BACKGROUND
soft tissue sarcomas are a group of highly heterogeneous tumors that exhibit a diverse spectrum of mesenchymal differentiations.
however, the molecular dissection of tumor heterogeneity has been hampered by the relatively low incidence of these tumors; approximately  <dig>  cases are reported annually in japan.
significant differences were observed in the five-year survival rates among the subtypes of sts, e.g., 100% for well-differentiated liposarcoma , 71% for synovial sarcoma , 46% for pleomorphic malignant fibrous histiocytoma , and 92% for myxofibrosarcoma .
the primary objective of this study was to identify a set of marker genes that facilitates accurate differential diagnosis of the sarcoma subtypes.
discrimination between mfh and mfs, for example, is particularly difficult because there is a histological overlap between the two.
information on such subtype-specific genes may also help in understanding the molecular pathways that are activated in each subtype of the different biological malignancies.
recent advances in dna microarray analysis have enabled the simultaneous evaluation of the expression levels of several tens of thousands of genes, thereby offering a rich source of information that is potentially useful in the diagnosis and prognosis of diseases  <cit> .
there are two main methods of expression data analyses: unsupervised learning methods and supervised learning methods.
the unsupervised learning methods, e.g., hierarchical clustering  <cit>  and fuzzy adaptive resonance theory   <cit> , are designed to identify previously unrecognized classes of disease based on their expression pattern; the biological significance of such disease subtypes, such as prognosis, is then assessed.
in contrast, the supervised learning methods use training sets to specify the genes that should be clustered together  <cit> .
however, to conduct either unsupervised or supervised analysis, it is necessary to select genes that have a strong correlation with the target phenotype, such as disease diagnosis or prognosis.
this is because the performance of classification analysis can decline if a large number of genes as predictor variables are incorporated in the model.
gene selection has been performed to screen candidate genes for modeling.
there are two types of approaches – the wrapper approach and the filtering approach.
in the former approach, genes are selected as a part of mining algorithms, such as k-nearest neighbor , multiple regression analysis , weighted voting   <cit> , support vector machines   <cit> , fuzzy neural network  combined with sweep operator  method  <cit> , and boosted fuzzy classifier with sweep operator  method  <cit> .
on the other hand, in the latter approach, prior to the application of the mining algorithms, genes are selected by filtering methods, such as the mann-whitney u test, student's t-test , welch's t-test , the signal-to-noise statistic   <cit> , significance analysis of microarrays   <cit> , nearest shrunken centroids   <cit> , and the projective adaptive resonance theory  filtering method  <cit> .
in a previous study, we developed the part filtering method by modifying part  <cit> , and reported that part exhibited a higher performance than conventional methods, such as s2n and nsc  <cit> .
the combination method of part and bfcs  was developed and applied to gene expression data, such as lymphoma  <cit>  and esophageal cancer  <cit> .
in the present study, we applied the various filtering methods to the gene expression profile data for the sts subtypes and constructed svm models using the filtered genes.
the results showed that the accuracy of the model based on the genes filtered by part was the highest.
in addition, various wrapper methods were applied to the genes that were filtered by the different filtering methods to extract essential genes for diagnosis.
the models of the part-bfcs method among various combinations of filtering and wrapper methods showed the highest accuracy, and  <dig> independent probes were extracted using this method.
seven genes among the  <dig> probes that were frequently selected by this method are known prognostic marker genes for other tumors.
these genes are candidate marker genes for sts.
correlation analysis was performed for the  <dig> genes to extract the subtype-specific genes that were not selected by part-bfcs.
sixteen genes among those extracted are also known prognostic marker genes for other tumors, and these could be candidate marker genes for sts.
RESULTS
clustering analysis for unfiltered data
hierarchical clustering was applied to  <dig> patients and  <dig>  unfiltered probes, as shown in fig.
