chronic kidney disease is a clinical syndrome consisting of a variety of symptoms and metabolic disorders that arise from the progressive and irreversible decline of kidney function . the end stage of chronic kidney disease is uremia [ 13 ] , which is not an independent kidney disease , but rather a series of clinical manifestations that make uremia a syndrome that is common to most end - stage kidney diseases . during end - stage uremia , for example , uremia patients can experience severe conditions such as heart failure , psychosis , coma , or other life - threatening complications . uremia can be caused by many factors such as chronic glomerulonephritis , chronic pyelonephritis , renal tuberculosis , renal arteriosclerosis , stones in the urinary tract , enlarged prostate , bladder cancer , lupus , and diabetes . the kidneys can be damaged by various factors that severely affect kidney function , leading to defects in nitrogen metabolism and waste excretion . excess accumulation of wastes can upset internal balances that in turn cause further damage to the kidneys and other organs . renal failure can also be caused by extra - renal factors such as heart failure , urinary tract obstruction , and urinary reflux . uremia results in the bodily retention of many different compounds that can accumulate to dangerous levels . current research on uremia has mainly focused on the relationship between metabolomics and renal disease , and more than 90 different uremia - related compounds , some of which are toxic , have been identified . however , the exact genetic and/or molecular mechanism of uremia is still not fully understood because of its complex etiology and the difficulty in obtaining tissue samples . thus , several studies have examined transcriptome expression of blood or urine in uremia , and results from these studies identified many mirnas and genes , as well as related biological functions and pathways , that are important for uremia [ 1215 ] . for a more comprehensive understanding of the mechanisms that are involved in uremia , we analyzed a whole - genome microarray case - control study of 75 uremia patients and 20 healthy controls included in the national center for biotechnology information ( ncbi ) gene expression omnibus ( geo ) , and performed a gene co - expression analysis to construct networks of differentially expressed genes ( degs ) in uremia . finally , mirna enrichment analysis was used to detect key mirnas in each hub model . we hope the results from this study may provide a more comprehensive understanding of the molecular mechanisms that are important in uremia . for this study we selected the gene expression profile dataset with accession number gse37171   from the ncbi geo . the dataset was acquired using an affymetrix human genome u133 plus 2.0 array ( platform number gpl570 ) that analyzed peripheral blood from 75 end - stage renal failure ( uremia ) patients and 20 healthy controls . the raw data were first normalized by robust multi - array average ( rma ) background subtraction , and quantile normalized using affymetrix expression console software ; all background noises were removed . then we used significance analysis of microarrays ( sam ) algorithm ( |log fold change|>1 ; fdr < 0.05 )   to screened differentially expressed genes ( degs ) between uremia patients and control subjects . gene co - expression networks were performed by pearson s correlation with |r| > 0.9 and p<0.01 . the co - expressed hub module analysis was performed on the co - expression network using the molecular complex detection ( mcode ) plugin in cytoscape with a degree cutoff=2 , node score cutoff=0.2 , k - core=2 , and maximum depth=100 . significant co - expressed modules with mcode score > 4 and more than six nodes were selected . we used the database for annotation , visualization , and integrated discovery ( david )   to identified the biological processes and/or functions involving the hub modules . one of david s features is a functional annotation cluster tool wherein similar gene ontology ( go ) categories , based on the parent / child go term associations and the number of shared genes , can be grouped into a functional cluster . we used this feature to identify the relationship between the go terms ( biological processes in the go database ) and kyoto encyclopedia of genes and genomes ( kegg ) pathways . the threshold enrichment score for the enriched clusters was arbitrarily set as 1 . in this study the target genes of all mirnas