cancer genomics has gone through a dramatic period of progress due to the availability of genome - wide technologies . ( tcga , https://cancergenome.nih.gov/ ) and the  international cancer genome consortium  ( icgc , http://icgc.org/ ) , involve thousands of patients and have generated petabytes of data . one of the major assets of such projects is the public availability of the data allowing their integration with data from other initiatives . in that way , data from these initiatives can push a more focused and deeper analysis either in a specific gene set or in a specific cohort of patients / samples . the human surfaceome , the collection of cell surface proteins in human cells , has been defined and studied by us previously . by using bioinformatics pipeline and an experimental approach based either on real - time pcr or on other gene expression technologies , we were able to identify potential new biomarkers for few tumor types and have characterized new cell surface putative cancer - testis ( ct ) antigens [ 1 , 2 ] . relevant roles of surface proteins include nutrient and ion transport , adhesion to substrates , signaling , and intercellular interaction . due to these roles and their subcellular localization , easily accessible to therapeutic agents , surface proteins are important targets for cancer intervention . since our original publication few reports have further explored the human surfaceome [ 26 ] , mostly in the context of a mass - spectrometry - based characterization of the cell surface of tumor cells . data from tcga / icgc allow the development of new metrics that evaluate the frequency of gene alterations in different cancer types . recently , we developed a new scoring system for the identification and prioritization of cancer genes . the s - score method integrates information derived from different  omics  technologies to generate a gene - specific score that indicates whether that specific gene is a tumor suppressor ( negative s - score ) or an oncogene ( positive s - score ) . the numerical value indicates the frequency in which that gene is altered in the cohort of samples used in the calculation . we have used the s - score metric to identify cancer genes in a set of human homologs of yeast genes characterized as suppressors of genome instability in yeast . the availability of the s - score system provides a quantitative way to identify and prioritize cancer genes in a particular set of samples . here , we capitalize on the availability of data from the tcga project to further and deeper investigate the status of the human surfaceome in 15 tumor types , including gbm and colorectal and breast tumors , all analyzed in our previous publications [ 1 , 2 ] . this generated a pan - cancer landscape of the human surfaceome with the identification of shared and tumor - specific markers . furthermore , the use of the s - score system allowed us to identify gene signatures associated with overall survival in breast cancer patients . these signatures can be ultimately used in the development of new and more efficient diagnostic and therapeutic protocols . the protein - coding sequences of all human genes were obtained from the ncbi refseq ( release 64 ) . the illumina human bodymap 2.0 dataset was obtained from embl - ebi expression atlas ( https://www.ebi.ac.uk/gxa/experiments/e-mtab-513 ) . tcga data was retrieved from both , the tcga web site ( https://cancergenome.nih.gov/ ) and the cbio cancer genomics portal ( http://www.cbioportal.org/ ) . to predict plasma membrane subcellular localization , the ncbi reference sequence dataset was submitted to tmhmm   version 2.0 ( http://www.cbs.dtu.dk/services/tmhmm/ ) . all sequences containing at least one tm domain were selected . to avoid false positives , sequences containing only one tm domain in the first 50 residues , which could be a signal peptide , were excluded and classified as secreted protein . furthermore , sequences were also filtered based on the identification of signal peptide cleavage sites by signalp , release 4.1 ( http://www.cbs.dtu.dk/services/signalp/ ) . since tm domains are not exclusive to cell surface proteins , the sequences were grouped according to subcellular localization as defined by gene ontology . we excluded sequences that were exclusively located at the following cellular compartments : lysosome , endoplasmic reticulum , mitochondria , cytoskeleton , endosome , liposome , nucleolus , nucleus , and ribosome . this step was conducted using in - house perl scripts . to validate the obtained list of surfaceome genes , we classified these genes as belonging to the following classes : g - protein - coupled receptors ( gpcrs ) , solute carrier ( slc ) proteins , and cluster of differentiation ( cd ) antigens . the gpcr genes were obtained from gpcrdb ( http://gpcrdb.org ) , while cd and slc genes were collected from hgnc ( http://www.genenames.org/genefamilies/a-z#r ) . s - scores were calculated for the human surfaceome and for the 15 tumor types as previously defined . the distribution of s - scores was used to calculate z - scores for all genes using r statistical package . the go enrichment analyses of the surfaceome gene cluster were conducted using clusterprofiler , implemented in r , with p values < 0.01 as a cutoff . genes with extreme s - score ( s - score < 2 and > 2 for breast tumors ) were selected to test any putative association with overall survival in breast cancer samples derived from tcga ( without subtype distinction ) . the  altered  set comprised samples within which the respective genes were differentially expressed ( z - score > 2 or z - score < 2 , as reported by tcga ) , amplified or deleted , or presenting deleterious mutations ( nonsense , frameshift , and splice - site ) . after that , for each gene , the survival analysis was performed using