all cliques across the populations. analysis of cliques common across the populations to understand the significance of the cliques across the populations, we first analyzed all the cliques with respect to their go biological processes and pathway associations. the numbers of go biological processes associated with cliques for each population network were:,,, and,. go terms with hyper-geometric p-values < for each population were identified ); go: go: and go: were identified as the processes with the smallest hyper-geometric p-values across all cliques in all population networks. go: was associated with signaling pathways, which have been identified as the targets for crc. go: was associated with cell division cycle. previous studies found genes involved in cell cycle, apoptosis, and invasion to play an important role in crc. go: was associated with protein phosphorylation. tgf-beta is a key pathway involved in crc, and progression of this pathway is known to be dependent on protein phosphorylation. the validation of these pathways with respect to involvement in crc supports that clique nodes are involved in important go biological processes, and these enriched go terms are key features by which crc can be evaluated across populations. the identified cliques were further analyzed using go term finder, and david level. cliques identified as significant were further evaluated based on literature. table shows the details of a few common cliques identified in all the population networks and their gene significance in crc. it was also observed that some of the genes in these common cliques have been widely studied in terms of crc, while others have relatively sparse available literature. go term finder and david level were considered for analysis. the clique {egfr, esr grb pik3r ptpn shc src} in table was enriched in the following go biological processes: egfr signaling pathway, cellular response to growth, and signal transduction; most of the genes in this clique have been identified as significant in crc. for example, targeted therapy using egfr is currently available for crc. egfr is a trans-membrane tyrosine kinase receptor belonging to her family of cell surface receptor; it is triggered by ligands and leads to the activation of many intracellular signal transduction pathways which are known to effect the activation of many transcription factors involved in cellular response. esr is used as a epigenetic marker, while activation of grb2/sos, leads to a cytoplasmic phosphorylation cascade involving kras. kras pathways are the targeted pathways in crc, ptpn mutation has not been identified specifically in crc, but it has been found in other cancers, including lymphoma and leukemia. similarly, although shc has not been identified directly in crc, it has been identified in lung cancer. the clique {brca crebbp, ep esr smad smad tp53} was enriched with the following go biological processes: regulation of transcription, response to lipid, and positive regulation of cellular metabolic process. again, these genes have all been studied in terms of crc or other cancers. crebbp and ep have been identified as prognostic markers for crc, and ep is additionally been identified in lipid metabolism in crc. some studies have identified brca, up-regulation of crebbp, mutation of ep loss of smad signaling in crc. the tgf-beta signaling pathway is known to play an important role as tumor suppressor and tumor promoters in crc by activating the smad2/smad complex, which enters the nucleus to further regulate transcription. overexpression and mutation of tp has also been associated with crc. the clique {csn csn csn csn csn csn7a, csn and tp53} is a part of signalosome of csn which acts as protein kinase. the enriched go biological processes were protein deneddylation and cellular protein metabolism. csn-mediated protein deneddylation has been identified in the literature to promote hedgehog-pathways, though it has not been reported specifically in crc. csn has been identified as essential for cell proliferation in hepatocellular carcinoma, while csn is known to be a regulator of tp and mdm2; additionally, csn is known to be important for regulating dna-damage-associated apoptosis and tumor genesis, as well as enhancing p53-mediated tumor suppression http://www.genedistiller.org. mdm has been identified as a probable therapeutic target for crc. in the clique {dis exosc exosc exosc exosc exosc exosc and mpp6}, the enriched go biological processes identified were: dna modification, dna deamination, and mrna catabolic process. a form of dna modification that is used to identify many cancers is dna methylation in the promoter regions, which causes silencing of many genes. dis which has been identified in cancer genomes, stabilizes rna and its translation into proteins, while exosc is involved in ribosome biogenesis and is highly up-regulated in crc. though not much has been reported about the other genes in this clique with respect to crc, they have been identified in other cancers. one of the unique cliques identified for usa was {lsm lsm lsm lsm lsm lsm smn1}. lsm is mapped on chromosome 8p, which has been identified in both prostate cancer and crc. although smn has not been identified in cancer directly, it has been proposed to interact with bcl- which is associated with crc, and has a high prognostic value. from this analysis, it can be stated that common and unique cliques identified across population networks are involved in important biological processes in crc. these cliques include genes that are both well-studied and less-studied in crc, as well as those known to play a role in other cancers, indicating their importance in crc networks and in better understanding crc across populations. this analysis also demonstrates the importance of cliques in the crc disease and can be used to understand the four population-specific networks. analysis of pathways associated with genes in cliques for all populations cliques identified in the population-specific networks were further analyzed using the kegg database for their pathway similarity score. figure shows the profile of pathways associated with the maximum number of genes in each network. this association varies across populations. for example, pathways in cancer is associated with the highest number of genes in all the populations -,,, and;many of the different pathways that belong to the domain of pathways in cancer in the kegg database were discussed in the previous section. though jak-stat pathways were identified to be associated with clique-genes in all the population, the