<dig>  figure  <dig> shows that patients were separated into three clusters – two mfh clusters and a single mfs cluster.
however, there were seven mfs patients in the mfh clusters and three mfh patients in the mfs cluster that were misclassified by the clustering.
on the basis of these results, various filtering and wrapper methods were performed for a more accurate separation of these patients.
construction of svm models by using filtered genes
to eliminate nonspecific genes for discriminating between mfh and mfs, various filtering methods, such as the u test, sttest, wttest, s2n, sam, nsc, and part were applied to the modeling data set comprising  <dig> patients and  <dig>  probes; the performances were evaluated by using prediction accuracies for the blind data.
the top  <dig>  genes selected by each filtering method were used to construct the svm models.
the blind accuracies of models for each method are shown in table  <dig>  table  <dig> shows that the accuracy of the svm model using genes filtered by part, which was  <dig> %, was the best in this study.
the accuracies of models using s2n or sam  were the second highest.
on the other hand, the accuracy of the svm model without filtering was  <dig> %, which was the lowest.
average accuracy of the models with random selection was also  <dig> %.
these results suggest that when constructing diagnostic models, it is necessary to incorporate a filtering step; further, in this study, the part filtering method was found to give the most accurate predictions.
application of various combinations of filtering and wrapper methods
to extract essential subtype-specific genes for differentiation between mfh and mfs, various wrapper methods such as knn, mra, wv, svm, fnn-sweep, and bfcs were applied to the modeling data set comprising  <dig> patients and  <dig>  probes filtered by each filtering method; the performances were evaluated by using the prediction accuracies of the blind data.
the genes selected by each wrapper method were used in the models, and numbers of inputs were optimized by cross-validation of the modeling data set.
the blind accuracies were calculated by using ten combination models that were constructed by pim, as shown in table  <dig>  table  <dig> shows that the average accuracy of part-bfcs was  <dig> %, which was the highest.
there was a total of  <dig> probes in ten combinations of 8-input models.
some probes were selected several times.
among  <dig> probes,  <dig> were independent.
the average accuracies of the sam-knn and part-svm methods, at  <dig> % and  <dig> %, were the second and third best, respectively.
these results imply that the combination of part and bfcs is the most accurate method for extraction of essential subtype-specific genes for sts.
clustering analysis using genes extracted by part-bfcs
hierarchical clustering was applied to  <dig> patients and  <dig> probes selected by part-bfcs, as shown in fig.
<dig>  figure  <dig> shows that patients were separated into two clusters – an mfh cluster and an mfs cluster.
the results show that there was a single mfs patient in the mfh cluster and three mfh patients in the mfs cluster.
these observations suggest that misclassification of samples was reduced using the genes that were extracted by the part-bfcs method and that essential genes could be extracted for the diagnosis of sts subtypes.
extraction of marker gene candidates by the correlation analysis
to extract the marker gene candidates unextracted by part-bfcs, the correlation analysis was applied to sts data.
twenty-eight probes were extracted by part-bfcs.
fifteen probes among  <dig> ones were selected two times or more.
as shown in table  <dig>  a total of  <dig> probes, comprising the top  <dig> probes having high correlation with the  <dig> probes, were extracted as marker gene candidates.
some probes were selected several times.
thus, these probes comprised  <dig> independent probes, which correspond to  <dig> independent genes.
the performance of the  <dig> probes was confirmed by hierarchical clustering, as shown in fig.
<dig>  figure  <dig> shows that patients were separated into two clusters – an mfh cluster and an mfs cluster.
the results show that there was two mfs patient in the mfh cluster and four mfh patients in the mfs cluster.
this result was almost the same as figure  <dig>  this is, the genes that have high performances, were extracted by using correlation analysis.
characteristics of the genes selected for the classification models and the genes highly correlated with them
significant differences were observed in the five-year survival rates between mfh and mfs.
thus, it was expected that prognostic marker genes would be extracted for the discrimination of mfh and mfs.
we investigated the presence of previously reported prognostic marker genes among the  <dig> probes  selected frequently by part-bfcs among the  <dig> probes.
furthermore,  <dig> probes which correspond to  <dig> independent genes, were investigated.
with regard to the genes selected directly by part-bfcs, seven genes among the  <dig> genes are reported to be prognostic markers for other tumors.
mif promotes tumor invasion and metastasis via the rho dependent pathway, reported by sun et al.
