BACKGROUND
promoter methylation is widely recognized as an important factor that regulates gene expression, especially in cancers  <cit> .
many genes with tumor-specific methylated promoters have been identified.
for example, the promoters of the pak <dig>  nisch, kif1a, and ogdhl genes are specifically methylated in several cancers, including breast, esophagus, lung, pancreas, colon, prostate, gastric, cervix, thyroid, kidney, head and neck, ovary, and bladder cancers  <cit> .
because genes with methylated promoters are believed to be suppressive, genes with tumor-specific hypermethylated promoters were assumed to be tumor suppressors.
similarly, genes with tumor-specific hypomethylated promoters were supposed to be oncogenic  and potential oncogene targets.
identification of promoter methylation in cancer genes is important in helping to find critical genes that can cause cancer formation.
genotype, on the other hand, is another critical factor that can affect cancer formation  <cit> .
many genotypes are known to be associated with cancers.
currently, there are no established mechanisms that can relate gene mutations to cancer formation.
for example, a cancer-specific single nucleotide polymorphism  is often associated with specific cancers  <cit> , but this snp is located in an intron of the gene.
it is still unclear how intronic snps affect gene expression.
typically, cancer-associated genotypes work solely as biomarkers.
despite of the known importance of dna methylation and genotype on cancer formation, how dna methylation and genotype cooperatively mediate cancer formation has rarely been discussed.
an exception is the recent association study reported by scherf et al.
<cit>  who found that genotype-specific promoter dna methylation of the oncogene chrnb <dig> was related to lung cancer.
opavsky et al.
<cit>  also found that the p <dig>  e2f <dig> and pten genes in a mouse model of lymphoma were methylated in a genotype-specific manner.
thus, genotype and dna methylation may contribute cooperatively to cancer formation in many other cancers.
in this paper, we sought to detect genotype-specific dna methylation in esophageal squamous cell carcinoma .
many previous studies have reported escc-specific genotypes.
for example, abnet et al.
<cit>  found that genotypic variants at position 2q <dig> on the human chromosome were related to risk of escc.
maeng et al.
<cit>  found that phosphoinositide-3-kinase and braf mutations were associated with metastatic escc and wang et al.
<cit>  found that escc was related to polymorphisms in aldh <dig> and adh1b in chinese females.
thus, genotype-specific dna methylation is expected to exist widely in escc.
in this study, we used two publicly available distinct snp microarray data sets to identify genotype-specific dna methylation in escc.
methods
dna methylation profiles and genotypes
dna methylation profiles and genotypes of blood, and normal and tumor tissues for  <dig> patients from two snp arrays, nsp and sty, were downloaded from the gene expression omnibus  at the national center for biotechnology information   <cit> .
a total of  <dig> samples for each of the dna methylation and genotypes were obtained.
the normalized data were used without further preprocessing.
principal component analysis of dna methylation profiles and genotypes
the downloaded samples were analyzed by principal component analysis  after substituting a zero for missing values.
principal components  that exhibited differences between the blood, normal tissue, and tumor tissue samples were selected for further analysis.
selection of snps  based on pcs and a t-test
the top n outliers among the pcs were selected as described previously  <cit> .
the dna methylation profiles and genotypes were investigated by three pairwise one-sided t-test comparisons: normal tissue vs tumor, blood vs tumor, and blood vs normal tissue.
then, the snps  with significant p-values  for all three pairwise comparisons were considered to be genes that displayed significant differences between all three cell types.
finally, genes that are selected in common for dna methylation and genotypes were picked up for further analysis.
gene annotation using the gendoo server
gene annotation was performed with gendoo   <cit> .
the refseq mrna ids for the selected genes were extracted from geo and transformed to the gene symbols.
the gene symbols were then uploaded to the gendoo server and diseases that were associated with gene symbols were listed with their p-values, which indicated the significance of the associations.
feature selection based on correlation coefficients
suppose xij is the microarray measurement for the ith probe  at the jth sample and yj depends on the class to which the jth sample belongs, then
yi= <dig>
the pearson and spearman correlation coefficient for the ith probe  was then computed between xij and yj.
finally, the  <dig> probes  with the largest correlation coefficients were selected.
feature selection based on partial least squares
partial least squares  provides a bilinear representation of data and pls-based feature selection aims to select features that have the most weight to linear combinations  <cit> .
for simplicity, we employed the pls+mclass strategy  <cit> , where pls was applied directly to multiclass samples.
this strategy is, at most, the third-best depending on the data set being tested .
however, because there are only three classes in our study, very little improvement can be expected even if the best strategy is employed, as shown previously  <cit> .
stepwise feature selection
stepwise feature selection was performed by adding/removing features iteratively, until the performance reached its maximum.
in this study we performed stepwise variable selection using the stepclass function with the lda function as implemented in r  <cit> .
lasso-based feature selection
least absolute shrinkage and selection operator   <cit>  is another frequently used feature extraction method.
lasso applies linear discriminant analysis with minimizing sum of regression coefficients.
this results in the elimination of redundant features.
to apply lasso to our data set, we employed the lars function implemented in r  <cit>  by specifying the type=" lasso" option.
t-test of the microarray measurements between genotype and dna methylation
for the snps that were selected in common between genotype and dna methylation, we used the one-sided t-test that rejects the null hypothesis that the microarray measurement of genotype is as large as the dna methylation value in favor of the microarray measurement of genotype is more than the dna methylation value.
for random sampling, the same set of snps was used for the genotype and dna methylation measurements.
protein tertiary structure prediction
to predict the tertiary structure of the proteins encoded by the selected genes we used the fams  <cit>  and phyre <dig> software  <cit> .
screening drug candidate compounds from the drugbank database
we downloaded  <dig> compounds in smiles format from drugbank  <cit> .
the smiles format was transformed to three dimensional structures by babel  <cit> .
the structures of  <dig> of the compounds were obtained.
tanimoto indices were computed between the individual compounds and ligands that bind to template proteins.
compounds with tanimoto indices larger than the threshold values ,  <dig>  for the other proteins) were selected as candidate drug compounds.
selection of template proteins and ligands
the template protein structures that we used for in silico drug discovery were selected as follows: first, each template must be used as a model protein for the ligand binding region of the target protein; second, the protein structures that ligands could bind to were selected as templates; and third, as many as possible of the ligands that could bind to several of the model proteins, including those not selected as templates, were selected and fitted to a template protein.
these ligands were the "fingerprint" for drug discovery and were used for to compute the tanimoto index.
docking simulation using chooseld
docking between the screened compounds and template proteins was performed using chooseld  <cit> .
the fpascore  <cit>   were computed ten times for each compound.
the compounds were ranked based on the best score among the ten values.
