BACKGROUND
osteosarcoma  is the most common malignant bone tumor in children and adolescents, and accounts for approximately 60% of bone cancers in the first two decades of life  <cit> .
despite intensive research in the pathogenesis of this cancer, the outcome of os patients has not significantly improved over the past three decades.
the main reason for the lack of survival improvement is that this cancer is highly prone to metastasis, which is the most consistent indicator of poor outcome.
even with the most current multi-drug treatment protocols, the survival rate for patients with metastatic disease at diagnosis is only about 20%  <cit> .
similar to other types of cancer, metastasis in os is a complex process  <cit>  and a single-level of analysis alone, such as mrna expression profiling, cannot capture the complete information to fully understand the metastatic mechanism.
therefore, a systems biology approach that takes into account different data sources such as gene and protein expression profiles is more likely to identify the dysfunctional molecules and pathways of cancer biology.
nonetheless, previous studies have revealed that direct comparisons of transcriptomic and proteomic data are difficult  <cit> .
major sources of discordance between the two types of omic data exist, such as mrna degradation, alternative splicing, translational regulation, post-translational modifications, and protein degradation.
this suggests that a new analytical approach is needed to identify biological pathways that are hidden from the direct analyses but commonly supported by various data sources.
the goal of our study is to test if we could identify common metastatic processes or pathways that are jointly supported by both mrna and protein profiling data in os using a topological significance analysis to identify the hidden nodes.
due to the low incidence and limited amounts of biopsy materials, clinical os samples available for research are particularly scarce, making it extremely difficult to analyze the tumor samples using multiple genomic and proteomic platforms  <cit> .
to circumvent this problem, isogenic metastatic cell lines have been developed for os research.
however, many of these cell lines have different genetic origins and have not been systematically characterized at the genomic or proteomic level  <cit> .
therefore, in this study we applied a systems biology approach to analyze, integrate, and identify hidden common functional pathways from two commonly used human metastatic os cell lines and their parental non-metastatic lines.
the two human metastatic os cell line models were hos/143b and saos-2/lm <dig>  the hos cell line, originally known as m.t.
and later as te- <dig>  was derived from an os of a  <dig> year-old girl.
the 143b metastatic subline was generated from hos by a ki-ras oncogene transformation  <cit> .
on the other hand, the saos- <dig> cell was derived from an os of an  <dig> year-old girl, and its metastatic subline lm <dig>  was developed by multiple in vivo selection of saos- <dig> cells in mice with pulmonary metastases  <cit> .
our systems biology approach involved the analysis of the transcriptomes and glycoproteomes of the two pairs of cell lines.
we validated our results using western blotting and reverse phase protein arrays , and the clinical significance of our findings are discussed.
RESULTS
to understand the common dysregulated processes in metastatic os, we employed a systems biology approach to characterize the transcriptomes and proteomes of two commonly used human metastatic os cell line models.
the two models used were hos and saos- <dig> and their isogenic metastatic sublines, 143b and lm <dig> respectively  <cit> .
mrna expression data
mrna expression microarray analysis was performed on both models, and the differentially expressed genes within each cell line model were identified by comparing the metastatic versus non-metastatic cell lines.
the differentially expressed genes were identified using two criteria: a p-value of differential expression less than  <dig>  and a fold change between the two classes greater than 2-fold.
we identified  <dig>  up-regulated genes and  <dig>  down-regulated genes in the 143b cell relative to the hos cell, and  <dig> up-regulated genes and  <dig> down-regulated genes in the lm <dig> cell relative to the saos- <dig> cell .
the two models had  <dig> common features in the up-regulated gene profiles and  <dig> of the down-regulated genes.
by comparing to the lm7/saos- <dig> results, which constitute smaller numbers of differentially expressed genes, the common features represent 16% of up-regulated genes and 21% of down-regulated genes.
the overall level of concordance for all differentially regulated genes between the two models was 19% of the differentially expressed genes in the lm7/saos- <dig> model.
to identify the pathways that are enriched in the mrna expression data, the differentially regulated genes from the cell line models were analyzed by calculating enrichment in functional pathways.
the most significant pathways from the hos/143b model were associated with important biological functions such as cell cycle regulation and g-protein signaling in the up-regulated genes, and cell adhesion and immune response in the down-regulated genes.
for the saos-2/lm <dig> model, the most significant pathways were associated with development and cell adhesion in the up-regulated genes, and muscle contraction and cytoskeleton remodeling in the down-regulated genes.
by analyzing all significant pathways, we found only one common pathway, the “development_wnt signaling pathway”, in the up-regulated genes between the two models .
similarly, there were  <dig> common pathways in the down-regulated genes, namely the “muscle contraction_gpcrs in the regulation of smooth muscle tone”, “cytoskeleton remodeling_regulation of actin cytoskeleton by rho gtpases”, and “development_mag-dependent inhibition of neurite outgrowth” .
