cardioembolic stroke ( ces ) , which causes 20% of all ischemic strokes each year , leads to severe neurological deficits .
ces is associated with high mortality and is a common cause of its atrial fibrillation ( af ) , which has an increasing incidence with age [ 35 ] .
proposed that af is an important and treatable cause of recurrent stroke and needs to be ruled - out by thorough evaluation before the diagnosis of cryptogenic stroke is assigned .
ces is largely preventable through control of major primary cardioembolic risk factors , such as hyperlipidemia and high blood pressure .
giralt et al .   offered evidence of significant genetic involvement in ischemic stroke . in recent years
, gene expression profiling of human disease tissues has provided insights into molecular mechanisms and eventually led to the identification of novel therapeutic targets .
currently available high - throughput microarray experiments were developed to analyze genetic expression patterns with differentially expressed genes ( deg ) and dysregulated pathways .
canonical reports claimed that gene expression patterns can identify biomarkers of ischemic stroke , which highlighted the relevance of the innate immune system through deg   and signaling pathways [ 1113 ] .
however , most methods did not consider regulatory cross - talk among pathways , and treated pathways as independent mechanisms .
although it is intuitive that interacting pathways could influence each other , the presence of this frame and available technique have not been completely studied yet .
antonio et al .   developed an integrated approach to identify functional mirnas regulating pathway cross - talk in breast cancer with pairs of pathways .
differential protein - protein interaction networks were constructed in ces with akaike information criterion ( aic ) method . to the best of our knowledge , there are few studies that constructed pathway networks correctly to discriminate controls versus ces . in this work
we develop an integrated approach that is able to construct individual networks comprising pathways cross - talk to quantify differences between ces and controls .
we used the individualized pathway aberrance score ( ipas ) to assess pathway statistics of every ingenuity pathways analysis ( ipa ) pathway .
random forest ( rf ) classification was implemented to calculate the auc of every network .
these procedures were tested by monte carlo cross - validation ( mccv ) for 50 bootstraps .
. the novel approach may be the basis of individual medical treatment in ces , serving as therapy targeting markers .
one biological dataset , e - geod-58294 , was derived from the gene expression omnibus ( geo ) database ( http://www.ncbi.nlm.nih.gov/geo/ ) .
the platform was a - affy-44  affymetrix genechip human genome u133 plus 2.0 , which was used to read the gene chip % h0 .
the linear models for microarray data ( limma ) was then used to preprocess data .
after quantile data normalization performed by robust multi - array average ( rma ) , 20 544 genes were obtained . in order to identify a group of pathways significantly enriched in ces with respect to controls , we collected 589 biological pathways including 5169 genes from the ipa tool ( http://www.ingenuity.com/ ) .
after genes of expression profile were enriched in ipa pathways , we focused on 4929 genes .
raw p - values were adjusted by false - discovery rate ( fdr ) procedure for multiple testing corrections .
a total of 23 accumulated normal samples ( ans ) were used to identify ipa pathways as reference .
individual normal sample gene expression was standardized with the mean and standard deviation ( sd ) . for genes of every ces sample , as quantile normalization was performed .
average z equation was recently proved to be a biologically valid modification of pathway analysis methods for ipas .
z=(z1 , z2 ,  , zn ) represents the expression state of a pathway where zi denotes the standardized expression value of i - th gene and the number of genes existing in the pathway is n. gene statistics of each gene from every ces sample : each ipa pathway statistics : zi represents the standardized gene level statistics of 1-i gene and the number of genes existing in the pathway is n. z values of every pathway in ces samples were gathered after significance testing . differentially expressed pathways were selected with z<0.05 .
the discriminating score ( ds ) was computed to quantify pathway cross - talk in each sample for the pair of pathways x and y. ds was defined as where mx and sx represent mean and standard deviation of expression levels of genes in a pathway x and my and sy in a pathway y .