whole computations were performed independently three times and consistency between the three trials was evaluated.
estimation of coincident of highly ranked compounds between three independent trials
suppose that ri,  is the descending rank order of the fpascore attributed to the ith compound at the nth trial, where nc is the total number of compounds considered, then,
s={i|ri≤k}
is the set of k highly ranked compounds at the nth trial.
then, the expected number of compounds selected in common up to the kth rank, n <dig>  is computed when there are no correlations between the ris.
because the probability that s includes compounds in s is k/nc, s is expected to include k2/nc compounds that exist in s. thus, the number of unique compounds in s and s is expected to be
k+k-k2nc=k2-knc
and the probability that s includes compounds in either s or s is
knc2-knc
thus, s is expected to include
k2nc2-knc
compounds that exist in either s or s. finally, the total number of unique compounds in s, s, and s is expected to be
n0=k2-knc+k-k2nc2-knc=k1+1-knc2-knc
when the number of highly ranked compounds selected in common between the three independent trials is much less than this number and is close to k, we can conclude that consistency between the three trials is high.
RESULTS
estimation of genotype-specific dna methylation
there is no unique criterion that can estimate genotype-specific dna methylation.
aberrant methylation itself can be estimated by various criteria; for example, using the ratio or the difference of mean values between normal and tumor tissues or using p-values obtained by a statistical test such as a t-test.
each of the criterion may give a different genotype-specific dna methylation set of genes.
in addition, some genotypes are either heavily demethylated or methylated in tumor tissue compared with normal tissue.
if this genotype is very rare in the tumor tissue, it is clearly unreasonable to regard this genotype-specific dna methylation as being the cause of the tumor.
ideally, to be sure that a particular genotype-specific dna methylation could cause the tumor, the following conditions should be satisfied:
<dig>  the genotype is specifically demethylated/methylated in the tumor tissue compared with other genotypes .
<dig>  the genotype is abundant in the tumor tissue .
the best balance between these two conditions is not easy to estimate, because there is no standard understanding about the kind of gene abnormalities that generally cause tumors.
in this study, we used three kinds of samples: blood, normal and tumor tissues.
this made the comparisons more difficult than a comparison between only normal and tumor tissues, because we are not sure if normal tissue is an expected intermediate between blood and tumor.
to avoid uncertainties that this complicated situations might cause when estimating genotype-specific dna methylation, we employed a recently proposed pca-based unsupervised feature selection method  <cit> .
this procedure does not require the user to select the criterion that is used to estimate genotype-specific dna methylation.
it is necessary simply to select the suitable pc by which the snps with genotype-specific dna methylation are selected.
genotype-specific dna methylation estimated using the nsp microarray data
the pcs obtained when pca was applied to the nsp microarray measurements of genotype are shown in figure  <dig>  although the first pc  had the dominant contribution , no significant differences between blood, and the normal and tumor tissues were seen.
on the other hand, the second pc  clearly distinguished between blood, and normal and tumor tissues.
therefore, we used pc <dig> to select probes  that exhibited significant differences between the blood, and normal and tumor tissues.
because pc <dig>  exhibited no significant differences between the blood, normal and tumor samples and had very little contribution, we did not use the third pc  to select snps.
the pcs obtained when pca was applied to the nsp microarray measurements of dna methylation are shown in figure  <dig>  pc <dig>  was again the pc that clearly distinguished between blood, and normal and tumor tissues.
pc <dig> was, therefore, used to select the snps that exhibited significant differences between the three samples.
the two dimensional  embedding of snps  for dna methylation and genotype are shown in figure  <dig>  because pc <dig> showed significant differences between the blood, and normal tissues and tumor tissues, we selected the  <dig> topmost outliers along the pc <dig> axis for both dna methylation and genotype.
to see if genotype-specific methylated snps were selected correctly, we filtered the selected snps based on the following criteria:
<dig>  intersection between top n outliers between dna methylation and genotype.
<dig>  all three associated p-values adjusted by the bh criterion  <cit>  are less than  <dig> , when three pairwise one-sided t-tests  are applied.
a total of  <dig> snps were selected in common from the top  <dig> outliers between genotype and dna methylation after applying the first criterion.
because there were more than  <dig>  snps on the nsp microarray, the p-value for  <dig> snps being selected in common from  <dig> is less than  <dig> × 10− <dig>  the topmost  <dig>   <dig>   <dig>   <dig>   <dig> and  <dig> selected snps from within the top n  outliers, respectively, after applying the p-value filtering  are listed in table  <dig>  more detailed annotations for selected snps and their associated genes are available in additional file  <dig>
the  <dig> selected snps  that exhibited significant differences between blood, and normal and tumor tissues, within the top n  outliers for both dna methylation and genotype.
top  <dig> outliers are shown in red in fig.
<dig>  for detailed annotations, including associated genes, of the selected snps, see additional file  <dig> .
genotype-specific dna methylation estimated using the sty microarray data
the pcs obtained when pca was applied to the sty microarray measurements of genotype are shown in figure  <dig>  although pc <dig>  had the dominant contribution of 81%, no significant differences between blood, normal and tumor tissues were observed.
pc <dig>  had very little contribution and also exhibited no significant differences between the three samples.
on the other hand, both pc <dig>  and the fourth pc  clearly distinguished between blood, normal and tumor tissues.
because the pc <dig> and pc <dig> results were similar, at this stage we did not decide which of them was the more suitable pc to use to select snps that exhibited significant differences between blood, normal and tumor tissues.
the pcs obtained when pca was applied to the sty microarray measurements of dna methylation are shown in figure  <dig>  pc <dig>  and pc <dig>  were again the pcs that clearly distinguished between blood, normal and tumor tissues.
pc <dig>  and pc <dig>  did not exhibit strong significant differences.
because, unlike in the case using the nsp microarray data, we could not uniquely select a pair of pcs to use to select the snps that exhibited the most significant differences between the blood, normal and tumor tissues, we tried various pc combinations for the genotype and dna methylation measurements.
we found that the best combinations were
<dig>  pc <dig> for genotype  and pc <dig> for dna methylation .