glycogenes expression and pathway analysis
since there is no single platform that can characterize the whole proteome in the cell and many of the clinically applicable tumor biomarkers, such as prostate-specific antigen  and carcinoembryonic antigen , are glycoproteins  <cit> , we tested if glycosylation was aberrantly regulated in the human metastatic cell line models.
the mrna expression data of  <dig> known human glycogenes in the metastatic and non-metastatic cell lines were investigated.
this panel of glycogenes represents a comprehensive group of known human genes related to glycosylation and glycan synthesis pathways  <cit> .
the analysis showed that  <dig> and  <dig> glycogenes had a p-value of differential expression less than  <dig>  and a fold change greater than 2-fold in the hos/143b and saos-2/lm <dig> models, respectively.
pathway analysis using all  <dig> glycogenes identified the baseline pathways involved in these glycogenes.
the top significant baseline pathways are o-glycan biosynthesis, neolacto-series glycosphingolipids metabolism, and keratan sulfate metabolism .
then, the pathway analysis was performed on the differentially regulated glycogenes from both cell line models and the results were compared to the baseline glyco-pathways.
results showed that “n-glycan biosynthesis” was the top common pathway between the two models .
these results suggest that the n-linked glycosylation may be involved in the metastatic process of os.
analysis of the n-linked glycoproteome will likely provide important information in metastatic os.
glycoproteomic analysis by a lectin column followed by multidimensional protein identification technology
next, we used an affinity-based mass spectrometry approach to identify the differentially expressed glycoproteins in the two cell line models.
first, we used wheat germ agglutinin  lectin chromatography to selectively capture n-linked glycoproteins .
subsequently, mudpit analysis was performed on the captured glycoproteins from the two pairs of human metastatic os cell line models.
totally,  <dig>  spectra were detected in the hos/143b model, and  <dig>  spectra in the saos-2/lm <dig> model.
these spectra corresponded to  <dig>  and  <dig>  unique peptides, which gave rise to  <dig> and  <dig> unique proteins identified in the hos/143b and saos-2/lm <dig> models, respectively.
the protein identification criteria were  at least two unique peptides, and  higher than 99% confidence of protein identification.
to measure the expressions or abundances of the glycoproteins in the metastatic os cell line models, a spectral counting method was employed .
spectral counting entails averaging the spectrum counts across the different protein samples tested, followed by calculating the quantitative value for each identified protein.
the quantitative values of the identified proteins in the metastatic versus non-metastatic cell lines were then compared and their respective fold change was calculated.
then, the p-value for the differential expression of each protein between the two classes was calculated by fisher’s exact test  <cit>  and corrected for multiple testing using a step-up method  <cit> .
proteins with a corrected p-value less than  <dig>  and a greater than 2-fold expression were considered significantly differentially expressed.
the analysis showed that  <dig> and  <dig> glycoproteins were differentially regulated in the hos/143b and saos-2/lm <dig> models, respectively.
<dig> glycoproteins were up-regulated and  <dig> glycoproteins down-regulated in the hos/143b model ; and  <dig> glycoproteins were up-regulated and  <dig> glycoproteins were down-regulated in the saos-2/lm <dig> model .
the models had  <dig> common proteins in the up-regulated glycoprotein profiles and  <dig> common proteins in the down-regulated glycoprotein profiles.
by comparing to the results of lm7/saos- <dig>  the common differentially expressed proteins represent 11% of up-regulated glycoproteins and 6% of down-regulated glycoproteins.
the overall level of concordance for all differentially regulated glycoproteins between the two models was 9% of the differently expressed glycoproteins in the lm7/saos- <dig> model.
canx
gene
to show that the identified proteins were likely to be glycosylated, the top  <dig> up-regulated proteins from each cell line model were analyzed using a post-translational modification prediction algorithm.
the selected proteins were modeled using net-n-glyc  <cit>  for prediction of n-glycosylation sites in human proteins.
the threshold was established at greater than 50% potential of glycosylation for each identified site.
results showed that 70% and 75% of the top proteins in hos/143b and saos-2/lm <dig>  respectively, have asparagines predicted to be n-glycosylated.
up-regulated proteins in each model included 14-3- <dig> protein theta and alpha enolase from hos/143b, and nestin and vimentin from saos-2/lm <dig>  all of which have been implicated in cancer progression and metastasis  <cit> .
nonetheless, when the differentially regulated proteins were interrogated by the pathway analysis, only one common significant pathway, the “immune response_antigen presentation by mhc class i”, was found in the up-regulated proteins .
in the down-regulated proteins there were  <dig> common significant pathways, namely the “proteolysis_role of parikin in the ubiquitin-proteasomal pathway”, “cytoskeleton remodeling_keratin filaments”, and “regulation of cftr activity”.
notably, none of these pathways was common at both the mrna and protein levels in the two models.
validation of the differentially expressed glycoproteins by western blot
two candidate glycoproteins were selected for validation based on criteria that required the candidates to be among the top significantly up-regulated glycoproteins from mudpit, be supported by the transcriptomic data, and have a commercially available antibody for validation experiments.