ds score indicates the relationships between pairs of pathway , with a larger value indicating relatively higher difference of activity between pathways .
ds of normal samples was standardized using the mean and sd as reference . z values of every pathway pair in ces samples
z values of differentially expressed pathways and pathway pairs were used to construct individual networks with cytoscape version 3.2.0 .
it randomly selected expression data in proportion 6:4 to form the training and testing set .
then the process was repeated in 50 bootstraps , randomly generating new training and test partitions each time .
the number of main networks appearing in the 50 bootstraps was counted by ranking all networks with their auc values .
one biological dataset , e - geod-58294 , was derived from the gene expression omnibus ( geo ) database ( http://www.ncbi.nlm.nih.gov/geo/ ) .
the platform was a - affy-44  affymetrix genechip human genome u133 plus 2.0 , which was used to read the gene chip % h0 .
the linear models for microarray data ( limma ) was then used to preprocess data .
after quantile data normalization performed by robust multi - array average ( rma ) , 20 544 genes were obtained .
in order to identify a group of pathways significantly enriched in ces with respect to controls , we collected 589 biological pathways including 5169 genes from the ipa tool ( http://www.ingenuity.com/ ) .
after genes of expression profile were enriched in ipa pathways , we focused on 4929 genes .
raw p - values were adjusted by false - discovery rate ( fdr ) procedure for multiple testing corrections .
a total of 23 accumulated normal samples ( ans ) were used to identify ipa pathways as reference .
individual normal sample gene expression was standardized with the mean and standard deviation ( sd ) . for genes of every ces sample , as quantile normalization was performed .
average z equation was recently proved to be a biologically valid modification of pathway analysis methods for ipas .
z=(z1 , z2 ,  , zn ) represents the expression state of a pathway where zi denotes the standardized expression value of i - th gene and the number of genes existing in the pathway is n. gene statistics of each gene from every ces sample : each ipa pathway statistics : zi represents the standardized gene level statistics of 1-i gene and the number of genes existing in the pathway is n. z values of every pathway in ces samples were gathered after significance testing .
the discriminating score ( ds ) was computed to quantify pathway cross - talk in each sample for the pair of pathways x and y. ds was defined as where mx and sx represent mean and standard deviation of expression levels of genes in a pathway x and my and sy in a pathway y .
ds score indicates the relationships between pairs of pathway , with a larger value indicating relatively higher difference of activity between pathways .
ds of normal samples was standardized using the mean and sd as reference . z values of every pathway pair in ces samples
z values of differentially expressed pathways and pathway pairs were used to construct individual networks with cytoscape version 3.2.0 .
it randomly selected expression data in proportion 6:4 to form the training and testing set .
then the process was repeated in 50 bootstraps , randomly generating new training and test partitions each time .
the number of main networks appearing in the 50 bootstraps was counted by ranking all networks with their auc values .
in the present study we developed an integrated approach that was sufficient to construct individual networks comprising pathways cross - talk to quantify differences between ces and controls .
rf classification was implemented to calculate auc of every network , which was tested by mccv for 50 bootstraps .
we obtained a heatmap in which pink squares indicate pathway pairs for classification in the training dataset for that bootstrap ( the frequency > 6 ) .
there were 4 pairs of pathways in 46 bootstraps : cholesterol biosynthesis i and cholesterol biosynthesis ii , cholesterol biosynthesis i and cholesterol biosynthesis iii , cholesterol biosynthesis ii and cholesterol biosynthesis iii , uracil degradation ii and thymine degradation .
individual networks were ordered with respect to their auc and 28 networks with auc > 0.9 were found between ces and controls . among them , 3 networks with auc=1.0 had the best performance for classification of ces and normal samples for all 50 bootstraps . as shown in figure 2 , the best individual networks were in 4 , 10 , and 23 bootstraps .
therefore , the 3 pathway networks were able to significantly identify ces versus controls , which showed as biomarkers in the regulation and development of ces .