were predicted using five prediction algorithms : pictar , diana - microt , miranda , rna22 , and targetscan . we collected 249 mirnas , 5,828 genes , and 60,038 interactions of mirna to gene . the mirna enrichment analysis is a useful tool to reveal modules of target gene sets with expression levels that change significantly relative to the background . the p values of certain mirnas and their target genes was estimated by the cumulative hypergeometric algorithm , and the function is as follows : in which m is the total number of genes tested , n is the number of genes targeted by a certain mirna , and k represents the number of degs in a given module ; x represents the number of genes overlap between degs in a given module and genes targeted by a certain mirna . at last , for this study we selected the gene expression profile dataset with accession number gse37171   from the ncbi geo . the dataset was acquired using an affymetrix human genome u133 plus 2.0 array ( platform number gpl570 ) that analyzed peripheral blood from 75 end - stage renal failure ( uremia ) patients and 20 healthy controls . the raw data were first normalized by robust multi - array average ( rma ) background subtraction , and quantile normalized using affymetrix expression console software ; all background noises were removed . then we used significance analysis of microarrays ( sam ) algorithm ( |log fold change|>1 ; fdr < 0.05 )   to screened differentially expressed genes ( degs ) between uremia patients and control subjects . gene co - expression networks were performed by pearson s correlation with |r| > 0.9 and p<0.01 . the co - expressed hub module analysis was performed on the co - expression network using the molecular complex detection ( mcode ) plugin in cytoscape with a degree cutoff=2 , node score cutoff=0.2 , k - core=2 , and maximum depth=100 . significant co - expressed modules with mcode score > 4 and more than six nodes were selected . we used the database for annotation , visualization , and integrated discovery ( david )   to identified the biological processes and/or functions involving the hub modules . one of david s features is a functional annotation cluster tool wherein similar gene ontology ( go ) categories , based on the parent / child go term associations and the number of shared genes , can be grouped into a functional cluster . we used this feature to identify the relationship between the go terms ( biological processes in the go database ) and kyoto encyclopedia of genes and genomes ( kegg ) pathways . in this study the target genes of all mirnas were predicted using five prediction algorithms : pictar , diana - microt , miranda , rna22 , and targetscan . we collected 249 mirnas , 5,828 genes , and 60,038 interactions of mirna to gene . the mirna enrichment analysis is a useful tool to reveal modules of target gene sets with expression levels that change significantly relative to the background . the p values of certain mirnas and their target genes was estimated by the cumulative hypergeometric algorithm , and the function is as follows : in which m is the total number of genes tested , n is the number of genes targeted by a certain mirna , and k represents the number of degs in a given module ; x represents the number of genes overlap between degs in a given module and genes targeted by a certain mirna . at last , we identified the degs between uremia patients and controls by the sam algorithm ( |log fold change| > 1 ; fdr < 0.05 ) and found 1,399 degs , including 311 upregulated genes and 1,088 downregulated genes . to investigate the interrelation between uremia - related degs , co - expression networks analysis was used to construct degs interaction networks in this study . as the results showed in figure 1 , we found a total of 1,046 genes ( 179 and 867 upregulated and downregulated genes , respectively ) and 59,290 interactions between the genes ( figure 1 ) . the interactions could be grouped into four networks that had more than four nodes , including one network wherein all nodes were downregulated genes ( 861 nodes , 57,604 interactions ) and three networks that had nodes where all genes were upregulated ( 132 nodes , 1,589 interactions ; 32 nodes , 83 interactions ; 5 nodes and 5 interactions , respectively ) . we next performed a co - expressed hub module analysis on the co - expression networks using the molecular complex detection ( mcode ) plugin in cytoscape ( mcode score > 4 and nodes > 6 ) . we also examined the biological