<dig>  pc <dig> for genotype  and pc <dig> for dna methylation .
the two dimensional embedding of snps  for dna methylation and genotypes for these two combinations of genotype and dna methylation pcs are shown in figure  <dig>  snps  that exhibited differences between the three samples, in common for both dna methylation and genotype, were selected using the criteria described in the previous section.
for the combination of pc <dig> for genotype  and the pc <dig> for dna methylation , a total of  <dig> snps were selected in common within the  <dig> topmost outliers between dna methylation and genotype after applying the first criterion.
similarly, for the combination of pc <dig> for genotype  and pc <dig> for dna methylation , a total of  <dig> snps were selected in common within the  <dig> topmost outliers between dna methylation and genotype after applying the first criterion.
because there were more than  <dig>  snps on the sty microarray, the p-values for  <dig> or  <dig> snps being selected in common from  <dig> are less than  <dig> × 10− <dig>  the topmost  <dig> ,  <dig> ,  <dig> ,  <dig> ,  <dig> , and  <dig>  snps from the top n  outliers, respectively, were selected after applying the p-value filtering for the first  combination of pcs are listed in table  <dig> .
more detailed annotations for selected snps and their associated genes are available in additional file  <dig>
the  <dig> selected snps  that exhibited significant difference between blood, and normal and tumor tissues, within the top n  outliers for both dna methylation and genotype.
top  <dig> outliers are shown in red in figures 6b and 6c.
for detailed annotations, including associated genes, of the selected snps, see additional file  <dig> .
estimation of optimal n
we did not know what is the optimal n, the number of selected snps with aberrant dna methylation, to use in this study.
the results in tables  <dig>   <dig>  and  <dig> indicate that increasing n helps in selecting a large enough number of snps that pass the selection criteria.
larger ns possibly give more plausible snps with genotype-specific dna methylation.
we used n =  <dig> mainly because, when a large number of snps are selected at this stage, in the following stages, it is difficult to screen the snps and to predict the tertiary structure of the proteins associated with the snps.
a rigorous estimation of the optimal n is a problem for future studies.
snp_a-4254588
snp_a-1827527
snp_a-2272342
the  <dig> selected snps  that exhibited significant difference between blood, and normal and tumor tissues, within the top n  outliers for both dna methylation and genotype.
top  <dig> outliers are shown in red in figures 6a and 6d.
for detailed annotations, including associated genes, of the selected snps, see additional file  <dig> .
comparison with other methods
to our knowledge, no feature selection methods that are applicable to three classes of data set without the need for preknowledge about the internal ranking between the classes are currently available.
although our method requires the manual selection of the pcs used for feature selection, no pre-knowledge about the ranking between classes is needed and how the classes should be ranked is quite clear from the pcs .
thus, there are no other methods that can be compared with our methods.
however, because we now know that the rank between the classes is blood <normal tissue <tumor tissue, we have applied other methods that require this pre-knowledge.
the number of snps selected in common between the top-ranked  <dig> snps in genotype and dna methylationusing various methods.
present, this study; pearson, pearson correlation coefficients based method; spearman, spearman correlation coefficients based method; pls, partial least squares based method; stepclass, stepclass  based method; and lasso: lasso based method.
nsp and sty are the microarray data sets used in the study.
sty <dig> and sty <dig> correspond to the pc <dig> for genotype /pc <dig> for dna methylation  and the pc <dig> for genotype /pc <dig> for dna methylation  combinations of pcs.
the pearson correlation-based, spearman correlation-based, and pls-based feature selection methods successfully selected the  <dig> topmost snps for genotype and dna methylation.
however, the number of snps selected in common between genotype and dna methylation was smaller than the numbers selected the present study .
thus, our method clearly outperforms the other methods in selecting the genes in common between genotype and dna methylation.
discussion
properties of the selected snps
almost all selected snps were located outside protein cording regions of the genes .
the only exceptions were snp_a- <dig> , snp_a- <dig> , and snp_a- <dig>  thus, the majority of the snps are presumably related to the regulation of gene expression.
the snps that were not located in protein coding regions were located in the promoters , and also in introns and in the downstream regions of genes.
thus, the effect of genotype-specific dna methylation on gene expression is not straightforward.
in addition, some of the selected snps have not been reported in chinese populations, although all patients in the microarray data sets that we used in this study were chinese.
this finding indicates that we have correctly selected mutation that may cause cancer formation.
screening of cancer-related genes
to determine if the selected snps are biologically related to cancers, the genes containing the snps were annotated using gendoo  <cit> .
the refseq mrna ids of the genes were extracted from geo and mapped to gene symbols .
the gene symbols were uploaded to the gendoo server and the diseases that were reported to be associated with each of the gene symbols were listed .
we found that  <dig> of the  <dig> genes listed in additional file  <dig> were associated with at least one cancer-related disease.
in addition, we performed a literature search to find papers that reported the relationship between any of the  <dig> selected genes and cancers, because the gendoo server annotation is based on automated text-mining and may include some misinterpretations.
we found that most of  <dig> genes were mentioned in at least one published paper that described their relationship with cancer .
thus, we confirmed that more than half  the  <dig> genes screened by our method were cancer-related genes.
in particular, twelve genes  were associated with "carcinoma, squamous cell" and eleven genes  were associated with "esophageal neoplasms".
among them, nine genes are associated with both.
because this study used data sets for escc , this association is reasonable and demonstrates the reliability of our method.
genes with genotype-specific dna methylation are less methylated than expected
we compared the microarray measurements between genotype and dna methylation of the probes selected in common  and found that the microarray dna methylation measurements were always less than the genotype measurements.
table  <dig> shows the results of the t-test applied to microarray measurements between genotype and dna methylation.
this observation is interesting, because a less methylated promoter usually indicates a more expressive genes, although not all the selected snps with dna methylation were in the promoter region of the genes .
to check that the demethylation was not because of inaccurate microarray measurement normalization, we randomly sampled the same number of snps as those in tables  <dig>   <dig>  and  <dig>  <dig>  times, and computed p-values adjusted by the bh criterion  <cit> .
we found that typically less than  <dig> % of the trials had adjusted p-vales < <dig>  .
thus, we determined that there were no normalization biases in the data sets and the low observed p-values shown in table  <dig> were not obtained because of fluctuations.
nsp and sty are the microarray data sets used in the study.
sty <dig> and sty <dig> correspond to the pc <dig> for genotype /pc <dig> for dna methylation  and pc <dig> for genotype /pc <dig> for dna methylation  combinations of pcs.
