prostate cancer has known a complex polygenic disorder which would be one of the most known cause of mortality in men .
although recent studies have identified a number of variants , gene fusions , and expression signatures have affiliated with prostate cancer , then identification and characterization of genes that have involved in this cancer , has remained as a formidable challenge .
the complexity and multigenic nature of cancer has caused various genome - wide studies , have been achieving a systems - level understanding of the key genetic mediators , involved in prostate cancer .
one focal point in cancer analysis would be the reconstruction of co - expression networks .
when accurate , co - expression networks have represented the key mediators that have involved in a specific process . the availability of the genome - wide gene expression data has helped the development of various state - of - art co - expression networks reconstruction methods [ 4 - 6 ] . taking a systems - wide approach ,
we have reconstructed two stage - specific co - expression networks , based on a comprehensive prostate cancer gene expression dataset containing 171 different samples monitoring gene expression in two different cell states .
it has played critical vital functions in male - specific organs , such as testis and prostate glands .
there were evidences indicating that many forms of tumors have associated with structural and gene expression variations of the y chromosome .
previous studies have shown that there were about 60 genes existing , but in the present day human y chromosome have identified as the survivors of at least a set of 1500 genes that have assumed to exist in the early proto - y element [ 7 , 9 ] . although the involvement of y chromosome has reported for association with prostate cancer [ 10 , 11 ]
, currently there was little information regarding the contribution of the y linked genes , with progression of prostate cancer .
in this paper we have tried to address this problem to identify candidate genes on the y chromosome that have involved in prostate cancer .
our analysis has led to identification of both well - established and novel genes , have involved in the prostate cancer .
after extensive literature search , we have found that for 16 of our candidate genes ( about 60% ) , there was experimental evidences suggesting a role in prostate cancer .

 network reverse engineering approaches 
  reverse engineering of co - expression networks , from the whole genome data , has entailed deciphering the underlying gene regulatory circuits , observing the changes in gene expression profiles .
after advances in high - throughput technologies , several computational reverse engineering has approached the different statistical measures [ 12 - 15 ] , including information - theoretic network inference methods , which has identified connections , between genes , to approach the quantity of common information to any pair of genes .
based on the dialogue on reverse engineering assessment and methods 5 ( dream5 ) challenge , the context likelihood relevance ( clr ) algorithm by faith et al .  
briefly , clr has evaluated an interaction between two genes as significant by estimation the significance of their mutual information ( mi ) value , against a mi values background distribution of every other pair , which have involved one of the two genes of interest . in this way
, the significance level has dynamically determined for each interacting pair , according to their expression profiles . to give a gene expression dataset and the significance scores ,
have calculated by the clr algorithm , the corresponding empirical false discovery rate ( fdr ) could be estimated by running the algorithm on randomly shuffled datasets . in this study , we have reconstructed two co - expression networks using clr with an fdr threshold of 0.05 . through a series of expression studies that have conducted on all 60 genes ( locus ) on the human y chromosome , at the initial phase of this study , we have retrieved all genes that interact with y chromosome genes from information hyperlinked over proteins ( ihop ) database .
overall , we have identified 471 genes in the entire human genome that interact with genes on the y chromosome . 

prostate cancer microarray data 
  prostate cancer microarray data have downloaded from the gene expression omnibus ( geo ) database , accession number gds2545 .
this dataset has contained 171 samples , including samples from normal prostate tissue free of any pathology ( normal with 18 samples ) , normal prostate tissue adjacent to tumors ( adjacent with 63 samples ) , primary prostate tumor tissue ( tumor with 65 samples ) , and metastatic prostate cancer ( metastasis with 25 samples ) .
we have considered normal and adjacent tissues as normal prostate tissues and tumor and metastasis as cancerous prostate tissues .
microarray data have preprocessed and analyzed , using the limma package in r   which has originally developed for differential expression analysis of microarray data .
quantile normalization and a moderated t - statistic have used to find differentially expressed genes .
the first was for identifying the most likely candidate oncogene among the genes in the reconstructed networks via topological analysis .
the second one was for evaluating the y - chromosome genes that involved in prostate cancer , based on whether they interact with the identified candidate(s ) in the reconstructed sub - networks .
to predict the key genes in the prostate cancer , we have searched in the stage - specific co - expression networks of prostate cancer for genes which either had a high number of connections or were bottleneck [ 20 , 21 ] .
the bottleneck genes were important , because if they have removed from a network , the network would be disrupted , as they are major intersections between clusters in the network . to find such genes ,
we have topologically analyzed both constructed networks , following the same rules , have been using cyto - hubba package . .