<cit> .
cyfip <dig>  is one of the genes downregulated by p <dig>  reported by ceballos et al.
<cit> .
p <dig> is a well-known type of tumor suppressor gene.
hspcb plays an important role in the assembly/disassembly of tubulin by inhibiting tubulin polymerization, reported by man et al.
<cit> .
tubulin is a simple and useful predictive marker for the clinical response to chemotherapy in gastric cancer, reported by urano et al.
<cit> .
reduced expression of timp <dig> in esophageal adenocarcinoma is associated with increased tumour invasiveness and reduced patient survival, reported by darnton et al.
<cit> .
ldha is a hypoxia-inducible gene and is associated with considerably poorer overall survival, reported by chi et al.
<cit> .
abr is a regulator of the rho gtp-binding protein family, reported by chuang et al.
<cit> .
the rho pathway is associated with tumor invasion and metastasis, reported by sun et al.
<cit> .
rgs <dig> is associated with tumor metastasis, reported by tatenhorst et al.
<cit> .
these findings suggest that the genes extracted by the part-bfcs method are new marker genes for the sts subtypes.
with regard to the genes selected by correlation analysis, sixteen genes among  <dig> genes are reported to be prognostic markers for other tumors.
the add <dig> protein  belongs to a family of ubiquitously expressed membrane-skeletal proteins that are localized at spectrin-actin junctions, reported by van den boom et al.
<cit> .
in renal carcinomas, changes in adducin expression, phosphorylation state, and localization were found to be associated with increased malignancy.
in addition, the down-regulation of adducin-γ expression is correlated with increased migratory activity of human glioma cells in vitro.
the expression of col11a <dig> in colorectal tumors could be associated with the apc/β-catenin pathway in familial adenomatous polyposis  and sporadic colorectal cancer, reported by fischer et al.
<cit> .
nuclear accumulation of the beta-catenin protein is associated with activation of the wnt/wg signaling pathway.
beta-catenin status predicts a favorable outcome in childhood medulloblastoma, reported by ellison et al.
<cit> .
smad <dig> is a component of the transforming growth factor-beta , which is a potent regulator of growth, apoptosis, and invasiveness of tumor cells, such as breast cancer cells, reported by dubrovska et al.
<cit> .
tgfβ1/smad <dig> suppresses brca1-dependent dna repair in response to dna damaging agents.
gas <dig>  a growth arrest-specific gene, is the partner gene of mll in treatment-related acute myeloid leukemia.
mll gene translocations can be present early during anticancer treatment at low cumulative doses of dna topoisomerase ii inhibitors, reported by megonigal et al.
<cit> .
cd <dig>  expression is associated with disease activity in multiple myeloma, reported by barille et al.
<cit> .
mmp <dig> expression is correlated significantly with the evolution of lymph node status and tumor-lymph node-metastasis  stage, reported by gouyer et al.
<cit> .
expression of mmp <dig> and mmp <dig> is positively associated with poor tumor cell differentiation, vessel permeation, and lymph node metastasis, reported by gu et al.
<cit> .
mmp <dig>  is associated with lymph node involvement and tumor progression, reported by soni et al.
<cit> .
tssc <dig> is one of the genes related to apoptosis, reported by muller et al.
<cit> .
hspb <dig>  is implicated in resistance to chemotherapy in breast cancer, and also predicts a poor response to chemotherapy in leukemia patients, reported by ciocca and calderwood  <cit> .
hsp105b is an alternatively spliced form of hsp105a, reported by yamagishi et al.
<cit> .
hsp105a prevents stress-induced apoptosis in neuronal pc <dig> cells, and it is a novel anti-apoptotic neuroprotective factor in the mammalian brain.
an anti-icam <dig> monoclonal antibody induces immune-mediated regressions of icam2-negative colon carcinomas, reported by melero et al.