then we found there were 22 pairs of pathways that commonly appeared in 3 networks ( table 1 ) , which revealed that the pathway pairs were important in regulating ces .
given the substantial difference in the activities of main networks between ces and controls , we examined its effectiveness in classifying ces and normal samples based on their profiles . in the best 3 networks
, we focused on pathways that had multi - cross - talk with others . the msp - ron signaling pathway had the most cross - talk , which played an important interaction role in the best networks .
a previous study has reported that msp - ron signaling is important for the invasive growth of many types of cancers and appeared to have potential as a therapeutic target .
pathway analysis has become the first choice for extracting and explaining the underlying pathology for high - throughput molecular measurements .
personalized identification of altered pathway pairs is important for understanding disease mechanisms and for the future application of custom therapeutic decisions .
existing pathway analysis methods are not suitable for identifying the pathway aberrance that may occur in an individual sample .
therefore , we employed the ipas to analyze the personalized identification of networks , taking advantage of a vast number of normal samples . a key innovation of the method is ipas using ans in ces .
proved that the average z equation can efficiently reveal noticeable aberrance in expression profiles and clinical significance , which sufficed to confirm the best averaged validation rate and distinguish a known survival - relevant pathway statistically .
furthermore , ans data is expected to be available in more fields of medicine along with rapid advances in high - throughput databases .
ds obtained lightly more improvement than the euclidean distance as a metric to quantify pathway cross - talk . in recent years
, different validation technologies have been generally used to evaluate performance of pathways and networks in medical regression analysis .
the mccv pays attention to a notable part of the sample at a time during network building and validation with multi - repeats .
compared with conventional validation tests for capturing the best predictor variables , mccv showed superior performance , resulting from a form of cross - validation based on vast combinations of data sets .
interestingly , mccv has not been utilized in individual networks comprising pathways cross - talk in ces patients . in this study we developed an integrated approach to quantify differences between ces and controls with the mccv test , which suggests that mccv worked better , based on strong predictive ability .
screened networks were efficient in distinguishing differences among individual ces samples , and can provide broader carcinogenic insight in personalized medicine .
the final purpose of our approach was to detect the best network able to discriminate ces versus controls .
we found that the 3 best networks were similar and had 22 common pairs of pathways .
we tended to select network 10 to differentiate ces disease from normal samples , with the fewest pairs of pathways ( figure 2b ) .
our novel approach identified 3 networks able to accurately classify ces and normal samples in individuals .
 backgroundcardioembolic stroke ( ces ) , which causes 20% cause of all ischemic strokes , is associated with high mortality . 
 previous studies suggest that pathways play a critical role in the identification and pathogenesis of diseases . 
 we aimed to develop an integrated approach that is able to construct individual networks of pathway cross - talk to quantify differences between patients with ces and controls.material/methodsone biological data set e - geod-58294 was used , including 23 normal controls and 59 ces samples . 
 we used individualized pathway aberrance score ( ipas ) to assess pathway statistics of 589 ingenuity pathways analysis ( ipa ) pathways . 
 random forest ( rf ) classification was implemented to calculate the auc of every network . 
 these procedures were tested by monte carlo cross - validation for 50 bootstraps.resultsa total of 28 networks with auc > 0.9 were found between ces and controls . among them , 3 networks with auc=1.0 had the best performance for classification in 50 bootstraps . 
 the 3 pathway networks were able to significantly identify ces versus controls , which showed as biomarkers in the regulation and development of ces.conclusionsthis novel approach could identify 3 networks able to accurately classify ces and normal samples in individuals . 
 this integrated application needs to be validated in other diseases . 
Background
Material and Methods
Step 1: Datasets
Step 2: Pathway enrichment analysis
Step 3: Pathway-level statistics
Step 4: Pathway pairs
Step 5: Construction of network
Step 6: Random Forest (RF) classification
Step 7: Selection of the best network
Results
Discussions
Conclusions