 stage specific network reconstruction of prostate cancer 
  for the first step , we have reconstructed two independent co - expression networks for the normal and cancerous tissues using the clr algorithm .
we have considered 310 genes of the 471 genes extracted from ihop due to the availability of expression data from our transcriptomics dataset .
the resulting normal network that has contained 2147 interactions , and the cancerous network contained 2201 interactions have used for further analysis .
additionally , topological analysis of the networks has revealed that both networks exhibit the small - word property   and scales - free architecture   ( figure 1 ) .
both reconstructed networks have mainly composed of the same set genes ; however the conserved interactions among these two networks were very low . 

detection of essential genes involved in the prostate cancer 
  to consider the importance of hub and bottleneck proteins in the structure of co - expression networks , the 10 highest - ranked genes have identified for each stage - specific network based on their degree and bottleneck scores , separately . in total , 22 candidate genes have selected for further analysis . in each network , we have categorized these 22 genes based on their degree and bottleneck scores in two groups : 1 ) hub - nonbottleneck : genes with high degrees and low bottleneck scores , were putative party hubs ; 2 ) hub - bottleneck : genes with high degrees and high bottleneck scores , were putative date hubs .
the results have shown hub type variation for 19 genes across different tissues , whereas 3 other genes have functionally conserved as date hubs under both conditions ( table 1 ) .
detection of y chromosome genes involved in the prostate cancer 
  for the second step , due to detection of the y chromosome genes that interact with 19 defined critical genes , we have extracted a sub - network using these critical genes and 60 genes on the y chromosome .
the sub - networks have extracted and visualized by cytoscape software ( figure 2 ) . according to figure 2 ,
all of the 19 defined critical genes had interactions with 19 genes of 60 genes on the y chromosome in the normal sub - network while the number of y chromosome genes in cancerous network decreased to 15 genes of 60 genes .
closer investigation have shown some genes such as sry , xkry2 , amely , uty , ddx3y and eif1ay of the normal network have substituted by bpy2 and rps4y1 in the cancerous network ( figure 2 ) .
topological comparison of these two sub - networks have recapitulated the previous findings about extensive networks rewiring .
we have done some literature search about these 8 genes . among the 8 genes , we have identified 4 genes associated with prostate cancer .
although bpy2 , uty , sry and eif1ay would be the most prominent in prostate cancer [ 11 , 28 ] , we could not find any evidence to show the relationship between the 4 remaining genes such as rps4y1 , amely , xkry2 and ddx3y to any type of cancer .
to reconstruct cell stage specific co - expression networks , we have focused on the available comprehensive transcriptome dataset , originally published in . in our approach , genes have analyzed and prioritized based on the transcriptome data .
hence , we were able to make reliable predictions only for genes with altered expression level across normal and cancerous conditions . to focus on these genes ,
we have considered only genes that have up-/ down - regulated ( fold change1.5 and p - value<0.05 ) in cancerous stage and had interaction with y chromosome genes ( 310 genes ) .
although most of the computational have approached to identify group of genes that have significantly up-/down regulated during cancer progression , we have believed in complementary analysis to identify critical genes which involved in cancer .
we have checked for enrichment of known cancer genes among this set by using a previously curated list of 555 high confidence cancer genes , originally published in .
we have also collected 100 genes identified as mediators in metastatic prostate cancer from , and we have added 276 genes annotated as either a cancer pathway or prostate cancer gene in the kegg database . to identify master genes and their associated interactions governing cell - specific behavior in normal and cancerous state , we have compared the networks of prostate cells in both stages with each other .
han et al . suggested the existence of two types of protein hubs in the protein - protein interaction networks : party hubs and date hubs .