∗ indicates p-values < <dig>  x 10− <dig>
p-values were computed using t-tests for the microarray measurements between genotype and dna methylation for  <dig> independent sets of randomly sampled snps.
each of the sets contained at least as many snps as are included in table  <dig>  nsp and sty are the microarray data sets used in the study.
sty <dig> and sty <dig> correspond to the pc <dig> for genotype / pc <dig> for dna methylation  and pc <dig> for genotype / pc <dig> for dna methylation  combinations of pcs.
p-values, adjusted by the bh criterion, of <  <dig>  were regarded as significant.
structure prediction of the proteins associated with selected genes
although we selected genes with genotype-specific dna methylation, for therapeutic purposes, we need to design drugs for the proteins that are encoded by these genes.
to identify candidate drugs computationally, the tertiary structures of the target proteins are required as templates.
however, the structures of many of the encoded proteins have not been reported.
to obtain the tertiary structure of these proteins, we used two protein structure prediction servers fams  <cit>  and phyre <dig>  <cit>  to predict the structure using only the amino acid sequence of the protein  that were used to predict the tertiary structures of the proteins).
the results of the protein structure predictions are summarized in additional file  <dig>  some protein structures were already in the protein data bank   <cit> , if not, they were modeled using the structure of a suitable reference protein.
these structures were then used as templates to predict drug candidates in silico.
for the proteins that were not in the pdb, for the reference proteins that were used for the structure prediction, we sought cancer-related papers that cited the reference proteins.
the references to these papers are listed in additional file  <dig>  most of reference proteins used for structure prediction were cancer-related.
this finding also suggests that our gene selection process and protein structure prediction are plausible.
a summary of the entire of gene selection processes is illustrated in figure  <dig>
in silico drug discovery
we tried to design drugs that could bind to some of the protein templates using an in silico drug discovery method in which chemical compounds that potentially bind to proteins and suppress protein functions were sought computationally.
for this purpose, we selected the three proteins encoded by alk, egln <dig>  and nuak <dig> as drug targets, based upon a literature search and the gene annotations that indicated that these genes were expressed in cancer and had potentially functional binding pockets  for ligands.
details of the annotations are listed in additional file  <dig>  the drug discovery process that we used is illustrated in figure  <dig> .
after the fpascores were estimated , to check if three independent trials were feasible, we tested coincidence between three trials in two ways.
first, we computed the correlation coefficients between three independent trials.
for all pairwise computations for alk, egln <dig>  and nuak <dig>  the correlation coefficients were greater than  <dig> .
this suggests that the frascores computed by chooseld were highly reproducible.
.
however, the correlation coefficients represent the overall reproducibilities of fpascores for the candidate drug compounds.
it is more important that the compounds with higher fpascores, i.e., those regarded as being highly reliable, were reproducible.
therefore, we checked how often the highly ranked compounds were selected between the three trials and found that the selection of the highly ranked compounds was also highly reproducible .
the ranking of the tested compounds based on their fpascores are available as additional file  <dig>  the results are summarized in table  <dig>  among the  <dig> top-ranked compounds for alk, eight compounds targeted cancer genes, and two out of the eight targeted alk.
among the  <dig> top-ranked compounds for elgn <dig>  including fe as a ligand, eight compounds targeted cancer genes and two out of the eight targeted egln <dig>  which is paralog of egln <dig>  among the  <dig> top-ranked compounds for elgn <dig>  without including fe as a ligand but as a mediator, six were in common with the top-ranked compounds for egln <dig> when fe was included as a ligand.
among the other four compounds, one targeted egln <dig>  of the  <dig> of the top-ranked compounds for nuak <dig>  most target more than  <dig> other genes and thus lack specificity.
for a more detailed discussion about top  <dig> ranked compounds for alk, egln <dig>  and nuak <dig>  see additional file  <dig>  all of these findings suggested that the top-ranked compounds for each of the proteins were feasible candidate drugs.
the compounds were ranked based on fpascores averaged over three independent trials and their representative target cancer genes.
for the full lists of ranked compounds and a detailed discussion of the target cancer genes listed here, see additional files  <dig> and  <dig>  respectively.
alk and egln <dig>  a paralog of egln <dig>  are in bold letters.
"―" indicates that no known cancer-associated genes are targeted by these compounds.
CONCLUSIONS
in this paper, we investigated genotype-specific dna methylation in esophageal squamous cell carcinoma, using principal component analysis.
we identified more than  <dig> genotype-specific dna methylation snps associated with the disease.
among  <dig> genotype-specific dna methylation associated genes,  <dig> were associated with cancers using the gendoo server.
the structures of proteins encoded by selected genotype-specific dna methylation associated genes were predicted successfully using two profile based methods, fams and phyre <dig>  candidate drug compounds were screened using the tanimoto index from drugbank and were evaluated by chooseld for three selected proteins, alk, egln <dig> and nuak <dig>  the selected drug candidates were promising starting points for future studies.
authors' contributions
mi, hu, and yht planned the research study.
rk and yht performed the gene screening.
mi and hu performed the protein structure prediction using fams and in silico drug discovery using chooseld.
mi, hu, and yht wrote the paper.
supplementary material
additional file 1
annotation of selected snps.
annotation information of selected snps  including a list of genes associated with each snp.
click here for file
additional file 2
genes with significant genotype-specific dna methylation.
nsp and sty are the microarray data sets used in the study.
sty <dig> and sty <dig> correspond to the pc <dig> for genotype /pc <dig> for dna methylation  and the pc <dig> for genotype /pc <dig> for dna methylation  combinations of pcs, respectively.
the genes indicated in bold letters were associated with at least one cancer-related disease due to gendoo  <cit> .
click here for file
additional file 3
association of cancer related diseases with genes based on the gendoo server.
list of cancer-related diseases associated with the genes indicated in bold letters in additional file  <dig> based on the gendoo server  <cit> .
associations with "esophageal neoplasms" and "carcinoma, squamous cell" are highlighted.
click here for file
additional file 4
list of references that report the association of the selected genes with cancer-related diseases.
the selected genes are those listed in additional file  <dig>  the list also includes the performance of the protein structure prediction and the references that associate the proteins that were used as reference proteins to predict protein structure with cancer-related diseases.
click here for file
additional file 5
amino acid sequences for the proteins encoded by the selected genes.
the amino acid sequences were used for protein structure predictions and are listed in fasta format.
click here for file
additional file 6
pearson correlation coefficients and scatter plots between independent trials for fpascore computation.
alk;  egln <dig> with fe.
egln <dig> without fe.
nuak <dig>  scatter plots are shown for reference.
red diagonal lines indicate that fpascores were identical between two trials.
click here for file
additional file 7
number of compounds selected in common in three trials as highly feasible drug candidate compounds.
the number of common selections from among the top k ranked compounds in three trials .
black solid line indicates the expected number of compounds to be selected in common between three trials when the three trials are not correlated at all , see methods for details).
blue straight line indicates a complete match between the three trials.
alk.
egln <dig> with fe.
egln <dig> without fe.
nuak <dig>  it is clear that the number of selections in common is much less than would be expected for random selections, n <dig>  and is very close to a complete match .
click here for file
additional file 8
full list of ranked compounds.
list of compounds ranked based on the fpascores averaged over three independent trials, for alk, egln <dig> , and nuak <dig>
click here for file
additional file 9
detailed discussion of the top  <dig> compounds listed in table  <dig>  target protein information and protein inhibition information were taken from drugbank  <cit>  and chembl  <cit> .