processes and/or functions involving the modules by performing functional enrichment and clustering analysis tools in david . this analysis identified nine modules ( figure 2 , table 1 ) : m1 ( score=88.249 ) containing 221 downregulated degs and 19,503 interactions that were significantly enriched in membrane - enclosed lumen  ,  negative regulation of gene expression  ,  apoptosis  , and  rna splicing via transesterification reactions ; m2 ( score=16.976 ) containing 126 downregulated degs and 2,139 interactions that were significantly enriched in  membrane - enclosed lumen  ,  chromosome organization  ,  nucleotide binding  ,  endosome  , and golgi apparatus  ; m3 ( score=14.944 ) containing 36 upregulated degs and 538 interactions that were significantly enriched in  membrane - enclosed lumen;m4(score=6.944 ) containing 72 down degs and 500 interactions that were significantly enriched in  protein localization  ,  post - transcriptional regulation of gene expression  ,  rna processing  , and  hormone secretion  ; m5 ( score=3.778 ) containing nine upregulated degs and 34 interactions that were significantly enriched in  metal ion binding  ; m6 ( score=3.643 ) containing 14 upregulated degs and 51 interactions that were significantly enriched in induction of apoptosis  ; m7 ( score=3.415 ) containing 41 downregulated degs and 141 interactions that were significantly enriched in  mrna processing  and  gtpase regulator activity  ; m8 ( score=2.267 ) containing 30 downregulated degs and 68 interactions that were significantly enriched in  protein localization  ; m9 ( score=2.071 ) containing 14 upregulated degs and 29 interactions that were significantly enriched in  membrane - enclosed lumen  . mirna enrichment analysis was used to predict mirnas that could significantly target genes in each module . this analysis clearly showed that 15 mirnas were selectively enriched in three modules ( table 2 , figure 3 , p<0.01 ) . of these , hsa - mir-505 - 3p , hsa - mir-490 - 3p , hsa - mir-411 - 5p , hsa - mir-212 - 3p , hsa - mir-154 - 5p , and hsa - mir-132 - 3p were significantly enriched for degs in m1 , which may indicate that these mirnas are involved in regulating  proteolysis  ,  membrane - enclosed lumen  ,  negative regulation of gene expression  ,  negative regulation of gene expression  ,  apoptosis  , and  rna splicing via transesterification reactions  functions by targeting the degs in this module . furthermore , the mirnas hsa - mir-875 - 5p , hsa - mir-590 - 3p , hsa - mir-448 , hsa - mir-381 - 3p , hsa - mir-300 , hsa - mir-22 - 3p , hsa - mir-203a , hsa - mir-154 - 5p , and hsa - mir-153 - 3p were significantly enriched in m4 degs , suggesting that these mirnas may regulate posttranscriptional regulation of gene expression  ,  rna processing  , and  hormone secretion  functions . the mirna hsa - mir-139 - 5p was significantly enriched in m8 degs , such that this mirna may affect  protein localization  functions . notably , hsa - mir-154 - 5p was significantly enriched with degs in both m1 and m4 . we identified the degs between uremia patients and controls by the sam algorithm ( |log fold change| > 1 ; fdr < 0.05 ) and found 1,399 degs , including 311 upregulated genes and 1,088 downregulated genes . to investigate the interrelation between uremia - related degs , co - expression networks analysis was used to construct degs interaction networks in this study . as the results showed in figure 1 , we found a total of 1,046 genes ( 179 and 867 upregulated and downregulated genes , respectively ) and 59,290 interactions between the genes ( figure 1 ) . the interactions could be grouped into four networks that had more than four nodes , including one network wherein all nodes were downregulated genes ( 861 nodes , 57,604 interactions ) and three networks that had nodes where all genes were upregulated ( 132 nodes , 1,589 interactions ; 32 nodes , 83 interactions ; 5 nodes and 5 interactions , respectively ) . we next performed a co - expressed hub module analysis on the co - expression networks using the molecular complex detection ( mcode ) plugin in cytoscape ( mcode score > 4 and nodes > 6 ) . we also examined the biological processes and/or functions involving the modules by performing functional enrichment and clustering analysis tools in david . this analysis identified nine modules ( figure 2 , table 1 ) : m1 ( score=88.249 ) containing 221 downregulated degs and 19,503 interactions that were significantly enriched in proteolysis  ,  membrane - enclosed lumen  ,  negative regulation of gene expression  ,  apoptosis  , and  rna splicing via transesterification reactions  ; m2 ( score=16.976 ) containing 126 downregulated degs and 2,139 interactions that were significantly enriched in  membrane - enclosed lumen  ,  chromosome organization  ,  nucleotide binding  ,  endosome  , and  golgi apparatus  ; m3 ( score=14.944 ) containing 36 upregulated degs and 538 interactions that were significantly enriched in  membrane - enclosed lumen;m4(score=6.944 ) containing 72 down degs and 500 interactions that were significantly enriched in  protein localization  ,  post - transcriptional regulation of gene expression  ,  rna processing  , and  hormone secretion  ; m5 ( score=3.778 ) containing nine upregulated degs and 34 interactions that were significantly enriched in  metal ion binding  ; m6 ( score=3.643 ) containing 14 upregulated degs and 51 interactions that were significantly enriched in induction of apoptosis  ; m7 ( score=3.415 ) containing 41 downregulated degs and 141 interactions that were significantly enriched in  mrna processing  and  gtpase regulator activity  ; m8 ( score=2.267 ) containing 30 downregulated degs and 68 interactions that were significantly enriched in  protein localization  ; m9 ( score=2.071 ) containing 14 upregulated degs and 29 interactions that were significantly enriched in  membrane - enclosed lumen  . mirna enrichment analysis was used to predict mirnas that could significantly target genes in each module . this analysis clearly showed that 15 mirnas were selectively enriched in three modules ( table 2 , figure 3 , p<0.01 ) . of these , hsa - mir-505 - 3p , hsa - mir-490 - 3p , hsa - mir-411 - 5p , hsa - mir-212 - 3p , hsa - mir-154 - 5p , and hsa - mir-132 - 3p were significantly enriched for degs in m1 , which may indicate that these mirnas are involved in regulating  proteolysis  ,  membrane - enclosed lumen  ,  negative regulation of gene expression  ,  negative regulation of gene expression  ,  apoptosis  , and  rna splicing via transesterification reactions  functions by targeting the degs in this module . furthermore , the mirnas hsa - mir-875 - 5p , hsa - mir-590 - 3p , hsa - mir-448 , hsa - mir-381 - 3p , hsa - mir-300 , hsa - mir-22 - 3p , hsa - mir-203a , hsa - mir-154 - 5p , and hsa - mir-153 - 3p were significantly enriched in m4 degs , suggesting that these mirnas may regulate  protein localization  , posttranscriptional regulation of gene expression  ,  rna processing  , and  hormone secretion  functions . the mirna hsa - mir-139 - 5p was significantly enriched in m8 degs , such that this mirna may affect  protein localization  functions . notably , hsa - mir-154 - 5p was significantly enriched with degs in both m1 and m4 . gene co - expression network analysis is a useful tool in genomics studies , because of its extraction of genes / mirnas with similar expression patterns from high throughput molecule expression datasets . the analysis could identify the relationships between genes and facilitated the understanding of the regulatory mechanism of genes . in the present study , we used the degs to construct the gene co - expression networks , which not only could identify the gene interactions / networks related to uremia , but also could reduce the false positive rate of the degs . meanwhile , the hub module analysis of differentially expressed genes identified clusters of genes with similar expression profiles that are suggestive of co - regulation . exploration of these co - regulated gene modules could help find underlying regulation molecules that are pivotal for gene expression in uremia . to explore the molecular mechanisms of degs in uremia , mirna enrichment analysis was used to predict pivotal mirnas that could significantly target genes in each functional module . results from these bioinformatics studies might inform future studies of the pathways and mechanisms that could be targeted for uremia treatments . recently , some reports have explored the molecule alterations in the blood of ckd patients [ 2831 ] . .   found that over one third of human genes were differential expressed in blood of uremia , and involved in various biological functions and pathways , including many immune and biological mechanisms . meanwhile , granata et al .   screened genome expression in peripheral blood mononuclear cells of ckd patients , and found some degs involved in mitochondrial respiratory system . moreover , neal et al .   