BACKGROUND
endogenous signal transduction in cancer cells is systematically disturbed to redirect the cellular decisions from differentiation and apoptosis to proliferation and, later, invasion  <cit> .
cancer cells acquire their malignancy through accumulation of advantageous gene mutations by which the necessary steps to malignancy are obtained  <cit> .
these selfish adaptations to independence can be described as a result from an evolutionary process of diversity and selection  <cit> .
we were interested to observe the resulting cellular signal transduction on a global view.
experimental high throughput methods such as gene expression profiling with microarrays enable investigating the pathogenic function of tumors on a mesoscopic level.
large-scale gene expression profiles were successfully used to predict clinical outcome  <cit>  and improved risk estimation  <cit> .
however these studies didn't relate genes and their expression to a functional context.
to gain an understanding on a systems view, gene expression can be mapped onto cellular networks.
several studies have been reported that used gene expression data from microarrays to describe specific characteristics of signaling networks in cancer.
discriminative components of a protein-protein interaction network were identified by comparing gene expression patterns of metastatic and non-metastatic tumors in breast cancer and suited as risk markers for metastasis of breast cancer  <cit> .
new genetic mediators for prostate cancer were found with networks that were reversely engineered from gene expression profiles  <cit> .
besides this, insights into evolutionary principles were gained by the analysis of gene expression profiles.
gene expression differences were used to define phylogenetic relationships of several drosophila species  <cit>  and a molecular clock for primates  <cit> .
furthermore, the regulation of signaling in yeast was investigated on a global scale to observe regulatory adaptation to the cellular environment.
yeast responded to exogenous signals by shorter regulatory cascades to enable fast signal propagation  <cit> .
the aim of our work was to detect characteristic signaling properties of cancer cells on a global scale.
we compared the regulation of signaling pathways in cancer with normal cells and mapped gene expression data of tumors and their corresponding non-malignant  samples onto a comprehensive protein-protein-interaction network.
for inferring regulation-principles in cellular signal transduction, we used a graph searching algorithm that tracked pathways with the highest correlation in regulation.
we investigated twenty tumor-datasets comprising acute myeloid leukemia, esophageal squamos cell-, lung adeno- and renal clear cell carcinoma, breast-, cervical-, head-and-neck-, oral-tongue-, pancreas- and prostate cancer, and vulva interstitial neoplasia.
the investigated tumors showed shorter pathways, but a larger extension of the network.
the tumors displayed lower frequency of central proteins and links and a higher information entropy  in their network.
these findings were embedded into a novel signal-regulation motif which was observed considerably more often in normal cells when compared to tumor cells .
similar to the study of cui and co-workers  <cit> , central proteins  were enriched with cancer mutations.
we observed that these proteins showed higher regulation-integrity in the normal samples whereas the tumor samples showed motifs of regulatory maintenance of the neighbors of hubs.
RESULTS
constructing the signaling networks
we assembled our signaling network employing a comprehensive data repository of known protein-protein interactions from the literature .
proteins were represented by their coding genes and will also be denoted as nodes of the networks in the following.
gene expression data of each cancer dataset  and the corresponding set of normal samples  was mapped onto the nodes of the network.
depending on the coverage of the probes on the microarray chips, the intersection with the hprd network comprised of  <dig> to  <dig> nodes including  <dig> to  <dig> receptors and  <dig> to  <dig> transcription factors .
similar to luscombe and co-workers, we assumed most likely signaling propagation by high co-regulation of genes of two neighboring proteins in the network  <cit> .
we calculated protein-protein-distances for each link  by the co-regulation  of the two interacting proteins .
the link-distances were higher  in cancer cells compared to normal cells .
we defined pathways for each pair of receptors  and transcription-factors  by their shortest paths yielding a range of  <dig>  to  <dig>  pathways for each of the investigated cancer datasets.
the tumor cells showed a distinct higher coverage of the original protein-interaction network for these pathways.
table  <dig> gives an overview of the network data for the different datasets we analyzed and also the network-coverage of all receptor-transcription-factor pathways for the tumors and the reference samples.
from these pathways we constructed specific networks for each tumor and reference sample.