click here for file
acknowledgements
we would like to thank dr. katsuichiro komatsu who helped with the in silico drug screening using chooseld.
declaration
this research was funded by kakenhi,  <dig> and chuo university joint research grant.
this article has been published as part of bmc systems biology volume  <dig> supplement  <dig>  2014: selected articles from the twelfth asia pacific bioinformatics conference : systems biology.
the full contents of the supplement are available online at http://www.biomedcentral.com/bmcsystbiol/supplements/8/s <dig>

 promoter methylation is widely recognized as an important factor that regulates gene expression, especially in cancers. many genes with tumor-specific methylated promoters have been identified. for example, the promoters of the pak nisch, kif1a, and ogdhl genes are specifically methylated in several cancers, including breast, esophagus, lung, pancreas, colon, prostate, gastric, cervix, thyroid, kidney, head and neck, ovary, and bladder cancers. because genes with methylated promoters are believed to be suppressive, genes with tumor-specific hypermethylated promoters were assumed to be tumor suppressors. similarly, genes with tumor-specific hypomethylated promoters were supposed to be oncogenic and potential oncogene targets. identification of promoter methylation in cancer genes is important in helping to find critical genes that can cause cancer formation. genotype, on the other hand, is another critical factor that can affect cancer formation. many genotypes are known to be associated with cancers. currently, there are no established mechanisms that can relate gene mutations to cancer formation. for example, a cancer-specific single nucleotide polymorphism is often associated with specific cancers, but this snp is located in an intron of the gene. it is still unclear how intronic snps affect gene expression. typically, cancer-associated genotypes work solely as biomarkers. despite of the known importance of dna methylation and genotype on cancer formation, how dna methylation and genotype cooperatively mediate cancer formation has rarely been discussed. an exception is the recent association study reported by scherf et al. who found that genotype-specific promoter dna methylation of the oncogene chrnb was related to lung cancer. opavsky et al. also found that the p e2f and pten genes in a mouse model of lymphoma were methylated in a genotype-specific manner. thus, genotype and dna methylation may contribute cooperatively to cancer formation in many other cancers. in this paper, we sought to detect genotype-specific dna methylation in esophageal squamous cell carcinoma. many previous studies have reported escc-specific genotypes. for example, abnet et al. found that genotypic variants at position 2q on the human chromosome were related to risk of escc. maeng et al. found that phosphoinositide-3-kinase and braf mutations were associated with metastatic escc and wang et al. found that escc was related to polymorphisms in aldh and adh1b in chinese females. thus, genotype-specific dna methylation is expected to exist widely in escc. in this study, we used two publicly available distinct snp microarray data sets to identify genotype-specific dna methylation in escc. methods dna methylation profiles and genotypes dna methylation profiles and genotypes of blood, and normal and tumor tissues for patients from two snp arrays, nsp and sty, were downloaded from the gene expression omnibus at the national center for biotechnology information. a total of samples for each of the dna methylation and genotypes were obtained. the normalized data were used without further preprocessing. principal component analysis of dna methylation profiles and genotypes the downloaded samples were analyzed by principal component analysis after substituting a zero for missing values. principal components that exhibited differences between the blood, normal tissue, and tumor tissue samples were selected for further analysis. selection of snps based on pcs and a t-test the top n outliers among the pcs were selected as described previously. the dna methylation profiles and genotypes were investigated by three pairwise one-sided t-test comparisons: normal tissue vs tumor, blood vs tumor, and blood vs normal tissue. then, the snps with significant p-values for all three pairwise comparisons were considered to be genes that displayed significant differences between all three cell types. finally, genes that are selected in common for dna methylation and genotypes were picked up for further analysis. gene annotation using the gendoo server gene annotation was performed with gendoo. the refseq mrna ids for the selected genes were extracted from geo and transformed to the gene symbols. the gene symbols were then uploaded to the gendoo server and diseases that were associated with gene symbols were listed with their p-values, which indicated the significance of the associations. feature selection based on correlation coefficients suppose xij is the microarray measurement for the ith probe at the jth sample and yj depends on the class to which the jth sample belongs, then yi= the pearson and spearman correlation coefficient for the ith probe was then computed between xij and yj. finally, the probes with the largest correlation coefficients were selected. feature selection based on partial least squares partial least squares provides a bilinear representation of data and pls-based feature selection aims to select features that have the most weight to linear combinations. for simplicity, we employed the pls+mclass strategy, where pls was applied directly to multiclass samples. this strategy is, at most, the third-best depending on the data set being tested. however, because there are only three classes in our study, very little improvement can be expected even if the best strategy is employed, as shown previously. stepwise feature selection stepwise feature selection was performed by adding/removing features iteratively, until the performance reached its maximum. in this study we performed stepwise variable selection using the stepclass function with the lda function as implemented in r. lasso-based feature selection least absolute shrinkage and selection operator is another frequently used feature extraction method. lasso applies linear discriminant analysis with minimizing sum of regression coefficients. this results in the elimination of redundant features. to apply lasso to our data set, we employed the lars function implemented in r by specifying the type=" lasso" option. t-test of the microarray measurements between genotype and dna methylation for the snps that were selected in common between genotype and dna methylation, we used the one-sided t-test that rejects the null hypothesis that the microarray measurement of genotype is as large as the dna methylation value in favor of the microarray measurement of genotype is more than the dna methylation value. for random sampling, the same set of snps was used for the genotype and dna methylation measurements. protein tertiary structure prediction to predict the tertiary structure of the proteins encoded by the selected genes we used the fams and phyre software. screening drug candidate compounds from the drugbank database we downloaded compounds in smiles format from drugbank. the smiles format was transformed to three dimensional structures by babel. the structures of of the compounds were obtained. tanimoto indices were computed between the individual compounds and ligands that bind to template proteins. compounds with tanimoto indices larger than the threshold values, for the other proteins) were selected as candidate drug compounds. selection of template proteins and ligands the template protein structures that we used for in silico drug discovery were selected as follows: first, each template must be used as a model protein for the ligand binding region of the target protein; second, the protein structures that ligands could bind to were selected as templates; and third, as many as possible of the ligands that could bind to several of the model proteins, including those not selected as templates, were selected and fitted to a template protein. these ligands were the "fingerprint" for drug discovery and were used for to compute the tanimoto index. docking simulation using chooseld docking between the screened compounds and template proteins was performed using chooseld. the fpascore were computed ten times for each compound. the compounds were ranked based on the best score among the ten values. whole computations were performed independently three times and consistency between the three trials was evaluated. estimation of coincident of highly ranked compounds between three independent trials suppose that ri, is the descending rank order of the fpascore attributed to the ith compound at the nth trial, where nc is the total