BACKGROUND
various gene signatures related to the survival and recurrence of hepatocellular carcinoma  have been identified using microarray analysis, and proposed to supplement clinicopathological prognostic factors for prediction of patient outcomes  <cit> .
in fact, prognostic genes are beneficial for the development of risk scores based on gene expression, that can overcome the limitations of clinical prognostic factors  <cit> .
interestingly, prognostic genes can be extracted from not only hcc, but also adjacent non-tumor liver tissues  <cit> .
however, the heterogeneous clinicopathological and biological nature of hccs makes it difficult to identify significant common genes that fit to different datasets  <cit> .
therefore, elucidation of the common biological functions related to prognosis that may be applicable to different datasets is also difficult.
one way to overcome the limitations of the gene-based approach is the use of functionally related predefined gene sets, such as pathways, instead of individual genes  <cit> .
pathway analysis provides biological information that facilitates characterization of the functional network and the relationships between selected significant genes  <cit> .
a number of methods have been proposed to identify the pathways associated with prognosis  <cit> .
recent reports also indicate that rather than whole genes, a subset of genes correlated with sample phenotype is more accurate in predicting clinical outcomes of patients  <cit> .
among the diverse factors implicated in hcc development, hepatitis b  and hepatitis c viruses  are the major etiological risk factors.
although hccs infected chronically with hbv and hcv are not distinguishable from histological and clinical evaluations  <cit> , studies to date consistently imply that different molecular mechanisms underlie the development of hbv- and hcv-positive hccs  <cit> .
we additionally observed functional differences and similarities between the development of hcv- and/or hbv-positive hcc at the pathway level  <cit> .
these results strongly suggest differences in biological behavior and consequent responses to treatment between hbv and hcv-positive hccs.
in the present study, we implemented the pathway-based supervised principal component analysis  with random permutations to identify recurrence-associated pathways of hbv-positive hcc.
comparison of our hbv-positive recurrence-associated pathways with those from two public datasets of hbv and hcv positive-hccs revealed significant overlap of pathways, despite limited common genes between the datasets.
the set of recurrence-associated pathways identified in this study effectively facilitated the stratification of hbv-positive hcc patients according to risk of recurrence.
our pathway-based approach should therefore provide clinically useful molecular insights into the mechanisms underlying hcc recurrence.
RESULTS
to determine the biological pathways associated with hcc prognosis, we established a genome-wide gene expression dataset via cdna microarray of  <dig> hcc cases positive for hbv.
a schematic diagram of the overall procedure for selection of pathways associated with recurrence is presented in figure  <dig> figure  <dig> schematic illustration of the analysis strategy.
the initial dataset was randomly divided into training and test sets.
in a training set, genes below the threshold p-value in a pathway were subjected to pca.
two models were constructed, specifically, one principal component model using the most significantly associated principal component and weighted model using multiple principal components, and evaluated in the test set.
this procedure was repeated  <dig>  times with random training and test sets.
finally, median values of statistics from test sets were measured to select significant pathways.
identification of genes related to hcc recurrence
initially, using the microarray dataset and long-term follow-up clinical data, we assessed the implications of gene expression on hcc recurrence.
in our cohort, median overall and recurrence free-survival times were  <dig>  and  <dig>  months, respectively.
as an initial attempt, we extracted a subset of  <dig> genes correlated with hcc recurrence  .
when these recurrence-associated genes were subjected to the unsupervised clustering method  <cit> , their hierarchical clustering led to the discrimination of hcc patients into low-risk and high-risk subclasses .
kaplan-meier plot of recurrence-free survival based on expression of the  <dig> recurrence-associated genes  showed significant discrimination of hcc patients according to risk, in which median recurrence-free survival rates of low  and high-risk  subgroups were  <dig>  and  <dig>  months , respectively.
upon further differentiation of these patients based on 2 years after surgery, defined as a cut-off for early recurrence  <cit> , those showing recurrence within the 2-year period were classified into the high-risk subgroup.figure  <dig> cluster analysis of recurrence-related genes.
dendrogram of clustering pattern measured from the matrix of  <dig> recurrence-associated genes  from our hbv-hcc.
samples were classified into two subgroups: low-risk and high-risk based on recurrence outcome.
the black bar indicates patients with early recurrence within 2 years after surgery.
columns represent individual samples, and rows genes.
red and green colors reflect high and low expression levels, respectively, as indicated by scale bars.
kaplan-meier plots for recurrence rates of the low- and high-risk subgroups.
p-values were obtained using the log-rank test.
overlap of recurrence-associated genes  among datasets.
to further evaluate the expression profiles for recurrence-free survival, we compared recurrence-associated genes of our current dataset with those from two public hcc datasets with available recurrence information, specifically, a hbv-positive hcc dataset   <cit>  and hcv-positive hcc dataset    <cit> .
in total,  <dig> and  <dig> genes from these two public datasets were selected, respectively .
comparison of the recurrence-related genes between datasets  disclosed a limited number of common genes .
only ten genes overlapped between our hbv-hcc and public hbv-hcc despite common viral etiology.
moreover, no common genes were identified among the three datasets.
indeed, the lack of common genes among different microarray datasets is the most significant problem in analysis of prognosis of tumors, including hcc  <cit> .
comparison of clinical distribution in hcc datasets
the distributions of clinical variables in the three datasets, including ours, are presented in additional file 2: table s <dig>  owing to the lack of specific variables, such as tumor differentiation and fibrosis, in the public hbv-hcc dataset, comparison of the datasets was difficult.