<cit> .
hspd <dig> is downregulated during early apoptosis of hepatoma cells, reported by lee et al.
<cit> .
wnt10b is a member of the wnt signaling molecules, which are potent targets for the diagnosis of cancer  as well as for the prevention and treatment of cancer, reported by kirikoshi and katoh <cit> .
tek is correlated with a higher risk of metastases in node-negative patients, reported by dales et al.
<cit> .
thus, correlation analysis was performed to extract the subtype-specific genes that were not selected by part-bfcs.
these findings suggest that the genes having a high correlation with those extracted by the part-bfcs method could also be new marker genes for the sts subtypes, and that this fact gives greater confidence in the accuracy of these potential maker genes selected directly by part-bfcs.
CONCLUSIONS
in this study, we applied the part filtering method to sts gene expression profiling data to construct subtype predictors for diagnosis.
the results showed that the genes selected by part exhibited higher prediction accuracy for sts than the other methods assessed.
the genes selected by part-bfcs such as mif, cyfip <dig>  hspcb, timp <dig>  ldha, abr, and rgs <dig> can be used as targets for molecular diagnosis and treatment.
in addition, the new candidate marker genes that were not extracted directly by part-bfcs, could be extracted by correlation analysis.
we believe that this procedure, the part filtering method, should be considered as one of the candidate analytical procedures in various class prediction problems in clinical and basic oncology using transcriptome data.

 soft tissue sarcomas are a group of highly heterogeneous tumors that exhibit a diverse spectrum of mesenchymal differentiations. however, the molecular dissection of tumor heterogeneity has been hampered by the relatively low incidence of these tumors; approximately cases are reported annually in japan. significant differences were observed in the five-year survival rates among the subtypes of sts, e.g., 100% for well-differentiated liposarcoma, 71% for synovial sarcoma, 46% for pleomorphic malignant fibrous histiocytoma, and 92% for myxofibrosarcoma. the primary objective of this study was to identify a set of marker genes that facilitates accurate differential diagnosis of the sarcoma subtypes. discrimination between mfh and mfs, for example, is particularly difficult because there is a histological overlap between the two. information on such subtype-specific genes may also help in understanding the molecular pathways that are activated in each subtype of the different biological malignancies. recent advances in dna microarray analysis have enabled the simultaneous evaluation of the expression levels of several tens of thousands of genes, thereby offering a rich source of information that is potentially useful in the diagnosis and prognosis of diseases. there are two main methods of expression data analyses: unsupervised learning methods and supervised learning methods. the unsupervised learning methods, e.g., hierarchical clustering and fuzzy adaptive resonance theory, are designed to identify previously unrecognized classes of disease based on their expression pattern; the biological significance of such disease subtypes, such as prognosis, is then assessed. in contrast, the supervised learning methods use training sets to specify the genes that should be clustered together. however, to conduct either unsupervised or supervised analysis, it is necessary to select genes that have a strong correlation with the target phenotype, such as disease diagnosis or prognosis. this is because the performance of classification analysis can decline if a large number of genes as predictor variables are incorporated in the model. gene selection has been performed to screen candidate genes for modeling. there are two types of approaches – the wrapper approach and the filtering approach. in the former approach, genes are selected as a part of mining algorithms, such as k-nearest neighbor, multiple regression analysis, weighted voting, support vector machines, fuzzy neural network combined with sweep operator method, and boosted fuzzy classifier with sweep operator method. on the other hand, in the latter approach, prior to the application of the mining algorithms, genes are selected by filtering methods, such as the mann-whitney u test, student's t-test, welch's t-test, the signal-to-noise statistic, significance analysis of microarrays, nearest shrunken centroids, and the projective adaptive resonance theory filtering method. in a previous study, we developed the part filtering method by modifying part, and reported that part exhibited a higher performance than conventional methods, such as s2n and nsc. the combination method of part and bfcs was developed and applied to gene expression data, such as lymphoma and esophageal cancer. in the present study, we applied the various filtering methods to the gene expression profile data for the sts subtypes and constructed svm models using the filtered genes. the results showed that the accuracy of the model based on the genes filtered by part was the highest. in addition, various wrapper methods were applied to the genes that were filtered by the different filtering methods to extract essential genes for diagnosis. the models of the part-bfcs method among various combinations of filtering and wrapper methods showed