although both interactions with many proteins , the difference was that party hubs would be proteins that interact with many other proteins simultaneously , whereas date hubs interacted with their partners asynchronously . by definition , the bottleneck proteins were responsible for the inter connection of clusters in the network , and thus bottlenecks with high degrees were most likely date hubs which contain groups of genes that assist to present common functions [ 22 , 31 ] .
the obtained results have recapitulated previous findings which have demonstrated some active sub - networks contained regulatory interactions supplant by new interactions and changed their degrees during different conditions .
our analysis has led to the identification of both well - established and novel genes involved in this type of cancer .
our result has led to the identification of 27 important genes putatively involved in prostate cancer .
additionally , we have identified 19 of 27 genes that are bottleneck and changed their interaction during cancer progression . although the functional role of 12 of 19 genes were well known as critical genes for prostate cancer , the remaining 7 genes ( usp9x , sf1 , bcr , hexa , tnfrsf25 , gpi and olfm1 ) were new candidates that might have critical roles in prostate cancer based on topological significance and regulatory changes during cancer progression ( table 1 ) .
we have identified these genes associated with other cancer types such as breast , leukemia , bladder and lung ( table 1 for more details ) .
we have also shown that in addition to the 4 well known y chromosome gene ( bpy2 , sry , uty and eif1ay ) , 4 other y chromosome genes such as rps4y1 , amely , xkry2 and ddx3y have rewired and thus predicted these to have important roles in prostate cancer . in this paper , we have presented an accurate network - based approach for the analysis of transcriptome data . the analysis of prostate state specific co - expression networks has revealed that for 16 of our candidate genes , there were experimental evidences regarding to their role in prostate cancer . additionally , we have found that about 85% of our candidate genes to be linked to various cancers , so they would be used as key factors for future research in the field of cancer studies .
the low numbers of predictions and high degree of overlap with previously known events have demonstrated the high efficiency of our approach .
in addition , the low number of predicted gene sets , has made it easy for designing follow up experiments to validate results .
for the genome - wide investigations , this would be a fundamental challenge for future development of the translational medical informatics , yielding new potential drug target candidates .
in this paper , we have presented an accurate network - based approach for the analysis of transcriptome data .
the analysis of prostate state specific co - expression networks has revealed that for 16 of our candidate genes , there were experimental evidences regarding to their role in prostate cancer .
additionally , we have found that about 85% of our candidate genes to be linked to various cancers , so they would be used as key factors for future research in the field of cancer studies .
the low numbers of predictions and high degree of overlap with previously known events have demonstrated the high efficiency of our approach .
in addition , the low number of predicted gene sets , has made it easy for designing follow up experiments to validate results .
for the genome - wide investigations , this would be a fundamental challenge for future development of the translational medical informatics , yielding new potential drug target candidates .
 backgroundprostate cancer , a serious genetic disease , has known as the first widespread cancer in men , but the molecular changes required for the cancer progression has not fully understood . 
 availability of high - throughput gene expression data has led to the development of various computational methods , for identification of the critical genes , have involved in the cancer.methodsin this paper , we have shown the construction of co - expression networks , which have been using y - chromosome genes , provided an alternative strategy for detecting of new candidate , might involve in prostate cancer . in our approach , we have constructed independent co - expression networks from normal and cancerous stages have been using a reverse engineering approach . 
 then we have highlighted crucial y chromosome genes involved in the prostate cancer , by analyzing networks , based on party and date hubs.resultsour results have led to the detection of 19 critical genes , related to prostate cancer , which 12 of them have previously shown to be involved in this cancer . 
 also , essential y chromosome genes have searched based on reconstruction of sub - networks which have led to the identification of 4 experimentally established as well as 4 new y chromosome genes might be linked putatively to prostate cancer.conclusioncorrect inference of master genes , which mediate molecular , has changed during cancer progression would be one of the major challenges in cancer genomics . in this paper 
 , we have shown the role of y chromosome genes in finding of the prostate cancer susceptibility genes . 
 application of our approach to the prostate cancer has led to the establishment of the previous knowledge about this cancer as well as prediction of other new genes . 
Introduction
Materials and Methods
Results
Discussion
Conclusion