in addition, tumor stage  was different among datasets.
specifically, the proportion of advanced-stage tumors iii and iv in our hbv-hcc dataset was  <dig> %, compared to those of public hbv-hcc  and hcv-hcc datasets  .
however, the incidence of recurrence in our hbv-hcc dataset showed a biphasic pattern , similar to the public hbv-hcc dataset  <cit> .
specifically, the cumulative recurrence rate was  <dig> % per year during the first two years after surgical resection of tumor in our hbc-hcc, whereas the rate decreased to  <dig> % per year beyond two years after surgery .
in agreement with this result, the recurrence rate per month peaked during the first two years and persisted through the following years .
in addition, tumor differentiation and fibrosis stages were similar between our dataset and public hcv-hcc, indicating clear advantages in comparing expression patterns and pathways associated with recurrence.figure  <dig> recurrence rates in the hbv-hcc dataset.
cumulative recurrence rate of hcc over time.
recurrence rate of hcc per month over time.
identification of recurrence-associated pathways between different hcc datasets
to overcome the limitations of the gene-based approach, we applied a pathway-based semi-supervised pca approach.
following the overall analytical procedure depicted in figure  <dig>  we initially determined a threshold p-value that maximally generated significant pathways in each dataset.
based on plots showing changes in the number of significant pathways when the p-value is gradually increased, threshold p-value were determined as  <dig> ,  <dig>  and  <dig> , and among a total of  <dig> pathways, we subsequently obtained  <dig>   <dig> and  <dig> pathways of maximum significance  for our hbv-hcc, public hbv-hcc and public hcv-hcc datasets, respectively .
among the  <dig> significant pathways identified within our hbv-hcc dataset,  <dig>  and  <dig>  were in common with  <dig> and  <dig> significant pathways of public hbv-hcc and hcv-hcc datasets, respectively .
in contrast to pathway-based analysis showing significant overlap between the datasets, the gene-based approach revealed a low rate of overlap of significant genes  under conditions of the same threshold p-values .
moreover, the majority of significant genes from each dataset were not commonly distributed on the  <dig> overlapping pathways in all three datasets .figure  <dig> determination of threshold p-value and comparison of recurrence-related features among the three hcc datasets.
starting with gene of the lowest univariate cox p-value, permutation-based pathway analysis was applied by gradually increasing the p-value.
the maximum average number of pathways below p-value of  <dig>  in the test set was measured at threshold p-values of  <dig> ,  <dig>  and  <dig>  from  hbv-hcc,  public hbv-hcc and  public hcv-hcc, respectively, as indicated with arrows.
overlap of recurrence-associated pathways obtained at threshold p-values among datasets.
overlap of recurrence-associated genes at threshold p-values among datasets.
distribution of genes below the threshold p-value from each dataset in  <dig> common significant pathways.
comparison of the significant pathways extracted from one optimal pca method with those from weighted pca  disclosed similar results .
interestingly, signaling pathways related to cell proliferation and invasiveness, such as mitogen-activated protein kinase , erbb, g protein-coupled receptor , and membrane integrity-related pathways, such as those involving actin cytoskeleton, axon guidance and adhesion, were significantly associated with recurrence in our hbv-hcc dataset .
moreover, all significant pathways obtained from each dataset  were associated with early recurrence within two years, with area under curve  >  <dig> .
for example, in kaplan-meier plots and receiver operating characteristic  curves of the axon guidance pathway, one significant pathway common to all datasets clearly distinguished patients according to recurrence risk, with auc >  <dig>  .
subgroup classification via semi-supervised clustering of pathways
to utilize individual pathway information, sample scores obtained from optimal principal components of the  <dig> recurrence-associated pathways were hierarchically clustered using unsupervised clustering analysis of gene expression  <cit> , as depicted in figure  <dig>  figure 5a shows the resulting two subgroups of hcc patients with different outputs of recurrence .
similar to the gene expression-based approach, the pathway-based assay classified patients with early recurrence within 2 years after surgery predominantly in the high-risk, relative to the low-risk subgroup.
the robustness of the two subgroups was validated using leave-one-out cross-validation with six algorithms, including compound covariate, diagonal linear discriminant analysis, 1-nearest neighbor, 3-nearest neighbor, nearest centroid, and support vector machine.
the algorithms clearly divided the high- and low-risk subgroups with cross-validated misclassification error rate below  <dig>  and corresponding p-values <  <dig> , based on  <dig>  random permutations .
roc curve computed with compound covariate algorithm values from subgroup classification using the principal components matrix showed an auc value of  <dig>  for early recurrence with sensitivity and specificity of  <dig>  and  <dig> , respectively .
similarly,  <dig> and  <dig> recurrence-associated pathways obtained from the public hbv-hcc and hcv-hcc datasets, respectively, clearly discriminated high-risk and low-risk recurrence subgroups  .
roc curves additionally showed that these pathways are significantly associated with early recurrence in both datasets .
our findings indicate that combining the recurrence-associated pathways increases the statistical significance of patient classification according to recurrence risk.figure  <dig> cluster analysis of recurrence-related pathways.
dendrogram of the clustering pattern measured from the matrix of principal components of  <dig> recurrence-associated pathways  from hbv-hcc.
samples were classified into two subgroups: low-risk and high-risk.
the black bar indicates patients with early recurrence within 2 years after surgery.
columns represent individual samples, and rows pathways.