for each tumor and normal sample, the constructed networks consisted only of those links and nodes that appeared at least once in their receptor-transcription-factor pathways.
not-appearing links and nodes were discarded .
we were interested if these networks were specific for the respective tumor type.
for this, we extracted all somatically mutated genes for specific cancer tissues from a database  and tested if our tumor networks contained genes which have been described specifically for the respective tumors.
we performed enrichment tests  and found that all tumor networks showed a considerably significant enrichment of their corresponding mutated tumor genes .
tumors use shorter paths, more links and less hubs
we calculated a variety of different network-features to characterize specific differences in signaling-regulation of tumor cells and non-malignant cells.
the results are given in table  <dig> and table  <dig> and will be explained in the following.
for getting a reasonable estimate of the general tendency of tumors, we calculated the average out of all datasets for cancer and normal networks and performed a significance test of the pair-wise differences between tumor and normal .
the average path-length of cancer networks was less than for non-malignant .
we wanted to know how often the same links  were used for different signaling pathways.
for this, we defined the frequency of a link  as the number of receptor-transcription-factor pathways it was involved in.
the average link-frequency was obtained by the number of links used in each single pathway from each respective receptor to each transcription factor, divided by the number of all used links.
the average link frequency was higher in normal cells .
similarly, the node frequency was calculated and showed the same tendency .
hence networks of normal cells used more often the same central proteins and interactions for different signaling tasks.
such a hub-like structure is the central characteristic of scale free networks  <cit> .
we were interested if the networks for cancer and normal samples followed these characteristics and if there were distribution differences between them.
in deed, the link-frequency distribution of the networks of both entities followed a power law .
in comparison to the networks from normal cells, the distributions of tumors showed a steeper decline.
we calculated the exponent α of the distribution and observed larger exponents for cancer networks .
.
this agrees with the lower average of their link-frequency.
these distributions also show that proteins of high connectivity  in the networks of normal cells are more abundant .
the clustering coefficient has been employed as a measure of connectedness of networks  <cit> .
we calculated the clustering coefficient and obtained lower values for the network of cancer cells supporting our findings that cancer showed a tendency for less centralized, less hub-dependent formation .
specifically, the number of nodes with a clustering coefficient greater zero was distinctively higher in cancer cells .
frequently involved genes are enriched with cancer mutated genes
cui and co-workers compiled a selective list of  <dig> cancer mutated genes which were derived from large scale sequencing and the literature .
we compared this list with the  <dig> most frequently involved nodes  of each network and found significant enrichment for  <dig> out of  <dig> normal and tumor datasets .
we then defined gene-lists of cancer mutated hubs for every cancer by intersecting the hubs of our network with the list of cancer mutated genes of cui et al.
.
interestingly, most of the genes which showed up in the tumor networks were also present in the normal networks.
this may indicate that normal cells intrinsically pave the way for their specific evolvement into malignancy.
signaling-regulation in cancer is detached at cancer mutated hubs but maintained in their vicinity
uri alon and his co-workers studied occurrences of direction-motifs in triangles and revealed a large variety of substantial characteristics in signaling networks characterized by consistent and non-consistent feed-forward and feedback loops  <cit> .
we were interested in local regulation patterns of the networks at cancer mutated hubs.
for this, we analyzed regulation motifs of every triangle consisting of exactly one hub and two of its neighbors which on their part also interact.
we defined two regulation motifs.
the first motif reflected the degree of regulatory integration of a hub and its network-vicinity and was defined by a high correlation of all pairs of nodes in the triangle motif .
we found this motif significantly more often in normal cells .
the second motif  described triangles which pairs of hub-nodes  showed high correlation in one tissue type and no correlation in the other, while the mutual correlation of nodes n1-n <dig> stayed in the same category .
such a scenario is reasonable for a mutated cancer protein with loss-of-function leaving their neighbors unaffected.
indeed, this motif occurred more often in the cancer networks .
tumor networks are more robust against directed attacks
albert and co-workers showed that scale free networks are error tolerant only against attacks of randomly selected nodes but not against directed removals of central nodes   <cit> .
we were interested in the robustness of the networks when removing their hubs.
for this, we removed the most frequently involved nodes of every network and calculated the average of pair-wise distances  as an estimate of the fragility of the networks  <cit> .