the highest accuracy, and independent probes were extracted using this method. seven genes among the probes that were frequently selected by this method are known prognostic marker genes for other tumors. these genes are candidate marker genes for sts. correlation analysis was performed for the genes to extract the subtype-specific genes that were not selected by part-bfcs. sixteen genes among those extracted are also known prognostic marker genes for other tumors, and these could be candidate marker genes for sts.  clustering analysis for unfiltered data hierarchical clustering was applied to patients and unfiltered probes, as shown in fig. figure shows that patients were separated into three clusters – two mfh clusters and a single mfs cluster. however, there were seven mfs patients in the mfh clusters and three mfh patients in the mfs cluster that were misclassified by the clustering. on the basis of these results, various filtering and wrapper methods were performed for a more accurate separation of these patients. construction of svm models by using filtered genes to eliminate nonspecific genes for discriminating between mfh and mfs, various filtering methods, such as the u test, sttest, wttest, s2n, sam, nsc, and part were applied to the modeling data set comprising patients and probes; the performances were evaluated by using prediction accuracies for the blind data. the top genes selected by each filtering method were used to construct the svm models. the blind accuracies of models for each method are shown in table table shows that the accuracy of the svm model using genes filtered by part, which was %, was the best in this study. the accuracies of models using s2n or sam were the second highest. on the other hand, the accuracy of the svm model without filtering was %, which was the lowest. average accuracy of the models with random selection was also %. these results suggest that when constructing diagnostic models, it is necessary to incorporate a filtering step; further, in this study, the part filtering method was found to give the most accurate predictions. application of various combinations of filtering and wrapper methods to extract essential subtype-specific genes for differentiation between mfh and mfs, various wrapper methods such as knn, mra, wv, svm, fnn-sweep, and bfcs were applied to the modeling data set comprising patients and probes filtered by each filtering method; the performances were evaluated by using the prediction accuracies of the blind data. the genes selected by each wrapper method were used in the models, and numbers of inputs were optimized by cross-validation of the modeling data set. the blind accuracies were calculated by using ten combination models that were constructed by pim, as shown in table table shows that the average accuracy of part-bfcs was %, which was the highest. there was a total of probes in ten combinations of 8-input models. some probes were selected several times. among probes, were independent. the average accuracies of the sam-knn and part-svm methods, at % and %, were the second and third best, respectively. these results imply that the combination of part and bfcs is the most accurate method for extraction of essential subtype-specific genes for sts. clustering analysis using genes extracted by part-bfcs hierarchical clustering was applied to patients and probes selected by part-bfcs, as shown in fig. figure shows that patients were separated into two clusters – an mfh cluster and an mfs cluster. the results show that there was a single mfs patient in the mfh cluster and three mfh patients in the mfs cluster. these observations suggest that misclassification of samples was reduced using the genes that were extracted by the part-bfcs method and that essential genes could be extracted for the diagnosis of sts subtypes. extraction of marker gene candidates by the correlation analysis to extract the marker gene candidates unextracted by part-bfcs, the correlation analysis was applied to sts data. twenty-eight probes were extracted by part-bfcs. fifteen probes among ones were selected two times or more. as shown in table a total of probes, comprising the top probes having high correlation with the probes, were extracted as marker gene candidates. some probes were selected several times. thus, these probes comprised independent probes, which correspond to independent genes. the performance of the probes was confirmed by hierarchical clustering, as shown in fig. figure shows that patients were separated into two clusters – an mfh cluster and an mfs cluster. the results show that there was two mfs patient in the mfh cluster and four mfh patients in the mfs cluster. this result was almost the same as figure this is, the genes that have high performances, were extracted by using correlation analysis. characteristics of the genes selected for the classification models and the genes highly correlated with them significant differences were observed in the five-year survival rates between mfh and mfs. thus, it was expected that prognostic marker genes would be extracted for the discrimination of mfh and mfs. we investigated the presence of previously reported prognostic marker genes among the probes selected frequently by part-bfcs among the probes. furthermore, probes which correspond to independent genes, were investigated. with regard to the genes selected directly by part-bfcs, seven