red and green colors reflect high and low levels of optimal principal component scores, respectively, as indicated by scale bars.
kaplan-meier plots for recurrence rates of low- and high-risk subgroups.
p-values were obtained using the log-rank test.
cross-validation of the low- and high-risk subgroups using six different algorithms: compound covariate , diagonal linear discriminant , 1-nearest neighbor , 3-nearest neighbor , nearest centroid  and support vector machine , implemented in brb arraytools.
roc curve computed with the compound covariate validation algorithm for subgroup classification.
dendrogram of the clustering pattern measured from the matrix of principal components of public hbv-hcc  or public hcv-hcc  on the  <dig> pathways associated with recurrence in our hbv-hcc dataset.
since our approach using optimal principal components was dependent on data structure, such as microarray platform and experimental conditions, we could not directly apply coefficients obtained from our microarray dataset to the other independent microarray datasets.
therefore, only pathway information from our hbv-hcc dataset was used to validate the effectiveness of pathway analysis in the public microarray datasets.
using our  <dig> recurrence-associated pathways, sample scores from the first, not optimal, principal component were measured and clustered in public hbv  and public hcv datasets .
as a result, two subgroups  were assigned, depending on the recurrence outcome.
as expected from the significant number of common pathways among the three datasets , these  <dig> pathways from our hbv-hcc dataset were also effective in discriminating patients of public microarray datasets according to risk .
clinical association of recurrence-related pathways
next, we examined the clinical implications of the recurrence-associated  <dig> pathways in our hbv-hcc dataset .
clinicopathological variations  associated with the high-risk subgroup classified according to pathway analysis included high levels of aspartate aminotransferase  and alanine aminotransferase , and presence of vascular invasion and lobular activity.
absence of capsule and narrow resection margin  were also marginally associated with the high-risk subgroup .
univariate analysis showed that the  <dig> pathways , together with clinicopathological parameters, including tumor stage , tumor number , tumor size , vascular invasion , lobular activity , child-pugh score , edmonson steiner’s grade  and capsule formation , are significantly or marginally associated with hcc recurrence in our present cohort.
multivariate analysis further led to the definition of these pathways  as an independent prognostic factor for recurrence together with clinical variables, including tumor number , tumor size  and edmondson steiner’s grade , in our hbv-positive hcc dataset.
in particular, focusing on tumor size among the variables , the recurrence-associated  <dig> pathways led to the extraction of a subgroup showing poor prognosis from patients with small tumors  in our hbv-hcc dataset .
similarly, recurrence-associated pathways in the public hbv-hcc dataset facilitated the identification of a subgroup of early recurrence in patients with small tumors  .
in addition, edmondson steiner’s grade  and tumor number , significant parameters in multivariate analysis , were effective in the extraction of a subgroup with early recurrence from patients with edmondson steiner’s grade i and ii or single nodular status.
these findings further validate the clinical utility of our pathway-based approach when combined with other clinical variables.table  <dig>
relationships between recurrence-associated pathways and clinicopathological variables in hbv-hcc
pathways-based subgroups
recurrence analysis
univariate analysis
variables
low risk
high risk
p-value
1
hazard ratio
2
log rank
p-value
3
<dig>
<dig>
<dig>
<dig>
<dig>
<dig>
<dig>
<dig>
<dig>
<dig>  × 10
−5
<dig>
<dig>  × 10
−7
multivariate analysis
<dig>  × 10
−7
<dig>
<dig>
<dig>
1
p-values were calculated using χ <dig> test.
2ci represents confidence interval.
3
p-values were calculated using the cox proportional hazards model.
4ast, aspartate aminotransferase; alt, alanine aminotransferase; afp, α-fetoprotein.
bold data indicate statistically significant values .
stratification of small tumor size patients by recurrence-associated pathways.
kaplan-meier plot for recurrence rates in two subgroups of patients based on tumor size  in our hbv-hcc dataset.
p-values were obtained using the log-rank test.
application of recurrence-associated pathways to patients with small tumors  , edmonson steiner’s grade   or single nodules  led to further stratification into two subgroups with different recurrence rates.
discussion
the identification of common biological features associated with hcc prognosis based on gene expression patterns that are interchangeable among different patient groups is a challenge, since shared significant prognostic genes among patient cohorts are extremely limited.
as expected, common significant recurrence-associated genes were rare among the hcc datasets examined in the present study, with only  <dig> % overlapping genes identified between the two hbv-positive hcc groups.
several factors may contribute to the observed rarity of common genes.
heterogeneity of patients in terms of clinicopathological characteristics as well as differences in microarray platforms and experimental conditions are among the main causes of inconsistent microarray results among datasets.
a gene set-based approach  was recently shown to partly overcome the heterogeneity of microarray analysis  <cit> .
the use of biologically related gene sets may be a more robust approach to suppress clinicopathological heterogeneity  <cit> .
data from the present study confirmed that the pathway-based approach is more beneficial than the gene-based approach in identifying common biological features in hcc recurrence.
for this purpose, we developed a pathway-based pca algorithm.
previously, pca using a subset of genes was reported to accurately predict clinical outcomes of patients  <cit> .
here, we used an optimal component, rather than the first or second principal component.
despite the rarity of common genes between the two hbv-positive hcc datasets, a large proportion  of the significant pathways overlapped.
interestingly,  <dig> pathways  were common among our hbv-positive hcc group and the two public hbv and hcv-positive hcc datasets, as shown in figure 4d .