the relative increase of the network diameter due to the removal was distinctively larger in normal cells compared to cancer cells  indicating higher robustness of the tumor networks against directed attacks at their hubs.
lower information content in normal cells
we used the number of pathways each single link was involved in  as an estimate of the probability that information  was passed through this link.
in this simplified model, every pathway was treated equally.
with this, we calculated the information content for each network.
as a measure of disorder, shannon's information entropy  <cit>  was calculated for each network.
the cancer networks exhibited a higher information entropy  indicating their higher degree of dispersal.
a comparative network motif
inspired by the results described above, we designed a comparative network-motif which is illustrated in figure  <dig>  we wanted to put up a model in which cancer cells use different pathways for different tasks whereas normal cells use common signaling interactions for different tasks.
therefore a model was designed such that two pathways  of the normal tissue shared at least one common link, whereas the same operator-receiver pairs for the tumor did not share any link.
we compared the abundance of this motif with the abundance of its counterpart in which the cancer cells used at least one common link and the normal cells did not share any link.
we found a significantly higher number of our motif in which the normal cells share a common link .
discussion
we investigated network properties of cancer signaling by looking at co-regulation patterns of genes for different cancer types.
we analyzed the general regulatory behavior of correlating gene expression samples of one tumor type and study, rather than analyzing the regulatory behavior of single patients.
for this, we calculated a gene to gene distance metric for all samples  of normal and cancerous tissues.
the networks of the investigated tumors showed distinctive mechanisms in the regulation of signal transduction when compared to normal cells and had shorter path lengths.
luscombe and co-workers analyzed the dynamics of regulatory networks in yeast  <cit> .
in comparison to endogenously caused changes, they discovered a different topological adaptation of the network when yeast responded to environmental changes.
for having quick responses, yeast reacted to environmental changes  by short regulatory cascades.
our investigated cancer cells showed a similar tendency as yeast under stress at which fine tuned endogenous homeostasis is of minor importance.
interestingly, for yeast, luscombe et al.
discovered a higher frequency of hubs for stress responses whereas we discovered that the tumors used hubs less frequently.
cells of normal sample had a more centralized network to regulate signals via common nodes and links.
this was reflected by a smaller network, higher frequency of hubs, lower entropy and a higher number of our signaling motif in which the number of pathway-pairs with common links was counted.
this makes sense, as fine-tuning and integrating diverse signals need to be coordinately transferred to the respective transcriptional response which is substantial for fine grained signaling homeostasis of normal cells to co-ordinate their signals in accordance to their cellular community in the tissue.
degenerated tumor cells do not need this any more.
in turn, the tumors showed a higher connectedness of the whole network which may strengthen their independency of exogenic perturbations.
similar to cui and co-workers  <cit> , we observed with our model that cancer specific mutations occur distinctively more often at hubs for signal transduction.
such a mutation can cause a loss of function.
this is beneficial for the cancer if the protein gets insensitive to upstream-signals and fires constitutively an oncogenic signal as e.g.
the abl-bcr fusion protein in chronic myelogenous leukemia  <cit> .
if the protein acts as a tumor suppressor, a complete loss of function is beneficial for oncogenesis.
in both scenarios, the regulation for signaling homeostasis of the local network environment is detached from this mal-functional protein and a coordinated regulation between the environment and this protein is not necessary any more.
we observed this by counting distinctively less integration-motifs in tumors .
interestingly, tumors seem to sustain the original signals between the environment.
we observed this by higher counts of the disruption motif in tumors which reflects the disruption of co-regulation of the hub, but maintained regulation between the neighbors of the hubs .
even though tumors may exhibit de-regulation of mal-functional hubs with their neighbors, such a maintained co-regulation of their neighbors gives evidence that bypass regulations are still necessary.
ma'ayan and co-workers observed an accumulation of feedback and feed-forward loops at such hubs  <cit>  which supports this idea.
tumors need to maintain the direct signal of e.g.
a feed-forward loop which is necessary for the effect of the constitutive signal of an oncogenic hub .
such oncogenic signaling motifs may have implications to drug therapy.