genes among the genes are reported to be prognostic markers for other tumors. mif promotes tumor invasion and metastasis via the rho dependent pathway, reported by sun et al.. cyfip is one of the genes downregulated by p reported by ceballos et al.. p is a well-known type of tumor suppressor gene. hspcb plays an important role in the assembly/disassembly of tubulin by inhibiting tubulin polymerization, reported by man et al.. tubulin is a simple and useful predictive marker for the clinical response to chemotherapy in gastric cancer, reported by urano et al.. reduced expression of timp in esophageal adenocarcinoma is associated with increased tumour invasiveness and reduced patient survival, reported by darnton et al.. ldha is a hypoxia-inducible gene and is associated with considerably poorer overall survival, reported by chi et al.. abr is a regulator of the rho gtp-binding protein family, reported by chuang et al.. the rho pathway is associated with tumor invasion and metastasis, reported by sun et al.. rgs is associated with tumor metastasis, reported by tatenhorst et al.. these findings suggest that the genes extracted by the part-bfcs method are new marker genes for the sts subtypes. with regard to the genes selected by correlation analysis, sixteen genes among genes are reported to be prognostic markers for other tumors. the add protein belongs to a family of ubiquitously expressed membrane-skeletal proteins that are localized at spectrin-actin junctions, reported by van den boom et al.. in renal carcinomas, changes in adducin expression, phosphorylation state, and localization were found to be associated with increased malignancy. in addition, the down-regulation of adducin-γ expression is correlated with increased migratory activity of human glioma cells in vitro. the expression of col11a in colorectal tumors could be associated with the apc/β-catenin pathway in familial adenomatous polyposis and sporadic colorectal cancer, reported by fischer et al.. nuclear accumulation of the beta-catenin protein is associated with activation of the wnt/wg signaling pathway. beta-catenin status predicts a favorable outcome in childhood medulloblastoma, reported by ellison et al.. smad is a component of the transforming growth factor-beta, which is a potent regulator of growth, apoptosis, and invasiveness of tumor cells, such as breast cancer cells, reported by dubrovska et al.. tgfβ1/smad suppresses brca1-dependent dna repair in response to dna damaging agents. gas a growth arrest-specific gene, is the partner gene of mll in treatment-related acute myeloid leukemia. mll gene translocations can be present early during anticancer treatment at low cumulative doses of dna topoisomerase ii inhibitors, reported by megonigal et al.. cd expression is associated with disease activity in multiple myeloma, reported by barille et al.. mmp expression is correlated significantly with the evolution of lymph node status and tumor-lymph node-metastasis stage, reported by gouyer et al.. expression of mmp and mmp is positively associated with poor tumor cell differentiation, vessel permeation, and lymph node metastasis, reported by gu et al.. mmp is associated with lymph node involvement and tumor progression, reported by soni et al.. tssc is one of the genes related to apoptosis, reported by muller et al.. hspb is implicated in resistance to chemotherapy in breast cancer, and also predicts a poor response to chemotherapy in leukemia patients, reported by ciocca and calderwood. hsp105b is an alternatively spliced form of hsp105a, reported by yamagishi et al.. hsp105a prevents stress-induced apoptosis in neuronal pc cells, and it is a novel anti-apoptotic neuroprotective factor in the mammalian brain. an anti-icam monoclonal antibody induces immune-mediated regressions of icam2-negative colon carcinomas, reported by melero et al.. hspd is downregulated during early apoptosis of hepatoma cells, reported by lee et al.. wnt10b is a member of the wnt signaling molecules, which are potent targets for the diagnosis of cancer as well as for the prevention and treatment of cancer, reported by kirikoshi and katoh. tek is correlated with a higher risk of metastases in node-negative patients, reported by dales et al.. thus, correlation analysis was performed to extract the subtype-specific genes that were not selected by part-bfcs. these findings suggest that the genes having a high correlation with those extracted by the part-bfcs method could also be new marker genes for the sts subtypes, and that this fact gives greater confidence in the accuracy of these potential maker genes selected directly by part-bfcs.  in this study, we applied the part filtering method to sts gene expression profiling data to construct subtype predictors for diagnosis. the results showed that the genes selected by part exhibited higher prediction accuracy for sts than the other methods assessed. the genes selected by part-bfcs such as mif, cyfip hspcb, timp ldha, abr, and rgs can be used as targets for molecular diagnosis and treatment. in addition, the new candidate marker genes that were not extracted directly by part-bfcs, could be extracted by correlation analysis. we believe that this procedure, the part filtering method, should be considered as one of the candidate analytical procedures in various class prediction problems in clinical and basic oncology using transcriptome data. 