our cohort exhibited a biphasic pattern in which the majority of recurrence occurred within the first two years, similar to the public hbv-hcc cohort  <cit> .
considering that recurrence is critically linked to survival in hcc, understanding the pathways associated with early recurrence is crucial for effective clinical management of the disease.
our pathway-based approach using optimal principal components was associated with early recurrence occurring within two years, with high sensitivity and specificity in roc curve analysis.
interestingly, patients with poor prognosis could be distinguished among those with small tumors using the present approach.
the finding that recurrence-associated pathways may be an independent variable for hcc recurrence along with tumor number, tumor size and edmondson’s grade supports the clinical utility of our method.
although the  <dig> pathways were common in both hbv- and hcv-hcc datasets, their clinical advantage in patient classification was not clear.
since these common pathways were not optimal, we obtained reduced performance in classification of patients.
in addition to its clinical value, the overlap of significant pathways between hbv- and hcv-positive datasets further suggests that recurrence of hbv- and hcv-positive hccs is commonly affected by dysfunction of common biological pathways .
notably, these pathways have already been reported in association with development or prognosis in diverse types of human cancers, including hcc.
for example, hemostasis and platelet-related pathways are critical in cancer development  <cit> .
signaling pathways, such as mapk and erbb, and immune-related pathways are involved in prognosis and tumorigenesis of hcc  <cit> .
g-protein pathways, including gpcr signaling, are de-regulated in diverse types of cancers and can thus be effectively used as prognostic markers  <cit> .
moreover, enrichment of immune response pathways  supports a close association of the high-risk subgroup with lobular activity, a clinical parameter for inflammation .
lobular activity status is related to not only these recurrence-associated pathways, but also a subset of recurrence-associated genes, as shown in additional file 9: figure s <dig>  under the condition where  <dig> genes were selected as associated with lobular activity ,  <dig>  were related to recurrence .
these gene sets exhibited differential expression profiles according to differences in lobular activity.
other diverse pathways, such as lysosome, endocytosis, and hemostasis, were additionally associated with recurrence.
although the functional involvement of these pathways in human cancer has not been fully elucidated, studies have shown that the lysosomal pathway is a novel target of cell death in cancer  <cit> .
moreover, lysosomal proteins, such as lysosome-associated membrane protein- <dig>  <cit>  and lysosomal cysteine proteases  <cit> , are associated with tumor development and progression.
the clathrin-mediated endocytosis pathway connected with the cytoskeleton has been shown to regulate functional changes in hcc  <cit> .
this association of diverse functional pathways clearly indicates that recurrence is a complex process involving changes in various biological functions.
in contrast to the common pathways discussed above, tight junction, focal adhesion and integrin pathways were more significantly associated with hbv-positive hcc.
these membrane mobility and intercellular interaction pathways are involved in hcc invasion, metastasis and angiogenesis  <cit> .
interestingly, sphingolipid metabolism was mainly associated with recurrence of hcv-positive hcc.
accumulating evidence suggests that sphingolipid regulates cell death in liver diseases, including hcc  <cit> .
previously, we reported functional differences and similarities in pathways implicated in the development of hcv- and/or hbv-positive hcc  <cit> .
by comparing these tumorigenic pathways with recurrence-associated pathways, we obtained those commonly involved in both processes in hbv-positive and hcv-positive hcc.
in the hbv-positive hcc dataset,  <dig> out of  <dig> recurrence-associated pathways  were associated with tumorigenesis, while  <dig> out of  <dig> recurrence-associated pathways  were involved in tumorigenesis in the public hcv-hcc dataset .
involvement of a relatively large proportion of recurrence pathways in tumorigenesis signifies that the biological processes associated with hepatocarcinogenesis also determine the prognostic process.
moreover, a number of pathways, including axon guidance, lysosome, actin cytoskeleton, and gpcr signaling, were shared between the hbv-hcc and hcv-hcc datasets , highlighting the possibility that a common process affects tumor development to prognosis, irrespective of the viral origin of hcc.
we further validated our pathway-based classification of hcc patients with those published employing prognostic genes for hcc.
survival-associated genes identified by lee et al.
<cit>  and hoshida et al.
<cit> , as well as recurrence-associated genes from the public hbv- and hcv-hcc datasets, led to grouping of patients into subclasses significantly associated with those obtained with the pathway-based approach using  <dig> pathways .
among the patients classified as high-risk based on our  <dig> recurrence-associated pathways, a significant number  belonged to the high-risk subclasses identified based on each of the four different survival or recurrence gene sets.
this comparative result further confirmed that our pathway-based classification of hcc prognosis is compatible with prior classification systems using survival- and recurrence-associated genes.
CONCLUSIONS
our pathway-based approach using optimal pca was more advantageous than the gene-based approach in several aspects, and revealed common prognostic pathways between hcc datasets of different patient cohorts, despite the rarity of common recurrence-associated genes.
identification of these shared pathways allowed the evaluation of functional similarities and differences between hbv- and hcv-positive hccs.
further studies are essential to validate the specific functions of these pathways in addition with hcc recurrence.