level of association, as defined by number of clique-genes identified, was higher in ger than in sa. similar observations of varying levels of association were made for many pathways, as can be seen in figure based on our analysis of the common cliques, unique cliques, and pathways associated with the cliques for all four populations, gene signatures for crc can then be developed from the genes identified in these cliques. specific gene signatures for each individual population could also be developed using the unique cliques that were identified in each population. analysis of cliquestrength the parameter cliquestrength was computed for all cliques in the population-specific networks based on both topological and biological features ). cliques associated with high cliquestrength were considered important in these networks. to assess the usefulness of the cliquestrength parameter, we analyzed two top-scored cliques that were common or unique across the populations. table shows the top scored cliques in each network, their associated biological processes, and the genes associated with each process. table additionally shows that the cliquestrength of a common clique varies across population, as can be seen by comparing scores for the first clique in usa and chn; this is due to the fact that the parameter is a function of topological and biological features, which, along with network size, is variable across populations. the genes identified in the biological processes in the top scored cliques were either transcription factors, hub nodes, or those genes discussed in table that are known to play an important role in crc. the top scoring clique identified in sa network was {mcm mcm mcm mcm mcm mcm orc2l}. interaction of highly expressed orc and mcm is responsible for the initiation of dna replication. moreover, orc2l is not yet identified in crc but is associated with breast cancer. these results suggest that the top scored cliques indeed are associated with genes of significance in crc. ep esr sp smad chn epidermal growth factor cliquestrength of each clique was represented in brackets with respect to the population in which it was identified cell cycle signal transduction pathways in cancer the high scoring cliques were further considered as a seed to identify the clique connectivity profiles for gene-signature identification. the overall connectivity using the top scoring clique can help to identify the crc gene signature profile for a specific population. discovering clique connectivity profile cliques cannot carry out biological processes in isolation, but rather, they interact with other cliques in order to perform a biological process. these interactions can also help to identify the interacting pathways between cliques. identifying a clique's connectivity profile is important for better understanding the biological processes and pathways. to decipher how these cliques interacted in the network, we analyzed them based on their connectivity profile. in our algorithm, we considered the connectivity of cliques based on two parameters: identification of common genes, and cliquestrength ). the initial condition of the ccp algorithm was the identification of common genes across cliques. for this analysis, the connectivity between two cliques was computed based on the following two conditions: maximum number of common genes across cliques and highest cliquestrength, and minimum number of common genes across cliques and highest cliquestrength,. analysis based on maxcliques condition the clique with maximum cliquestrength was selected as a seed, and the ccp was determined based on maximum common nodes and highest cliquestrength until no new cliques could be added. for each iteration, the cliqueconnectivityscore was computed as described in the algorithm ). figure depicts the connectivity profile for one of the top common scoring cliques identified in usa, ger and chn. these populations had cliques that were common and unique to all three connectivity profiles. although the three ccps shown in figure originated from the same seed, their connectivity profiles diverged at cliques # {ep esr smad smad cbp, ar, ctnnb1}, # {brca ep esr smad smad, tp sp1}, and #gc {egfr, grb pik3r ptpn shc, src, esr1}. clique# was the first divergent point where the profiles differed for chn when compared to usa and ger. expression of ar, which was included in clique# has been associated with brca mutations in breast cancer. clique# includes brca whose mutations are associated with early-onset of colorectal cancer. transcription factors significant in crc were also identified both as hub nodes in our analysis and in these connectivity profiles. using goterm finder and david-level the biological processes for these ccps were identified, and pathways associated with these ccps were obtained from the kegg database. table shows the enrichment with respect to go biological processes and pathway analysis for these clique connectivity profiles. the biological processes enriched in all three clique connectivity profiles included cellular process, and cell differentiation. these were analyzed in earlier section of this paper and proved to be significant in crc. the total numbers of pathways identified for genes present in the ccps for each population were:,, and. all pathways identified in usa and ger were also present in chn. in usa, the pathway with lowest e-value was the wnt signaling pathway; however, for ger, the erbb signaling pathway was the lowest. wnt signaling was identified in chn along with the mapk and chemokine signaling pathways. these pathways are all known to be associated with biological processes in crc. this analysis depicts the importance of cliques and their connectivity profiles with respect to the important biological processes, and pathways and it helps to demonstrate the divergence of them across the four populations. analysis based on mincliques condition using the same seed as given in figure we found the ccps for mincliques as shown in figure the usa ccps identifed a new clique that contains the egfr gene, whereas for ger and chn, the ccp identified the same divergent clique as shown in figure {egfr, grb pik3r ptpn shc, src, esr1}. the ccp then diverged for ger and chn. for chn, the new connected clique contained the genes {zap vav fyn, and crk} while the ger clique had {stat ptpn egfr, jak stat5b, stat5a, and stat3}. zap and vav are known to be over-expressed in crc, and stat jak and others are related to the jak-stat pathways associated with crc. figure depicts the advantages and disadvantages of using a threshold