the kaplan - meier method   and the difference in survival curves was tested for statistical significance using the log rank test p value . we then selected a nonredundant set of 20 genes with the lowest p value ( cutoff of 0.05 ) and tested all possible groups of three genes . first of all , we decided to reannotate the set of human genes coding for putative cell surface proteins . this was required due to ( i ) an improvement in the annotation of gene and protein databases regarding a protein 's subcellular localization and ( ii ) the inclusion of new human genes in the reference sequence collection . the same approach used by da cunha et al . ( 2009 )   generated now a set of 3,758 human genes , composing the human surfaceome ( figure 1 ) . the complete list of human genes coding for cell surface proteins is provided as supplementary table  s1 , in supplementary material available online at http://dx.doi.org/10.1155/2016/8346198 . as expected , the great majority ( 85% ) of surfaceome genes present in our dataset in 2009 remained classified as such in 2016 . new genes were added ( 585 ) , mostly due to their inclusion in the reference sequence collection and some other genes ( 529 ) were excluded due mainly to new functional annotation that classified their protein products as belonging to other subcellular compartments . to assess the robustness of our approach , we performed the same analysis reported by us in our original 2009 paper   checking the representation of three known families of cell surface proteins ( g - protein - coupled receptors ( gpcrs ) , solute carrier ( slc ) proteins and cluster of differentiation ( cd ) antigens ) . since these are large and well - studied families of cell surface proteins , we envisaged that they would be appropriate for a benchmark analysis . for gpcrs , 98% of their known members were represented in our dataset . for slc proteins and cd antigens we found 77% and 88% represented in the surfaceome set , respectively . overall , 90% of members of these three families were represented in our present surfaceome set , compared to 83% in our previous analysis . this improvement is expected due to a better annotation of the sequences in public databases . capitalizing on the availability of surfaceome sets derived from mass - spectrometry analysis , we decided to compare our dataset to the dataset from bausch - fluck et al . . although this type of comparison is problematic for different reasons , including ( i ) the nonexhaustive nature of the wet - based approach ( due to the method itself and the samples screened ) and ( ii ) the different premises of both methods ( the requirement of at least one tm domain per protein in our pipeline and the lack of such requirement in   which allowed the authors to characterize gpi - anchored proteins , e.g. ) , the analysis may be illuminating in the sense that it can highlight important differences in both methodologies . we found that 66.6% ( 664 out of 996 ) of the proteins classified by bausch - fluck et al . were present in our dataset while only 17.6% ( 664 out of 3758 ) of our proteins were present in their dataset . this was expected due to the issues raised above . to illustrate the complex nature of this comparison , 23.8% of all cell surface proteins found in   have no tm domain , as identified by tmhmm . next , the s - score method was used to identify cancer genes within the surfaceome set . s - score threshold was defined for each tumor type as the s - score representing the average s - score plus / minus three standard deviations ( z - score  3 or 3 ) . the list of all cancer genes coding for cell surface proteins in all 15 tumors types is shown in supplementary table  s1 . using the above z - score threshold , we found 248 surfaceome genes classified as a cancer gene in at least one tumor type . in the heatmap representation of the surfaceome cancer genes ( figure 2(a ) ) we can clearly identify three distinct clusters based on the s - score values for all 15 tumor types . although all three groups have a variety of oncogenes and suppressors , some features deserve further comments . for example , the first group is mainly composed of suppressors , especially in melanoma and colorectal and lung adenocarcinoma and uterine corpus endometrial carcinoma . genes in this group include several members of the cadherin superfamily ( pcdhgb3 , pcdha2 , pcdha7 , pcdh15 , pcdhgb5 , pcdh11x , pcdhac1 , fat1 , fat2 , and fat4 ) . there is a set of oncogenes in group 2 shared by almost all tumors and involving 30 genes , including ephb1 and ephb3 . there is no clear pattern in group 3 and oncogenes and suppressors seem to be distributed evenly across all tumors . to better understand the pattern presented in figure 2(a ) , we performed a gene ontology ( go ) enrichment analysis ( using the  biological process ontology ) for the three different clusters . as expected , all three groups shared go categories associated with the cell surface such as transmembrane transport and cell surface receptor signaling pathway . more interestingly , however , is the fact that specific go categories were enriched in individual groups ( figure 2(b ) ) . go categories exclusively found in group 1 were clearly associated with nervous system including  neuromuscular process  ;  memory  ; and  neuronal action potential . the same pattern was observed for group 2 although the go categories represented different aspects of nervous system including the following :  sensory perception of pain  and other categories related to axonogenesis . regarding group 3 , go analysis lent further support for the current concept of ion transport associated with cancer , including  manganese ion transport . additionally , this group presented genes related to antigen processing and presentation , highlighting that the interplay