 various gene signatures related to the survival and recurrence of hepatocellular carcinoma have been identified using microarray analysis, and proposed to supplement clinicopathological prognostic factors for prediction of patient outcomes. in fact, prognostic genes are beneficial for the development of risk scores based on gene expression, that can overcome the limitations of clinical prognostic factors. interestingly, prognostic genes can be extracted from not only hcc, but also adjacent non-tumor liver tissues. however, the heterogeneous clinicopathological and biological nature of hccs makes it difficult to identify significant common genes that fit to different datasets. therefore, elucidation of the common biological functions related to prognosis that may be applicable to different datasets is also difficult. one way to overcome the limitations of the gene-based approach is the use of functionally related predefined gene sets, such as pathways, instead of individual genes. pathway analysis provides biological information that facilitates characterization of the functional network and the relationships between selected significant genes. a number of methods have been proposed to identify the pathways associated with prognosis. recent reports also indicate that rather than whole genes, a subset of genes correlated with sample phenotype is more accurate in predicting clinical outcomes of patients. among the diverse factors implicated in hcc development, hepatitis b and hepatitis c viruses are the major etiological risk factors. although hccs infected chronically with hbv and hcv are not distinguishable from histological and clinical evaluations, studies to date consistently imply that different molecular mechanisms underlie the development of hbv- and hcv-positive hccs. we additionally observed functional differences and similarities between the development of hcv- and/or hbv-positive hcc at the pathway level. these results strongly suggest differences in biological behavior and consequent responses to treatment between hbv and hcv-positive hccs. in the present study, we implemented the pathway-based supervised principal component analysis with random permutations to identify recurrence-associated pathways of hbv-positive hcc. comparison of our hbv-positive recurrence-associated pathways with those from two public datasets of hbv and hcv positive-hccs revealed significant overlap of pathways, despite limited common genes between the datasets. the set of recurrence-associated pathways identified in this study effectively facilitated the stratification of hbv-positive hcc patients according to risk of recurrence. our pathway-based approach should therefore provide clinically useful molecular insights into the mechanisms underlying hcc recurrence.  to determine the biological pathways associated with hcc prognosis, we established a genome-wide gene expression dataset via cdna microarray of hcc cases positive for hbv. a schematic diagram of the overall procedure for selection of pathways associated with recurrence is presented in figure  figure schematic illustration of the analysis strategy. the initial dataset was randomly divided into training and test sets. in a training set, genes below the threshold p-value in a pathway were subjected to pca. two models were constructed, specifically, one principal component model using the most significantly associated principal component and weighted model using multiple principal components, and evaluated in the test set. this procedure was repeated times with random training and test sets. finally, median values of statistics from test sets were measured to select significant pathways. identification of genes related to hcc recurrence initially, using the microarray dataset and long-term follow-up clinical data, we assessed the implications of gene expression on hcc recurrence. in our cohort, median overall and recurrence free-survival times were and  months, respectively. as an initial attempt, we extracted a subset of genes correlated with hcc recurrence. when these recurrence-associated genes were subjected to the unsupervised clustering method, their hierarchical clustering led to the discrimination of hcc patients into low-risk and high-risk subclasses. kaplan-meier plot of recurrence-free survival based on expression of the recurrence-associated genes showed significant discrimination of hcc patients according to risk, in which median recurrence-free survival rates of low and high-risk subgroups were and  months, respectively. upon further differentiation of these patients based on 2 years after surgery, defined as a cut-off for early recurrence, those showing recurrence within the 2-year period were classified into the high-risk subgroup.figure cluster analysis of recurrence-related genes. dendrogram of clustering pattern measured from the matrix of recurrence-associated genes from our hbv-hcc. samples were classified into two subgroups: low-risk and high-risk based on recurrence outcome. the black bar indicates patients with early recurrence within 2 years after surgery. columns represent individual samples, and rows genes. red and green colors reflect high and low expression levels, respectively, as indicated by scale bars. kaplan-meier plots for recurrence rates of the low- and high-risk subgroups. p-values were obtained using the log-rank test. overlap of recurrence-associated genes among datasets. to further evaluate the expression profiles for recurrence-free survival, we compared recurrence-associated genes of our current dataset with those from two public hcc datasets with available recurrence information, specifically, a hbv-positive hcc dataset and hcv-positive hcc dataset. in total, and genes from these two public datasets were selected, respectively. comparison of the recurrence-related genes between datasets disclosed a limited number of common genes. only ten genes overlapped between our hbv-hcc and public hbv-hcc despite common viral etiology. moreover, no common genes were identified among the three datasets. indeed, the lack of common genes among different microarray datasets is the most significant problem in analysis of prognosis of tumors, including hcc. comparison of clinical distribution in hcc datasets the distributions of clinical variables in the three datasets, including ours, are presented in additional file 2: table s owing to the lack of specific variables, such as tumor differentiation and fibrosis, in the public hbv-hcc dataset, comparison of the datasets was difficult. in addition, tumor stage was different among datasets. specifically, the proportion of advanced-stage tumors iii and iv in our hbv-hcc dataset was %, compared to those of public hbv-hcc and hcv-hcc datasets. however, the incidence of recurrence in our hbv-hcc dataset showed a biphasic pattern, similar to the public hbv-hcc dataset. specifically, the cumulative recurrence rate was % per year during the first two years after surgical resection of tumor in our hbc-hcc, whereas the rate decreased to % per year beyond two years after surgery. in agreement with this result, the recurrence rate per month peaked during the first two years