if an oncogenic hub is treated  resistance can occur by mutations of the target protein which reduce the affinity of the drug to the target.
a combined therapy may avoid this evolvement by additionally blocking the signaling-maintenance of the neighbors.
in addition, we found that the observed cancer networks showed higher error tolerance against directed attacks of hub removals.
hence, some maintenance signals may not only support cancer mutated hubs but also pave the way for the signaling network to get independent of them, specifically for proteins of cancer mutated genes with loss-of-function.
it is challenging but highly relevant to shed light into these effects experimentally with cell lines exhibiting drug resistances at such hubs.
we analyzed networks based on cohorts of patients and used the correlation of expression between gene pairs for the whole cohorts.
this approach does not allow the analysis of a single sample and therefore can't be employed for diagnosis of a single patient, but rather for the analysis of tumor subgroups.
it may be worthwhile developing distance metrics of gene pairs for single samples with which the investigated topology features can be employed supporting diagnosis.
we proposed a novel comparative signaling-motif for malignant signaling-regulation which sums up our findings .
there have been elaborated studies on network motifs  <cit> .
our comparative cancer motif is different from these motifs in that it shows signaling-regulation in cancer reflecting less centralized formation.
the comparative cancer motif agrees with our findings of non-integration  but signaling-maintenance  of proteins with higher involvement in signal propagation.
CONCLUSIONS
we analyzed network models that based on correlation of gene expression between interacting proteins which enabled us to track basic principles of signaling by its regulation.
the malignant signaling networks showed more diverse signaling pathways , shorter pathways , the networks were less centralized  and less dependent on hubs .
the cancer networks indicated signaling maintenance and increased error tolerance to punctual attacks even at hubs which makes cancer treatment at specific targets challenging.

 endogenous signal transduction in cancer cells is systematically disturbed to redirect the cellular decisions from differentiation and apoptosis to proliferation and, later, invasion. cancer cells acquire their malignancy through accumulation of advantageous gene mutations by which the necessary steps to malignancy are obtained. these selfish adaptations to independence can be described as a result from an evolutionary process of diversity and selection. we were interested to observe the resulting cellular signal transduction on a global view. experimental high throughput methods such as gene expression profiling with microarrays enable investigating the pathogenic function of tumors on a mesoscopic level. large-scale gene expression profiles were successfully used to predict clinical outcome and improved risk estimation. however these studies didn't relate genes and their expression to a functional context. to gain an understanding on a systems view, gene expression can be mapped onto cellular networks. several studies have been reported that used gene expression data from microarrays to describe specific characteristics of signaling networks in cancer. discriminative components of a protein-protein interaction network were identified by comparing gene expression patterns of metastatic and non-metastatic tumors in breast cancer and suited as risk markers for metastasis of breast cancer. new genetic mediators for prostate cancer were found with networks that were reversely engineered from gene expression profiles. besides this, insights into evolutionary principles were gained by the analysis of gene expression profiles. gene expression differences were used to define phylogenetic relationships of several drosophila species and a molecular clock for primates. furthermore, the regulation of signaling in yeast was investigated on a global scale to observe regulatory adaptation to the cellular environment. yeast responded to exogenous signals by shorter regulatory cascades to enable fast signal propagation. the aim of our work was to detect characteristic signaling properties of cancer cells on a global scale. we compared the regulation of signaling pathways in cancer with normal cells and mapped gene expression data of tumors and their corresponding non-malignant samples onto a comprehensive protein-protein-interaction network. for inferring regulation-principles in cellular signal transduction, we used a graph searching algorithm that tracked pathways with the highest correlation in regulation. we investigated twenty tumor-datasets comprising acute myeloid leukemia, esophageal squamos cell-, lung adeno- and renal clear cell carcinoma, breast-, cervical-, head-and-neck-, oral-tongue-, pancreas- and prostate cancer, and vulva interstitial neoplasia. the investigated tumors showed shorter pathways, but a larger extension of the network. the tumors displayed lower frequency of central proteins and links and a higher information entropy in their network. these findings were embedded into a novel signal-regulation motif which was observed considerably more often in normal cells when compared to tumor cells. similar to the study of cui and co-workers, central proteins were enriched with cancer mutations. we observed that these proteins showed higher regulation-integrity in the normal samples whereas the