these genes are candidate marker genes for the diagnosis of sts. sixteen genes among those extracted are also known prognostic marker genes for other tumors, and they could be candidate marker genes for the diagnosis of sts. in this study, we applied the part filtering method to analyze microarray data that were obtained from soft tissue sarcoma patients for the extraction of subtype-specific genes. in addition, various combinations of filtering and modeling methods were used to extract essential subtype-specific genes. recent advances in genome technologies have provided an excellent opportunity to determine the complete biological characteristics of neoplastic tissues, resulting in improved diagnosis and selection of treatment. the results suggest that novel diagnostic and therapeutic targets for sts can be extracted by a procedure that includes the part filtering method. the performance of the filtering method was evaluated by comparison with other widely used methods, such as signal-to-noise, significance analysis of microarrays, and nearest shrunken centroids. previously, we developed the projective adaptive resonance theory filtering method as a gene filtering method. to accomplish this objective, it is important to establish a sophisticated algorithm that can deal with large quantities of data such as gene expression profiles obtained by dna microarray analysis. seven genes among the genes that are frequently selected by this method – mif, cyfip hspcb, timp ldha, abr, and rgs – are known prognostic marker genes for other tumors. correlation analysis was performed to extract marker genes that were not selected by part-bfcs. the procedure that consisted of two steps, such as the part-bfcs and the correlation analysis, was proposed.
 recent advances in genome technologies have provided an excellent opportunity to determine the complete biological characteristics of neoplastic tissues, resulting in improved diagnosis and selection of treatment. to accomplish this objective, it is important to establish a sophisticated algorithm that can deal with large quantities of data such as gene expression profiles obtained by dna microarray analysis.  previously, we developed the projective adaptive resonance theory filtering method as a gene filtering method. this is one of the clustering methods that can select specific genes for each subtype. in this study, we applied the part filtering method to analyze microarray data that were obtained from soft tissue sarcoma patients for the extraction of subtype-specific genes. the performance of the filtering method was evaluated by comparison with other widely used methods, such as signal-to-noise, significance analysis of microarrays, and nearest shrunken centroids. in addition, various combinations of filtering and modeling methods were used to extract essential subtype-specific genes. the combination of the part filtering method and boosting – the part-bfcs method – showed the highest accuracy. seven genes among the genes that are frequently selected by this method – mif, cyfip hspcb, timp ldha, abr, and rgs – are known prognostic marker genes for other tumors. these genes are candidate marker genes for the diagnosis of sts. correlation analysis was performed to extract marker genes that were not selected by part-bfcs. sixteen genes among those extracted are also known prognostic marker genes for other tumors, and they could be candidate marker genes for the diagnosis of sts.  the procedure that consisted of two steps, such as the part-bfcs and the correlation analysis, was proposed. the results suggest that novel diagnostic and therapeutic targets for sts can be extracted by a procedure that includes the part filtering method. 