and persisted through the following years. in addition, tumor differentiation and fibrosis stages were similar between our dataset and public hcv-hcc, indicating clear advantages in comparing expression patterns and pathways associated with recurrence.figure recurrence rates in the hbv-hcc dataset. cumulative recurrence rate of hcc over time. recurrence rate of hcc per month over time. identification of recurrence-associated pathways between different hcc datasets to overcome the limitations of the gene-based approach, we applied a pathway-based semi-supervised pca approach. following the overall analytical procedure depicted in figure  we initially determined a threshold p-value that maximally generated significant pathways in each dataset. based on plots showing changes in the number of significant pathways when the p-value is gradually increased, threshold p-value were determined as, and, and among a total of pathways, we subsequently obtained and pathways of maximum significance for our hbv-hcc, public hbv-hcc and public hcv-hcc datasets, respectively. among the significant pathways identified within our hbv-hcc dataset, and were in common with and significant pathways of public hbv-hcc and hcv-hcc datasets, respectively. in contrast to pathway-based analysis showing significant overlap between the datasets, the gene-based approach revealed a low rate of overlap of significant genes under conditions of the same threshold p-values. moreover, the majority of significant genes from each dataset were not commonly distributed on the overlapping pathways in all three datasets.figure determination of threshold p-value and comparison of recurrence-related features among the three hcc datasets. starting with gene of the lowest univariate cox p-value, permutation-based pathway analysis was applied by gradually increasing the p-value. the maximum average number of pathways below p-value of in the test set was measured at threshold p-values of, and from hbv-hcc, public hbv-hcc and public hcv-hcc, respectively, as indicated with arrows. overlap of recurrence-associated pathways obtained at threshold p-values among datasets. overlap of recurrence-associated genes at threshold p-values among datasets. distribution of genes below the threshold p-value from each dataset in common significant pathways. comparison of the significant pathways extracted from one optimal pca method with those from weighted pca disclosed similar results. interestingly, signaling pathways related to cell proliferation and invasiveness, such as mitogen-activated protein kinase, erbb, g protein-coupled receptor, and membrane integrity-related pathways, such as those involving actin cytoskeleton, axon guidance and adhesion, were significantly associated with recurrence in our hbv-hcc dataset. moreover, all significant pathways obtained from each dataset were associated with early recurrence within two years, with area under curve  > . for example, in kaplan-meier plots and receiver operating characteristic curves of the axon guidance pathway, one significant pathway common to all datasets clearly distinguished patients according to recurrence risk, with auc > . subgroup classification via semi-supervised clustering of pathways to utilize individual pathway information, sample scores obtained from optimal principal components of the recurrence-associated pathways were hierarchically clustered using unsupervised clustering analysis of gene expression, as depicted in figure  figure 5a shows the resulting two subgroups of hcc patients with different outputs of recurrence. similar to the gene expression-based approach, the pathway-based assay classified patients with early recurrence within 2 years after surgery predominantly in the high-risk, relative to the low-risk subgroup. the robustness of the two subgroups was validated using leave-one-out cross-validation with six algorithms, including compound covariate, diagonal linear discriminant analysis, 1-nearest neighbor, 3-nearest neighbor, nearest centroid, and support vector machine. the algorithms clearly divided the high- and low-risk subgroups with cross-validated misclassification error rate below and corresponding p-values < , based on random permutations. roc curve computed with compound covariate algorithm values from subgroup classification using the principal components matrix showed an auc value of for early recurrence with sensitivity and specificity of and, respectively. similarly, and recurrence-associated pathways obtained from the public hbv-hcc and hcv-hcc datasets, respectively, clearly discriminated high-risk and low-risk recurrence subgroups. roc curves additionally showed that these pathways are significantly associated with early recurrence in both datasets. our findings indicate that combining the recurrence-associated pathways increases the statistical significance of patient classification according to recurrence risk.figure cluster analysis of recurrence-related pathways. dendrogram of the clustering pattern measured from the matrix of principal components of recurrence-associated pathways from hbv-hcc. samples were classified into two subgroups: low-risk and high-risk. the black bar indicates patients with early recurrence within 2 years after surgery. columns represent individual samples, and rows pathways. red and green colors reflect high and low levels of optimal principal component scores, respectively, as indicated by scale bars. kaplan-meier plots for recurrence rates of low- and high-risk subgroups. p-values were obtained using the log-rank test. cross-validation of the low- and high-risk subgroups using six different algorithms: compound covariate, diagonal linear discriminant, 1-nearest neighbor, 3-nearest neighbor, nearest centroid and support vector machine, implemented in brb arraytools. roc curve computed with the compound covariate validation algorithm for subgroup classification. dendrogram of the clustering pattern measured from the matrix of principal components of public hbv-hcc or public hcv-hcc on the pathways associated with recurrence in our hbv-hcc dataset. since our approach using optimal principal components was dependent on data structure, such as microarray platform and experimental conditions, we could not directly apply coefficients obtained from our microarray dataset to the other independent microarray datasets. therefore, only pathway information from our hbv-hcc dataset was used to validate the effectiveness of pathway analysis in the public microarray datasets. using our recurrence-associated pathways, sample scores from the first, not optimal, principal component were measured and clustered in public hbv and public hcv datasets. as a result, two subgroups were assigned, depending on the recurrence outcome. as expected from the significant number of common pathways among the three datasets, these pathways from our hbv-hcc dataset were also effective in discriminating patients of public microarray datasets according to risk. clinical association of recurrence-related pathways next, we examined the clinical implications of the recurrence-associated pathways in our hbv-hcc dataset. clinicopathological variations