tumor samples showed motifs of regulatory maintenance of the neighbors of hubs.  constructing the signaling networks we assembled our signaling network employing a comprehensive data repository of known protein-protein interactions from the literature. proteins were represented by their coding genes and will also be denoted as nodes of the networks in the following. gene expression data of each cancer dataset and the corresponding set of normal samples was mapped onto the nodes of the network. depending on the coverage of the probes on the microarray chips, the intersection with the hprd network comprised of to nodes including to receptors and to transcription factors. similar to luscombe and co-workers, we assumed most likely signaling propagation by high co-regulation of genes of two neighboring proteins in the network. we calculated protein-protein-distances for each link by the co-regulation of the two interacting proteins. the link-distances were higher in cancer cells compared to normal cells. we defined pathways for each pair of receptors and transcription-factors by their shortest paths yielding a range of to pathways for each of the investigated cancer datasets. the tumor cells showed a distinct higher coverage of the original protein-interaction network for these pathways. table gives an overview of the network data for the different datasets we analyzed and also the network-coverage of all receptor-transcription-factor pathways for the tumors and the reference samples. from these pathways we constructed specific networks for each tumor and reference sample. for each tumor and normal sample, the constructed networks consisted only of those links and nodes that appeared at least once in their receptor-transcription-factor pathways. not-appearing links and nodes were discarded. we were interested if these networks were specific for the respective tumor type. for this, we extracted all somatically mutated genes for specific cancer tissues from a database and tested if our tumor networks contained genes which have been described specifically for the respective tumors. we performed enrichment tests and found that all tumor networks showed a considerably significant enrichment of their corresponding mutated tumor genes. tumors use shorter paths, more links and less hubs we calculated a variety of different network-features to characterize specific differences in signaling-regulation of tumor cells and non-malignant cells. the results are given in table and table and will be explained in the following. for getting a reasonable estimate of the general tendency of tumors, we calculated the average out of all datasets for cancer and normal networks and performed a significance test of the pair-wise differences between tumor and normal. the average path-length of cancer networks was less than for non-malignant. we wanted to know how often the same links were used for different signaling pathways. for this, we defined the frequency of a link as the number of receptor-transcription-factor pathways it was involved in. the average link-frequency was obtained by the number of links used in each single pathway from each respective receptor to each transcription factor, divided by the number of all used links. the average link frequency was higher in normal cells. similarly, the node frequency was calculated and showed the same tendency. hence networks of normal cells used more often the same central proteins and interactions for different signaling tasks. such a hub-like structure is the central characteristic of scale free networks. we were interested if the networks for cancer and normal samples followed these characteristics and if there were distribution differences between them. in deed, the link-frequency distribution of the networks of both entities followed a power law. in comparison to the networks from normal cells, the distributions of tumors showed a steeper decline. we calculated the exponent α of the distribution and observed larger exponents for cancer networks.. this agrees with the lower average of their link-frequency. these distributions also show that proteins of high connectivity in the networks of normal cells are more abundant. the clustering coefficient has been employed as a measure of connectedness of networks. we calculated the clustering coefficient and obtained lower values for the network of cancer cells supporting our findings that cancer showed a tendency for less centralized, less hub-dependent formation. specifically, the number of nodes with a clustering coefficient greater zero was distinctively higher in cancer cells. frequently involved genes are enriched with cancer mutated genes cui and co-workers compiled a selective list of cancer mutated genes which were derived from large scale sequencing and the literature. we compared this list with the most frequently involved nodes of each network and found significant enrichment for out of normal and tumor datasets. we then defined gene-lists of cancer mutated hubs for every cancer by intersecting the hubs of our network with the list of cancer mutated genes of cui et al.. interestingly, most of the genes which showed up in the tumor networks were also present in the normal networks. this may indicate that normal cells intrinsically pave the way for their specific evolvement into malignancy. signaling-regulation in cancer is detached at cancer mutated hubs but maintained in their vicinity uri alon and his co-workers studied occurrences of direction-motifs in triangles and revealed a large variety of substantial