number of compounds considered, then, s={i|ri≤k} is the set of k highly ranked compounds at the nth trial. then, the expected number of compounds selected in common up to the kth rank, n is computed when there are no correlations between the ris. because the probability that s includes compounds in s is k/nc, s is expected to include k2/nc compounds that exist in s. thus, the number of unique compounds in s and s is expected to be k+k-k2nc=k2-knc and the probability that s includes compounds in either s or s is knc2-knc thus, s is expected to include k2nc2-knc compounds that exist in either s or s. finally, the total number of unique compounds in s, s, and s is expected to be n0=k2-knc+k-k2nc2-knc=k1+1-knc2-knc when the number of highly ranked compounds selected in common between the three independent trials is much less than this number and is close to k, we can conclude that consistency between the three trials is high.  estimation of genotype-specific dna methylation there is no unique criterion that can estimate genotype-specific dna methylation. aberrant methylation itself can be estimated by various criteria; for example, using the ratio or the difference of mean values between normal and tumor tissues or using p-values obtained by a statistical test such as a t-test. each of the criterion may give a different genotype-specific dna methylation set of genes. in addition, some genotypes are either heavily demethylated or methylated in tumor tissue compared with normal tissue. if this genotype is very rare in the tumor tissue, it is clearly unreasonable to regard this genotype-specific dna methylation as being the cause of the tumor. ideally, to be sure that a particular genotype-specific dna methylation could cause the tumor, the following conditions should be satisfied: the genotype is specifically demethylated/methylated in the tumor tissue compared with other genotypes. the genotype is abundant in the tumor tissue. the best balance between these two conditions is not easy to estimate, because there is no standard understanding about the kind of gene abnormalities that generally cause tumors. in this study, we used three kinds of samples: blood, normal and tumor tissues. this made the comparisons more difficult than a comparison between only normal and tumor tissues, because we are not sure if normal tissue is an expected intermediate between blood and tumor. to avoid uncertainties that this complicated situations might cause when estimating genotype-specific dna methylation, we employed a recently proposed pca-based unsupervised feature selection method. this procedure does not require the user to select the criterion that is used to estimate genotype-specific dna methylation. it is necessary simply to select the suitable pc by which the snps with genotype-specific dna methylation are selected. genotype-specific dna methylation estimated using the nsp microarray data the pcs obtained when pca was applied to the nsp microarray measurements of genotype are shown in figure although the first pc had the dominant contribution, no significant differences between blood, and the normal and tumor tissues were seen. on the other hand, the second pc clearly distinguished between blood, and normal and tumor tissues. therefore, we used pc to select probes that exhibited significant differences between the blood, and normal and tumor tissues. because pc exhibited no significant differences between the blood, normal and tumor samples and had very little contribution, we did not use the third pc to select snps. the pcs obtained when pca was applied to the nsp microarray measurements of dna methylation are shown in figure pc was again the pc that clearly distinguished between blood, and normal and tumor tissues. pc was, therefore, used to select the snps that exhibited significant differences between the three samples. the two dimensional embedding of snps for dna methylation and genotype are shown in figure because pc showed significant differences between the blood, and normal tissues and tumor tissues, we selected the topmost outliers along the pc axis for both dna methylation and genotype. to see if genotype-specific methylated snps were selected correctly, we filtered the selected snps based on the following criteria: intersection between top n outliers between dna methylation and genotype. all three associated p-values adjusted by the bh criterion are less than, when three pairwise one-sided t-tests are applied. a total of snps were selected in common from the top outliers between genotype and dna methylation after applying the first criterion. because there were more than snps on the nsp microarray, the p-value for snps being selected in common from is less than × 10− the topmost and selected snps from within the top n outliers, respectively, after applying the p-value filtering are listed in table more detailed annotations for selected snps and their associated genes are available in additional file the selected snps that exhibited significant differences between blood, and normal and tumor tissues, within the top n outliers for both dna methylation and genotype. top outliers are shown in red in fig. for detailed annotations, including associated genes, of the selected snps, see additional file. genotype-specific dna methylation estimated using the sty microarray data the pcs obtained when pca was applied to the sty microarray measurements of genotype are shown in figure although pc had the dominant contribution of 81%, no significant differences between blood, normal and tumor tissues were observed. pc had very little contribution and also exhibited no significant differences between the three samples. on the other hand, both pc and the fourth pc clearly distinguished between blood, normal and tumor tissues. because the pc and pc results were similar, at this stage we did not decide which of them was the more suitable pc to use to select snps that exhibited significant differences between blood, normal and tumor tissues. the pcs obtained when pca was applied to the sty microarray measurements of dna methylation are shown in figure pc and pc were again the pcs that clearly distinguished between blood, normal and tumor tissues. pc and pc did not exhibit strong significant differences. because, unlike in the case using the nsp microarray data, we could not uniquely select a pair of pcs to use to select the snps that exhibited the most significant differences between the blood, normal and tumor tissues, we tried various pc combinations for the genotype and dna methylation measurements. we found that the best combinations were pc for genotype and pc for dna methylation. pc for genotype and pc for dna methylation. the two dimensional embedding of snps for dna methylation and genotypes for these two combinations of genotype and dna methylation pcs are shown in figure snps that exhibited differences between the three samples, in common for both dna methylation and genotype, were selected using the criteria described in the previous section. for the combination of pc for genotype and the pc for dna methylation, a total of snps were selected in common within the topmost outliers between dna methylation and genotype after applying the first criterion. similarly, for the combination of pc for genotype and pc for dna methylation, a total of snps were selected in common within the topmost outliers between dna methylation and genotype after applying the first criterion. because there were more than snps on the sty microarray, the p-values for or snps being selected in common from are less than × 10− the topmost,,,,, and snps from the top n outliers, respectively, were selected after applying the p-value filtering for the first combination of pcs are listed in table. more detailed annotations for selected snps and their associated genes are available in additional file the selected snps that exhibited significant difference between blood, and normal and tumor tissues, within the top n outliers for both dna methylation and genotype. top outliers are shown in red in figures 6b and 6c. for detailed annotations, including associated genes, of the selected snps, see additional file. estimation of optimal n we did not know what is the optimal n, the number of selected snps with aberrant dna methylation, to use in this study. the results in tables and indicate that increasing n helps in selecting a large enough number of snps that pass the selection criteria. larger ns possibly give more plausible snps with genotype-specific dna methylation. we used n = mainly because, when a large number of snps are selected at this stage, in the following stages, it is difficult to screen the snps and to predict the tertiary structure of the proteins associated with the snps. a rigorous estimation of the optimal n is a problem for future studies. snp_a-4254588 snp_a-1827527 snp_a-2272342 the selected snps that exhibited significant