associated with the high-risk subgroup classified according to pathway analysis included high levels of aspartate aminotransferase and alanine aminotransferase, and presence of vascular invasion and lobular activity. absence of capsule and narrow resection margin were also marginally associated with the high-risk subgroup. univariate analysis showed that the pathways, together with clinicopathological parameters, including tumor stage, tumor number, tumor size, vascular invasion, lobular activity, child-pugh score, edmonson steiner’s grade and capsule formation, are significantly or marginally associated with hcc recurrence in our present cohort. multivariate analysis further led to the definition of these pathways as an independent prognostic factor for recurrence together with clinical variables, including tumor number, tumor size and edmondson steiner’s grade, in our hbv-positive hcc dataset. in particular, focusing on tumor size among the variables, the recurrence-associated pathways led to the extraction of a subgroup showing poor prognosis from patients with small tumors in our hbv-hcc dataset. similarly, recurrence-associated pathways in the public hbv-hcc dataset facilitated the identification of a subgroup of early recurrence in patients with small tumors. in addition, edmondson steiner’s grade and tumor number, significant parameters in multivariate analysis, were effective in the extraction of a subgroup with early recurrence from patients with edmondson steiner’s grade i and ii or single nodular status. these findings further validate the clinical utility of our pathway-based approach when combined with other clinical variables.table relationships between recurrence-associated pathways and clinicopathological variables in hbv-hcc pathways-based subgroups recurrence analysis univariate analysis variables low risk high risk p-value 1 hazard ratio 2 log rank p-value 3  × 10 −5  × 10 −7 multivariate analysis  × 10 −7 1 p-values were calculated using χ test. 2ci represents confidence interval. 3 p-values were calculated using the cox proportional hazards model. 4ast, aspartate aminotransferase; alt, alanine aminotransferase; afp, α-fetoprotein. bold data indicate statistically significant values. stratification of small tumor size patients by recurrence-associated pathways. kaplan-meier plot for recurrence rates in two subgroups of patients based on tumor size in our hbv-hcc dataset. p-values were obtained using the log-rank test. application of recurrence-associated pathways to patients with small tumors, edmonson steiner’s grade or single nodules led to further stratification into two subgroups with different recurrence rates. discussion the identification of common biological features associated with hcc prognosis based on gene expression patterns that are interchangeable among different patient groups is a challenge, since shared significant prognostic genes among patient cohorts are extremely limited. as expected, common significant recurrence-associated genes were rare among the hcc datasets examined in the present study, with only % overlapping genes identified between the two hbv-positive hcc groups. several factors may contribute to the observed rarity of common genes. heterogeneity of patients in terms of clinicopathological characteristics as well as differences in microarray platforms and experimental conditions are among the main causes of inconsistent microarray results among datasets. a gene set-based approach was recently shown to partly overcome the heterogeneity of microarray analysis. the use of biologically related gene sets may be a more robust approach to suppress clinicopathological heterogeneity. data from the present study confirmed that the pathway-based approach is more beneficial than the gene-based approach in identifying common biological features in hcc recurrence. for this purpose, we developed a pathway-based pca algorithm. previously, pca using a subset of genes was reported to accurately predict clinical outcomes of patients. here, we used an optimal component, rather than the first or second principal component. despite the rarity of common genes between the two hbv-positive hcc datasets, a large proportion of the significant pathways overlapped. interestingly, pathways were common among our hbv-positive hcc group and the two public hbv and hcv-positive hcc datasets, as shown in figure 4d. our cohort exhibited a biphasic pattern in which the majority of recurrence occurred within the first two years, similar to the public hbv-hcc cohort. considering that recurrence is critically linked to survival in hcc, understanding the pathways associated with early recurrence is crucial for effective clinical management of the disease. our pathway-based approach using optimal principal components was associated with early recurrence occurring within two years, with high sensitivity and specificity in roc curve analysis. interestingly, patients with poor prognosis could be distinguished among those with small tumors using the present approach. the finding that recurrence-associated pathways may be an independent variable for hcc recurrence along with tumor number, tumor size and edmondson’s grade supports the clinical utility of our method. although the pathways were common in both hbv- and hcv-hcc datasets, their clinical advantage in patient classification was not clear. since these common pathways were not optimal, we obtained reduced performance in classification of patients. in addition to its clinical value, the overlap of significant pathways between hbv- and hcv-positive datasets further suggests that recurrence of hbv- and hcv-positive hccs is commonly affected by dysfunction of common biological pathways. notably, these pathways have already been reported in association with development or prognosis in diverse types of human cancers, including hcc. for example, hemostasis and platelet-related pathways are critical in cancer development. signaling pathways, such as mapk and erbb, and immune-related pathways are involved in prognosis and tumorigenesis of hcc. g-protein pathways, including gpcr signaling, are de-regulated in diverse types of cancers and can thus be effectively used as prognostic markers. moreover, enrichment of immune response pathways supports a close association of the high-risk subgroup with lobular activity, a clinical parameter for inflammation. lobular activity status is related to not only these recurrence-associated pathways, but also a subset of recurrence-associated genes, as shown in additional file 9: figure s under the condition where genes were selected as associated with lobular activity, were related to recurrence. these gene sets exhibited differential expression profiles according to differences in lobular activity. other diverse pathways, such as lysosome, endocytosis, and hemostasis, were additionally associated with recurrence. although the functional involvement of these pathways in human cancer has not been fully elucidated, studies have shown that the lysosomal pathway is a novel target of cell death in cancer. moreover, lysosomal proteins, such as lysosome-associated membrane protein- and lysosomal