for overlapping nodes to identify ccp - the altering the number of overlapping nodes. when all clique sizes were considered for the maxcliques algorithm, our study identified cliques with taf taf jun, and fos in all the populations. taf is a regulator of apoptosis in cancer and has been identified to be up-regulated in nci- cell lines. kras, which was present in a size three clique, was identified in usa, sa, and china populations. kras clique had its ccps connected with the clique of bcl. kras pathways are known to be associated with crc. the clique {mcm mcm mcm mcm mcm mcm orc2l} identified in table was associated with cliques of cdkn1a in all the populations and down-regulation of cdkn1a plays a role in crc. the clique {dis exosc exosc exosc exosc exosc exosc mpp6} identified in table does not have any ccp with any other size cliques in any population networks. genes present in cliques can form signatures for crc specific to population. the ccp algorithm, if preceded with either maxcliques or mincliques, will still identify the important genes associated with crc. analysis of cliques of all sizes can identify the divergence in ccps across population. as the definition of cliques is more stringent than that of modules, networks have fewer cliques than modules, allowing for more manageable analysis. our analysis showed that ccps can identify the commonality and divergence across populations. the ability of both cliques and ccps to identify commonalities and divergences allows for them to be considered as gene signatures for crc and can be evaluated further in the laboratory.  in this paper we developed a methodology for identification of commonalities and variations in crc across populations by evaluating cliques and their connectivity profiles. in this study, we considered four distinct populations across the world. we used both topological and biological features - specifically co-expression, go distances for biological process, and pathway similarity scores - in our network analysis. we additionally introduced the concept of using cliques to capture gene signatures for crc across populations. the methodology developed for joining cliques is powerful for finding the commonalities and divergences among populations with respect to their gene signatures. using the ccp, we were able to capture important network components, including biological processes, pathways, and genes, and use these to elucidate the gene signature of crc. the advantage of using cliques as opposed to functional modules is that although there are fewer cliques in a network, they are still able to capture the key gene signatures of a disease. though the current study only applied the use of clique analysis to small datasets, we plan to validate the procedure in larger datasets. we additionally plan to make our ccp algorithm more stringent with respect to overlapping nodes. as our methodology is scalable with respect to annotation, different features such as static and dynamic profiles, literature score, and phenotypes can give in-depth stratification of crc across populations. comparison of all cliques as gene signatures across populations may ultimately aid the advancement of personalized medicine and the identification of efficient drug targets. 
our study was based on four publicly available expression datasets belonging to four different populations across the world. with these clique connectivity profiles, we were able to identify the divergence among the populations, important biological processes, and related gene pathways. we identified cliques of various sizes across the four population networks. these cliques were further analyzed based on their connectivity profiles. we found associations between the cliques and their connectivity profiles across networks. therefore the genes identified in these cliques and their connectivity profiles can be defined as the gene-signatures across populations. we developed a new approach where cliques and their connectivity profiles helped elucidate the variation and similarity in crc gene profiles across four populations with unique dietary habits. forty-nine common cliques of size seven were identified. gene signatures for any disease can identify the key biological processes, which is especially useful in studying cancer development. we developed a topological and biological feature-based network approach for identifying the gene signatures across populations.
 colorectal cancer is one of the most commonly diagnosed cancers worldwide. studies have correlated risk of crc development with dietary habits and environmental conditions. gene signatures for any disease can identify the key biological processes, which is especially useful in studying cancer development. such processes can be used to evaluate potential drug targets. though recognition of crc gene-signatures across populations is crucial to better understanding potential novel treatment options for crc, it remains a challenging task.  we developed a topological and biological feature-based network approach for identifying the gene signatures across populations. in this work, we propose a novel approach of using cliques to understand the variability within population. cliques are more conserved and co-expressed, therefore allowing identification and comparison of cliques across a population which can help researchers study gene variations. our study was based on four publicly available expression datasets belonging to four different populations across the world. we identified cliques of various sizes across the four population networks. cliques of size seven were further analyzed across populations for their commonality and uniqueness. forty-nine common cliques of size seven were identified. these cliques were further analyzed based on their connectivity profiles. we found associations between the cliques and their connectivity profiles across networks. with these clique connectivity profiles, we were able to identify the divergence among the populations, important biological processes, and related gene pathways. therefore the genes identified in these cliques and their connectivity profiles can be defined as the gene-signatures across populations. in this work we demonstrate the power and effectiveness of cliques to study crc across populations.  we developed a new approach where cliques and their connectivity profiles helped elucidate the variation and similarity in crc gene profiles across four populations with unique dietary habits. 22- april the international conference on intelligent biology and medicine nashville, tn, usa 