difference between blood, and normal and tumor tissues, within the top n outliers for both dna methylation and genotype. top outliers are shown in red in figures 6a and 6d. for detailed annotations, including associated genes, of the selected snps, see additional file. comparison with other methods to our knowledge, no feature selection methods that are applicable to three classes of data set without the need for preknowledge about the internal ranking between the classes are currently available. although our method requires the manual selection of the pcs used for feature selection, no pre-knowledge about the ranking between classes is needed and how the classes should be ranked is quite clear from the pcs. thus, there are no other methods that can be compared with our methods. however, because we now know that the rank between the classes is blood <normal tissue <tumor tissue, we have applied other methods that require this pre-knowledge. the number of snps selected in common between the top-ranked snps in genotype and dna methylationusing various methods. present, this study; pearson, pearson correlation coefficients based method; spearman, spearman correlation coefficients based method; pls, partial least squares based method; stepclass, stepclass based method; and lasso: lasso based method. nsp and sty are the microarray data sets used in the study. sty and sty correspond to the pc for genotype /pc for dna methylation and the pc for genotype /pc for dna methylation combinations of pcs. the pearson correlation-based, spearman correlation-based, and pls-based feature selection methods successfully selected the topmost snps for genotype and dna methylation. however, the number of snps selected in common between genotype and dna methylation was smaller than the numbers selected the present study. thus, our method clearly outperforms the other methods in selecting the genes in common between genotype and dna methylation. discussion properties of the selected snps almost all selected snps were located outside protein cording regions of the genes. the only exceptions were snp_a-, snp_a-, and snp_a- thus, the majority of the snps are presumably related to the regulation of gene expression. the snps that were not located in protein coding regions were located in the promoters, and also in introns and in the downstream regions of genes. thus, the effect of genotype-specific dna methylation on gene expression is not straightforward. in addition, some of the selected snps have not been reported in chinese populations, although all patients in the microarray data sets that we used in this study were chinese. this finding indicates that we have correctly selected mutation that may cause cancer formation. screening of cancer-related genes to determine if the selected snps are biologically related to cancers, the genes containing the snps were annotated using gendoo. the refseq mrna ids of the genes were extracted from geo and mapped to gene symbols. the gene symbols were uploaded to the gendoo server and the diseases that were reported to be associated with each of the gene symbols were listed. we found that of the genes listed in additional file were associated with at least one cancer-related disease. in addition, we performed a literature search to find papers that reported the relationship between any of the selected genes and cancers, because the gendoo server annotation is based on automated text-mining and may include some misinterpretations. we found that most of genes were mentioned in at least one published paper that described their relationship with cancer. thus, we confirmed that more than half the genes screened by our method were cancer-related genes. in particular, twelve genes were associated with "carcinoma, squamous cell" and eleven genes were associated with "esophageal neoplasms". among them, nine genes are associated with both. because this study used data sets for escc, this association is reasonable and demonstrates the reliability of our method. genes with genotype-specific dna methylation are less methylated than expected we compared the microarray measurements between genotype and dna methylation of the probes selected in common and found that the microarray dna methylation measurements were always less than the genotype measurements. table shows the results of the t-test applied to microarray measurements between genotype and dna methylation. this observation is interesting, because a less methylated promoter usually indicates a more expressive genes, although not all the selected snps with dna methylation were in the promoter region of the genes. to check that the demethylation was not because of inaccurate microarray measurement normalization, we randomly sampled the same number of snps as those in tables and times, and computed p-values adjusted by the bh criterion. we found that typically less than % of the trials had adjusted p-vales <. thus, we determined that there were no normalization biases in the data sets and the low observed p-values shown in table were not obtained because of fluctuations. nsp and sty are the microarray data sets used in the study. sty and sty correspond to the pc for genotype /pc for dna methylation and pc for genotype /pc for dna methylation combinations of pcs. ∗ indicates p-values < x 10− p-values were computed using t-tests for the microarray measurements between genotype and dna methylation for independent sets of randomly sampled snps. each of the sets contained at least as many snps as are included in table nsp and sty are the microarray data sets used in the study. sty and sty correspond to the pc for genotype / pc for dna methylation and pc for genotype / pc for dna methylation combinations of pcs. p-values, adjusted by the bh criterion, of < were regarded as significant. structure prediction of the proteins associated with selected genes although we selected genes with genotype-specific dna methylation, for therapeutic purposes, we need to design drugs for the proteins that are encoded by these genes. to identify candidate drugs computationally, the tertiary structures of the target proteins are required as templates. however, the structures of many of the encoded proteins have not been reported. to obtain the tertiary structure of these proteins, we used two protein structure prediction servers fams and phyre to predict the structure using only the amino acid sequence of the protein that were used to predict the tertiary structures of the proteins). the results of the protein structure predictions are summarized in additional file some protein structures were already in the protein data bank, if not, they were modeled using the structure of a suitable reference protein. these structures were then used as templates to predict drug candidates in silico. for the proteins that were not in the pdb, for the reference proteins that were used for the structure prediction, we sought cancer-related papers that cited the reference proteins. the references to these papers are listed in additional file most of reference proteins used for structure prediction were cancer-related. this finding also suggests that our gene selection process and protein structure prediction are plausible. a summary of the entire of gene selection processes is illustrated in figure in silico drug discovery we tried to design drugs that could bind to some of the protein templates using an in silico drug discovery method in which chemical compounds that potentially bind to proteins and suppress protein functions were sought computationally. for this purpose, we selected the three proteins encoded by alk, egln and nuak as drug targets, based upon a literature search and the gene annotations that indicated that these genes were expressed in cancer and had potentially functional binding pockets for ligands. details of the annotations are listed in additional file the drug discovery process that we used is illustrated in figure. after the fpascores were estimated, to check if three independent trials were feasible, we tested coincidence between three trials in two ways. first, we computed the correlation coefficients between three independent trials. for all pairwise computations for alk, egln and nuak the correlation coefficients were greater than. this suggests that the frascores computed by chooseld were highly reproducible.. however, the correlation coefficients represent the overall reproducibilities of fpascores for the candidate drug compounds. it is more important that the compounds with higher fpascores, i.e., those regarded as being highly reliable, were reproducible. therefore, we checked how often the highly ranked compounds were selected between the three trials and found that the selection of the highly ranked compounds was also highly reproducible. the ranking of the tested compounds based on their fpascores are available as additional file the results are summarized in table among the top-ranked compounds for alk, eight compounds targeted cancer genes, and two out of the eight targeted alk. among the top-ranked compounds for elgn including fe as a ligand, eight compounds targeted cancer genes and two out of the eight targeted egln which is paralog of egln among the top-ranked compounds for elgn without including fe as a ligand but as a mediator, six were in common with the top-ranked compounds for egln when fe was included as a ligand. among the other four compounds, one targeted egln of the of the top-ranked compounds for nuak most target more than other genes and thus lack specificity. for a more detailed discussion about top ranked compounds for alk, egln and nuak see additional file all of these findings suggested that the top-ranked compounds for each of the proteins were feasible candidate drugs. the compounds were ranked based on fpascores averaged over three independent trials and their representative target cancer genes. for the full lists of ranked compounds and a detailed discussion of the target cancer genes listed here, see additional files and respectively. alk and egln a paralog of egln are in bold letters. "―" indicates that no known cancer-associated genes are targeted by these compounds.  