cysteine proteases, are associated with tumor development and progression. the clathrin-mediated endocytosis pathway connected with the cytoskeleton has been shown to regulate functional changes in hcc. this association of diverse functional pathways clearly indicates that recurrence is a complex process involving changes in various biological functions. in contrast to the common pathways discussed above, tight junction, focal adhesion and integrin pathways were more significantly associated with hbv-positive hcc. these membrane mobility and intercellular interaction pathways are involved in hcc invasion, metastasis and angiogenesis. interestingly, sphingolipid metabolism was mainly associated with recurrence of hcv-positive hcc. accumulating evidence suggests that sphingolipid regulates cell death in liver diseases, including hcc. previously, we reported functional differences and similarities in pathways implicated in the development of hcv- and/or hbv-positive hcc. by comparing these tumorigenic pathways with recurrence-associated pathways, we obtained those commonly involved in both processes in hbv-positive and hcv-positive hcc. in the hbv-positive hcc dataset, out of recurrence-associated pathways were associated with tumorigenesis, while out of recurrence-associated pathways were involved in tumorigenesis in the public hcv-hcc dataset. involvement of a relatively large proportion of recurrence pathways in tumorigenesis signifies that the biological processes associated with hepatocarcinogenesis also determine the prognostic process. moreover, a number of pathways, including axon guidance, lysosome, actin cytoskeleton, and gpcr signaling, were shared between the hbv-hcc and hcv-hcc datasets, highlighting the possibility that a common process affects tumor development to prognosis, irrespective of the viral origin of hcc. we further validated our pathway-based classification of hcc patients with those published employing prognostic genes for hcc. survival-associated genes identified by lee et al. and hoshida et al., as well as recurrence-associated genes from the public hbv- and hcv-hcc datasets, led to grouping of patients into subclasses significantly associated with those obtained with the pathway-based approach using pathways. among the patients classified as high-risk based on our recurrence-associated pathways, a significant number belonged to the high-risk subclasses identified based on each of the four different survival or recurrence gene sets. this comparative result further confirmed that our pathway-based classification of hcc prognosis is compatible with prior classification systems using survival- and recurrence-associated genes.  our pathway-based approach using optimal pca was more advantageous than the gene-based approach in several aspects, and revealed common prognostic pathways between hcc datasets of different patient cohorts, despite the rarity of common recurrence-associated genes. identification of these shared pathways allowed the evaluation of functional similarities and differences between hbv- and hcv-positive hccs. further studies are essential to validate the specific functions of these pathways in addition with hcc recurrence. 
moreover, the pathway-based approach had a clinical advantage in terms of discriminating the high-risk subgroup among patients with small hcc. in relation to the public hbv- and public hepatitis c virus -positive hcc datasets, we detected and common recurrence-associated pathways, respectively. through multivariate analysis, these recurrence-associated pathways were identified as an independent prognostic factor along with tumor number, tumor size and edmondson’s grade. the pathways identified were additionally successfully applied to discriminate subgroups depending on recurrence risk within the public hcc datasets. to overcome the limitations of individual gene-based analysis, we applied a pathway-based approach for analysis of hcc recurrence. by implementing a permutation-based semi-supervised principal component analysis algorithm using the optimal principal component, we selected sixty-four pathways associated with hepatitis b virus -positive hcc recurrence, from our microarray dataset composed of hbv-positive hccs. non-supervised clustering of the recurrence-associated pathways facilitated the classification of hcc patients into high- and low-risk subgroups, based on risk of recurrence. using pathway-based analysis, we successfully identified the pathways involved in recurrence of hbv-positive hcc that may be effectively used as prognostic markers. despite the recent identification of several prognostic gene signatures, the lack of common genes among experimental cohorts has posed a considerable challenge in uncovering the molecular basis underlying hepatocellular carcinoma recurrence for application in clinical purposes. however, overlap of recurrence-associated genes between datasets was rare, further supporting the utility of the pathway-based approach for recurrence analysis between different hcc datasets.
 despite the recent identification of several prognostic gene signatures, the lack of common genes among experimental cohorts has posed a considerable challenge in uncovering the molecular basis underlying hepatocellular carcinoma recurrence for application in clinical purposes. to overcome the limitations of individual gene-based analysis, we applied a pathway-based approach for analysis of hcc recurrence.  by implementing a permutation-based semi-supervised principal component analysis algorithm using the optimal principal component, we selected sixty-four pathways associated with hepatitis b virus -positive hcc recurrence, from our microarray dataset composed of hbv-positive hccs. in relation to the public hbv- and public hepatitis c virus -positive hcc datasets, we detected and common recurrence-associated pathways, respectively. however, overlap of recurrence-associated genes between datasets was rare, further supporting the utility of the pathway-based approach for recurrence analysis between different hcc datasets. non-supervised clustering of the recurrence-associated pathways facilitated the classification of hcc patients into high- and low-risk subgroups, based on risk of recurrence. the pathways identified were additionally successfully applied to discriminate subgroups depending on recurrence risk within the public hcc datasets. through multivariate analysis, these recurrence-associated pathways were identified as an independent prognostic factor along with tumor number, tumor size and edmondson’s grade. moreover, the pathway-based approach had a clinical advantage in terms of discriminating the high-risk subgroup among patients with small hcc.  using pathway-based analysis, we successfully identified the pathways involved in recurrence of hbv-positive hcc that may be effectively used as prognostic markers. electronic supplementary material the online version of this article contains supplementary material, which is available to authorized users. keywords recurrence-associated pathwayhepatocellular carcinomaprincipal component analysisprognosisrisksmall tumorissue-copyright-statement© the author 2015 