characteristics in signaling networks characterized by consistent and non-consistent feed-forward and feedback loops. we were interested in local regulation patterns of the networks at cancer mutated hubs. for this, we analyzed regulation motifs of every triangle consisting of exactly one hub and two of its neighbors which on their part also interact. we defined two regulation motifs. the first motif reflected the degree of regulatory integration of a hub and its network-vicinity and was defined by a high correlation of all pairs of nodes in the triangle motif. we found this motif significantly more often in normal cells. the second motif described triangles which pairs of hub-nodes showed high correlation in one tissue type and no correlation in the other, while the mutual correlation of nodes n1-n stayed in the same category. such a scenario is reasonable for a mutated cancer protein with loss-of-function leaving their neighbors unaffected. indeed, this motif occurred more often in the cancer networks. tumor networks are more robust against directed attacks albert and co-workers showed that scale free networks are error tolerant only against attacks of randomly selected nodes but not against directed removals of central nodes. we were interested in the robustness of the networks when removing their hubs. for this, we removed the most frequently involved nodes of every network and calculated the average of pair-wise distances as an estimate of the fragility of the networks. the relative increase of the network diameter due to the removal was distinctively larger in normal cells compared to cancer cells indicating higher robustness of the tumor networks against directed attacks at their hubs. lower information content in normal cells we used the number of pathways each single link was involved in as an estimate of the probability that information was passed through this link. in this simplified model, every pathway was treated equally. with this, we calculated the information content for each network. as a measure of disorder, shannon's information entropy was calculated for each network. the cancer networks exhibited a higher information entropy indicating their higher degree of dispersal. a comparative network motif inspired by the results described above, we designed a comparative network-motif which is illustrated in figure we wanted to put up a model in which cancer cells use different pathways for different tasks whereas normal cells use common signaling interactions for different tasks. therefore a model was designed such that two pathways of the normal tissue shared at least one common link, whereas the same operator-receiver pairs for the tumor did not share any link. we compared the abundance of this motif with the abundance of its counterpart in which the cancer cells used at least one common link and the normal cells did not share any link. we found a significantly higher number of our motif in which the normal cells share a common link. discussion we investigated network properties of cancer signaling by looking at co-regulation patterns of genes for different cancer types. we analyzed the general regulatory behavior of correlating gene expression samples of one tumor type and study, rather than analyzing the regulatory behavior of single patients. for this, we calculated a gene to gene distance metric for all samples of normal and cancerous tissues. the networks of the investigated tumors showed distinctive mechanisms in the regulation of signal transduction when compared to normal cells and had shorter path lengths. luscombe and co-workers analyzed the dynamics of regulatory networks in yeast. in comparison to endogenously caused changes, they discovered a different topological adaptation of the network when yeast responded to environmental changes. for having quick responses, yeast reacted to environmental changes by short regulatory cascades. our investigated cancer cells showed a similar tendency as yeast under stress at which fine tuned endogenous homeostasis is of minor importance. interestingly, for yeast, luscombe et al. discovered a higher frequency of hubs for stress responses whereas we discovered that the tumors used hubs less frequently. cells of normal sample had a more centralized network to regulate signals via common nodes and links. this was reflected by a smaller network, higher frequency of hubs, lower entropy and a higher number of our signaling motif in which the number of pathway-pairs with common links was counted. this makes sense, as fine-tuning and integrating diverse signals need to be coordinately transferred to the respective transcriptional response which is substantial for fine grained signaling homeostasis of normal cells to co-ordinate their signals in accordance to their cellular community in the tissue. degenerated tumor cells do not need this any more. in turn, the tumors showed a higher connectedness of the whole network which may strengthen their independency of exogenic perturbations. similar to cui and co-workers, we observed with our model that cancer specific mutations occur distinctively more often at hubs for signal transduction. such a mutation can cause a loss of function. this is beneficial for the cancer if the protein gets insensitive to upstream-signals and fires constitutively an oncogenic signal as e.g. the abl-bcr fusion protein in chronic myelogenous leukemia. if the protein acts as a tumor suppressor, a complete loss of function is beneficial for oncogenesis. in both scenarios, the regulation for signaling