in this paper, we investigated genotype-specific dna methylation in esophageal squamous cell carcinoma, using principal component analysis. we identified more than genotype-specific dna methylation snps associated with the disease. among genotype-specific dna methylation associated genes, were associated with cancers using the gendoo server. the structures of proteins encoded by selected genotype-specific dna methylation associated genes were predicted successfully using two profile based methods, fams and phyre candidate drug compounds were screened using the tanimoto index from drugbank and were evaluated by chooseld for three selected proteins, alk, egln and nuak the selected drug candidates were promising starting points for future studies. authors' contributions mi, hu, and yht planned the research study. rk and yht performed the gene screening. mi and hu performed the protein structure prediction using fams and in silico drug discovery using chooseld. mi, hu, and yht wrote the paper. supplementary material additional file 1 annotation of selected snps. annotation information of selected snps including a list of genes associated with each snp. click here for file additional file 2 genes with significant genotype-specific dna methylation. nsp and sty are the microarray data sets used in the study. sty and sty correspond to the pc for genotype /pc for dna methylation and the pc for genotype /pc for dna methylation combinations of pcs, respectively. the genes indicated in bold letters were associated with at least one cancer-related disease due to gendoo. click here for file additional file 3 association of cancer related diseases with genes based on the gendoo server. list of cancer-related diseases associated with the genes indicated in bold letters in additional file based on the gendoo server. associations with "esophageal neoplasms" and "carcinoma, squamous cell" are highlighted. click here for file additional file 4 list of references that report the association of the selected genes with cancer-related diseases. the selected genes are those listed in additional file the list also includes the performance of the protein structure prediction and the references that associate the proteins that were used as reference proteins to predict protein structure with cancer-related diseases. click here for file additional file 5 amino acid sequences for the proteins encoded by the selected genes. the amino acid sequences were used for protein structure predictions and are listed in fasta format. click here for file additional file 6 pearson correlation coefficients and scatter plots between independent trials for fpascore computation. alk; egln with fe. egln without fe. nuak scatter plots are shown for reference. red diagonal lines indicate that fpascores were identical between two trials. click here for file additional file 7 number of compounds selected in common in three trials as highly feasible drug candidate compounds. the number of common selections from among the top k ranked compounds in three trials. black solid line indicates the expected number of compounds to be selected in common between three trials when the three trials are not correlated at all, see methods for details). blue straight line indicates a complete match between the three trials. alk. egln with fe. egln without fe. nuak it is clear that the number of selections in common is much less than would be expected for random selections, n and is very close to a complete match. click here for file additional file 8 full list of ranked compounds. list of compounds ranked based on the fpascores averaged over three independent trials, for alk, egln, and nuak click here for file additional file 9 detailed discussion of the top compounds listed in table target protein information and protein inhibition information were taken from drugbank and chembl. click here for file acknowledgements we would like to thank dr. katsuichiro komatsu who helped with the in silico drug screening using chooseld. declaration this research was funded by kakenhi, and chuo university joint research grant. this article has been published as part of bmc systems biology volume supplement 2014: selected articles from the twelfth asia pacific bioinformatics conference: systems biology. the full contents of the supplement are available online at http://www.biomedcentral.com/bmcsystbiol/supplements/8/s 
thus, genes with genotype-specific dna methylation patterns will be good therapeutic candidates. we detected genes with genotype-specific dna methylation in squamous cell carcinoma that are candidate drug targets. using in silico drug discovery, we successfully identified several candidate drugs for the alk, egln and nuak genes that displayed genotype-specific dna methylation. genotypedna methylationprincipal component analysisprotein tertiary structurecancerin silico drug discoverygene selection17- january the twelfth asia pacific bioinformatics conference shanghai, china the recently proposed principal component analysis based selection of genes with aberrant dna methylation was applied to genotype and dna methylation patterns in squamous cell carcinoma measured using single nucleotide polymorphism arrays. aberrant dna methylation is often associated with cancers. thus, screening genes with cancer-associated aberrant dna methylation is a useful method to identify candidate cancer-causing genes. aberrant dna methylation is also genotype dependent. thus, the selection of genes with genotype-specific aberrant dna methylation in cancers is potentially important for tailor-made medicine. the tertiary structures of the proteins encoded by the selected genes were successfully inferred using two profile-based protein structure servers, fams and phyre candidate drugs for three of these proteins, tyrosine kinase receptor, egln protein, and nuak family snf1-like kinase, were identified by chooseld.
 aberrant dna methylation is often associated with cancers. thus, screening genes with cancer-associated aberrant dna methylation is a useful method to identify candidate cancer-causing genes. aberrant dna methylation is also genotype dependent. thus, the selection of genes with genotype-specific aberrant dna methylation in cancers is potentially important for tailor-made medicine. the selected genes are important candidate drug targets.  the recently proposed principal component analysis based selection of genes with aberrant dna methylation was applied to genotype and dna methylation patterns in squamous cell carcinoma measured using single nucleotide polymorphism arrays. snps that are frequently found in cancers are usually highly methylated, and the genes that were selected using this method were reported previously to be related to cancers. thus, genes with genotype-specific dna methylation patterns will be good therapeutic candidates. the tertiary structures of the proteins encoded by the selected genes were successfully inferred using two profile-based protein structure servers, fams and phyre candidate drugs for three of these proteins, tyrosine kinase receptor, egln protein, and nuak family snf1-like kinase, were identified by chooseld.  we detected genes with genotype-specific dna methylation in squamous cell carcinoma that are candidate drug targets. using in silico drug discovery, we successfully identified several candidate drugs for the alk, egln and nuak genes that displayed genotype-specific dna methylation. genotypedna methylationprincipal component analysisprotein tertiary structurecancerin silico drug discoverygene selection17- january the twelfth asia pacific bioinformatics conference shanghai, china 