between immune and tumor cells is complex . several of the identified surfaceome cancer genes are known for their involvement in different aspects of cancer biology , especially the ones classified in group 2 . abcc5 , a cell surface transporter , was involved in resistance to anticancer drugs   and overexpression of atp11b has been linked to drug resistance in ovarian cancer . both genes were regarded as oncogenic by our work especially in lung squamous cell carcinoma and ovarian cancer . on the other hand , ephb3 has already been suggested as a candidate target gene for both lung small cell carcinoma   and colorectal cancer . finally , a transferrin receptor ( tfrc ) has shown an increased expression in many malignant tumors   and was also found to be highly oncogenic in this work . as previously discussed by us , the s - score method allows the prioritization of cancer genes based on clinical parameters . for example , we have identified genes associated with both short- and long - term survival in ovarian cancer . to test whether we could identify genes in the surfaceome set associated with clinical parameters , we decided to look at overall survival in breast tumors , since this type of tumor is the one with the largest cohort in tcga . for this specific analysis a more relaxed threshold ( z - score < 2 or > 2 ) was used to classify a gene as a cancer gene in breast tumor to increase the number of genes under test without compromising the quality of the classification ( a heatmap , similar to figure 2(a ) and generated using the dataset with a more relaxed threshold , is presented in supplementary figure  1 ) . for each surfaceome gene classified as oncogene or suppressor in breast tumor , we split the breast cancer samples into two groups : altered ( genes with differential expression , genes amplified / deleted , or genes mutated ) and unaltered . for each gene , the two groups were then compared by a kaplan - meier analysis to evaluate whether they had significantly different overall survival . twenty - three genes , seven oncogenes and 16 suppressors , were significantly ( p - value < 0.05 ) associated with differences in overall survival in breast cancer patients ( supplementary table  2 ) . these genes are involved in cell adhesion and ion transport , two of the main categories enriched in our gene ontology analysis . next , all possible combinations of these genes were similarly tested for differences in overall survival . although we found several combinations with statistically significant differences in overall survival , we have focused on wnt5a , cnga2 , and igsf9b due to statistical significance ( it is the most significant three - gene set in supplementary table  2 ) and novelty . patients in which one of the three genes was altered had a significantly shorter survival ( p value = 1.82e)compared to patients where these three genes were unaltered ( figure 3 ) . the wnt5a , cnga2 , and igsf9b genes have negative s - scores in breast cancer ( 2.59 , 3.39 , and 2.56 , resp . ) , demonstrating a tumor suppressor profile . , the loss of wnt5a has been associated with poor prognosis , in agreement with the suppressor status defined by the respective s - score . on the other hand , wnt5a was recently reported to promote cancer cell lines invasion and proliferation , a feature typical of oncogenes . wnt5a is present in pathways where wnt signaling is involved through interaction with frizzleds ( fzd10 , e.g. ) and dishevelled . cnga2 , a homotetrameric channel in olfactory sensory neurons , has not been reported in association with cancer . however , cnga2 represents the alpha subunit of a cyclic nucleotide - gated olfactory channel possessing a role in calcium signaling pathway acting through calmodulin - like 6 and calcium / calmodulin - dependent protein kinase iv   directly involved with protein kinase a ( pka ) , a biological target in cancer therapy . igsf9b was only recently identified as an inhibitory synaptic adhesion molecule   and no link with cancer was found in the literature . we have updated the set of human genes coding for cell surface proteins , the human surfaceome . using tcga data for 15 tumor types and a new method of cancer genes classification that integrates information from different  omics  technologies and allows a ranking based on clinical parameters ( s - score ) , we identified several potential therapeutic targets within the surfaceome set . furthermore , we present evidence that a three - gene set  wnt5a , cnga2 and igsf9b  was associated with shorter survival in breast cancer patients . our results clearly show the importance of large - scale genomics datasets from cancer patient cohorts , like the one provided by tcga . we envisage that the data we provide here will be extremely useful to researchers who aim to characterize cell surface targets for a variety of tumor types .
it is estimated that 10 to 20% of all genes in the human genome encode cell surface proteins and due to their subcellular localization these proteins represent excellent targets for cancer diagnosis and therapeutics . 
 therefore , a precise characterization of the surfaceome set in different types of tumor is needed . 
 using tcga data from 15 different tumor types and a new method to identify cancer genes , the s - score , we identified several potential therapeutic targets within the surfaceome set . 
 this allowed us to expand a previous analysis from us and provided a clear characterization of the human surfaceome in the tumor landscape . 
 moreover , we present evidence that a three - gene set  wnt5a , cnga2 , and igsf9b  can be used as a signature associated with shorter survival in breast cancer patients . 
 the data made available here will help the community to develop more efficient diagnostic and therapeutic tools for a variety of tumor types .