homeostasis of the local network environment is detached from this mal-functional protein and a coordinated regulation between the environment and this protein is not necessary any more. we observed this by counting distinctively less integration-motifs in tumors. interestingly, tumors seem to sustain the original signals between the environment. we observed this by higher counts of the disruption motif in tumors which reflects the disruption of co-regulation of the hub, but maintained regulation between the neighbors of the hubs. even though tumors may exhibit de-regulation of mal-functional hubs with their neighbors, such a maintained co-regulation of their neighbors gives evidence that bypass regulations are still necessary. ma'ayan and co-workers observed an accumulation of feedback and feed-forward loops at such hubs which supports this idea. tumors need to maintain the direct signal of e.g. a feed-forward loop which is necessary for the effect of the constitutive signal of an oncogenic hub. such oncogenic signaling motifs may have implications to drug therapy. if an oncogenic hub is treated resistance can occur by mutations of the target protein which reduce the affinity of the drug to the target. a combined therapy may avoid this evolvement by additionally blocking the signaling-maintenance of the neighbors. in addition, we found that the observed cancer networks showed higher error tolerance against directed attacks of hub removals. hence, some maintenance signals may not only support cancer mutated hubs but also pave the way for the signaling network to get independent of them, specifically for proteins of cancer mutated genes with loss-of-function. it is challenging but highly relevant to shed light into these effects experimentally with cell lines exhibiting drug resistances at such hubs. we analyzed networks based on cohorts of patients and used the correlation of expression between gene pairs for the whole cohorts. this approach does not allow the analysis of a single sample and therefore can't be employed for diagnosis of a single patient, but rather for the analysis of tumor subgroups. it may be worthwhile developing distance metrics of gene pairs for single samples with which the investigated topology features can be employed supporting diagnosis. we proposed a novel comparative signaling-motif for malignant signaling-regulation which sums up our findings. there have been elaborated studies on network motifs. our comparative cancer motif is different from these motifs in that it shows signaling-regulation in cancer reflecting less centralized formation. the comparative cancer motif agrees with our findings of non-integration but signaling-maintenance of proteins with higher involvement in signal propagation.  we analyzed network models that based on correlation of gene expression between interacting proteins which enabled us to track basic principles of signaling by its regulation. the malignant signaling networks showed more diverse signaling pathways, shorter pathways, the networks were less centralized and less dependent on hubs. the cancer networks indicated signaling maintenance and increased error tolerance to punctual attacks even at hubs which makes cancer treatment at specific targets challenging. 
in cancer cells we observed shorter pathways, larger extension of the networks, a lower signaling frequency of central proteins and links and a higher information content of the network. our global analysis revealed a distinct formation of signaling-regulation in cancer cells when compared to cells of normal samples. these proteins showed motifs of regulatory integration in normal cells which was disrupted in tumor cells. formation of cellular malignancy results from the disruption of fine tuned signaling homeostasis for proliferation, accompanied by mal-functional signals for differentiation, cell cycle and apoptosis. we wanted to observe central signaling characteristics on a global view of malignant cells which have evolved to selfishness and independence in comparison to their non-malignant counterparts that fulfill well defined tasks in their sample. proteins of high signaling frequency were enriched with cancer mutations.
 formation of cellular malignancy results from the disruption of fine tuned signaling homeostasis for proliferation, accompanied by mal-functional signals for differentiation, cell cycle and apoptosis. we wanted to observe central signaling characteristics on a global view of malignant cells which have evolved to selfishness and independence in comparison to their non-malignant counterparts that fulfill well defined tasks in their sample.  we investigated the regulation of signaling networks with twenty microarray datasets from eleven different tumor types and their corresponding non-malignant tissue samples. proteins were represented by their coding genes and regulatory distances were defined by correlating the gene-regulation between neighboring proteins in the network. in cancer cells we observed shorter pathways, larger extension of the networks, a lower signaling frequency of central proteins and links and a higher information content of the network. proteins of high signaling frequency were enriched with cancer mutations. these proteins showed motifs of regulatory integration in normal cells which was disrupted in tumor cells.  our global analysis revealed a distinct formation of signaling-regulation in cancer cells when compared to cells of normal samples. from these cancer-specific regulation patterns novel signaling motifs are proposed. 