found the mass of mirnas were reduced in the blood of ckd patients . all these reports indicated a profound magnitude of the changes in ckd , but not enough to define further critical molecular mechanisms in ckd . however , the bioinformatics tools used in our study might provide us with insight into critical biological alterations . in this study , co - expression analysis and co - expressed hub module analysis found nine hub modules that may play important roles in uremia . for example , module m1 was significantly enriched in  proteolysis  ,  membrane - enclosed lumen  ,  negative regulation of gene expression  ,  apoptosis  , and  rna splicing via transesterification reactions  . these biological functions have been demonstrated to have significant roles in uremia [ 3235 ] . in accord with the clinical characteristics of uremia , protein fragments in plasma and urine are a hallmark of many renal diseases , and can be an important component of plasma and urine for diagnostic and research purposes . meanwhile , uremia is typically accompanied by apoptotic erythrocyte death that may lead to anemia . ,  posttranscriptional regulation of gene expression  ,  rna processing  , and  hormone secretion  functions , suggesting that in uremia homeostasis may be severely affected and accompanied by significant disturbances in rna and protein metabolism . notably , the  membrane - enclosed lumen  function was significantly enriched in modules m1 , m2 , m3 , and m9 . indeed , uremia is typically accompanied by changes in cellular homeostasis that involve metabolic disturbances in membrane - enclosed lumens such as the nucleus and endoplasmic reticulum . micrornas are small non - coding , single - stranded rnas that regulate messenger rnas at the post - transcriptional level . the role of mirnas in chronic kidney disease ( ckd ) has recently gained increased attention . in this study , we found 15 key mirnas that were significantly enriched in three co - expressed modules . of these mirnas , mirna-21 - 3p and mirna-210 - 3p were shown to be significantly related to uremia . moreover , hsa - mir-154 - 5p was significantly enriched in both m1 and m4 degs . however , the other mirnas identified in this study await further characterization and investigation for their role in uremia - related mechanisms . our study provides a comprehensive perspective of gene expression in uremia using a combination of gene co - expression network analyses and degs analyses to identify nine hub co - expressed modules that can be assigned to corresponding biological functions . the 15 key mirnas identified in the mirna enrichment analysis likely also play important roles in uremia . further study of the networks and mirnas identified in this study may allow a better connection of biological functions , genes , and mirnas that underpin the network modules , and in turn could be used to elucidate the molecular mechanisms involved in uremia . in conclusion , our study provided an orientation for further work , and the bioinformatics analysis pipeline of this study provides a model for others .
backgrounduremia involves a series of clinical manifestations and is a common syndrome that occurs in nearly all end - stage kidney diseases . 
 however , the exact genetic and/or molecular mechanisms that underlie uremia remain poorly understood.material/methodsin this case - control study , we analyzed whole - genome microarray of 75 uremia patients and 20 healthy controls to investigate changes in gene expression and cellular mechanisms relevant to uremia . 
 gene co - expression network analysis was performed to construct co - expression networks using differentially expressed genes ( degs ) in uremia . 
 we then determined hub models of co - expressed gene networks by mcode , and we used mirna enrichment analysis to detect key mirnas in each hub module.resultswe found nine co - expressed hub modules implicated in uremia . 
 these modules were enriched in specific biological functions , including 
 proteolysis  ,  membrane - enclosed lumen  , and 
 apoptosis  . 
 finally , mirna enrichment analysis to detect key mirnas in each hub module found 15 mirnas that were specifically targeted to uremia - related hub modules . 
 of these , mirna-21 - 3p and mirna-210 - 3p have been identified in other studies as being important for uremia.conclusionsin summary , our study connected biological functions , genes , and mirnas that underpin the network modules that can be used to elucidate the molecular mechanisms involved in uremia .