the first candidate glycoprotein selected for validation was amidophosphoribosyltransferase , which was up-regulated by 14-fold in the metastatic 143b relative to the non-metastatic hos in the mudpit analysis.
the other candidate was vimentin, which was up-regulated by 350-fold in the metastatic lm <dig> relative to the non-metastatic saos- <dig> in the mudpit analysis.
the results confirmed that both proteins were overexpressed in the metastatic sublines compared to the parental cell line .
topological significance scoring analysis of genomic and glycoproteomic data
although the direct analyses indicated that no dysregulated pathways were common between the two cell line models at both the mrna and protein levels, we hypothesized that common metastatic processes could be uncovered by identifying the hidden nodes in the data.
we applied the “shortest-path” algorithm of topological scoring to the differentially regulated genes and glycoproteins.
this algorithm has been shown previously to be able to identify significant topological nodes with respect to the differentially expressed genes or proteins by combining the high-throughput data with the global network of protein interactions  <cit> .
these significant nodes represent a common set of signaling proteins responsible for changes in the expression of target genes and proteins.
the activity of such nodes often remains hidden from direct genomic and proteomic analyses due to posttranslational modifications, binding to second messengers, recruitment to sub-cellular compartments, or specific limitations of the approach used, such as glycoproteomics focusing on a specific subproteome  <cit> .
in our study, the identified hidden nodes may reveal common dysregulated pathways between the two models at the genomic and proteomic levels associated to the metastatic phenotype.
in the hos/143b model, the topological analysis revealed  <dig> and  <dig>  significant nodes for the up-regulated genes and down-regulated genes respectively, and  <dig> and  <dig> nodes for the up-regulated proteins and down-regulated proteins respectively.
for the saos-2/lm <dig> model, there are  <dig> and  <dig> significant nodes for the up-regulated genes and down-regulated genes respectively, and  <dig> and  <dig> significant nodes for up-regulated proteins and down-regulated proteins respectively.
the numbers of common genes between the models for the gene topological profiles were  <dig> for up-regulated genes and  <dig> for down-regulated genes.
for the protein topological sets, there are  <dig> common proteins for the up-regulated proteins and  <dig> for the down-regulated proteins.
when compared to the direct analyses, the level of concordance in the differentially regulated molecules between the two models increased in both, the genomic profiles  and proteomic profiles  following the topological analysis.
additionally, the majority of the topological significant nodes identified were hidden molecules that were not present in the differentially regulated genes and proteins sets.
in the transcriptomic profiles, 85% and 95% of the topological nodes from hos/143b and lm7/saos <dig> respectively, represent hidden molecules that were not identified by the direct mrna analysis.
similarly, in the proteomic profiles 93% and 91% of the topological nodes represent hidden molecules in the hos/143b and lm7/saos <dig>  respectively.
significant topological nodes that had been already identified by the direct transcriptomic analysis, and were therefore not hidden, include molecules such as chemokine , oncogenes suppressor , growth factor receptor , and downstream wnt signaling factor .
conversely, topological nodes that were not hidden to the direct proteomic analysis include factors involved in cell division cycle , eukaryotic translation elongation , and interleukin enhancement .
on the other hand, the topological nodes that remained hidden to the direct transcriptomic and proteomic analyses include a much larger number of molecules, many of which also belong to important cancer associated families such as smad , fox , gata , and mmp .
these hidden nodes may carry additional information of functional processes and pathways that were unidentifiable in the direct analyses of differentially regulated genes and glycoproteins.
therefore, after the statistically significant nodes were identified, they were further interrogated to identify the associated pathways.
in contrast to the previous pathway analysis results, the topological nodes analysis revealed a remarkable concordance between the two metastatic models.
in the top  <dig> significant pathways of the genomic data, we identified that  <dig> and  <dig> pathways were common between the  <dig> models from the topological analysis of up-regulated genes and down-regulated genes, respectively .
the common pathways from the differentially regulated genes are the “cytoskeleton remodeling/tgf/wnt”, “cytoskeleton remodeling/cytoskeleton remodeling”, “cell adhesion/chemokines and adhesion”, and “cell adhesion/ecm remodeling”.
for the proteomic data, the two models shared  <dig> common pathways for the up-regulated and none in the down-regulated proteins in the top  <dig> most significant pathways.
the common pathways from the differentially regulated glycoproteins nodes are the “cytoskeleton remodeling/tgf/wnt”, “cytoskeleton remodeling/cytoskeleton remodeling”, and “cell adhesion/chemokines and adhesion”.
together, we identified the “cytoskeleton remodeling/tgf/wnt” pathway to be the most significant common pathway of the topological analysis of the up-regulated mrna and glycoprotein profiles from both cell line models .
to test the specificity of our pathway analysis and if the common pathways could be identified by a random chance, we created  <dig> sets of  <dig> randomly selected genes .
the number of selected genes was chosen based on the topological proteomic analysis, where approximately  <dig> significant molecules were identified.
we then analyzed these gene sets in an identical fashion using the topological scoring algorithm and the pathway analysis.
if the topological significance analysis or the pathway analysis would bias certain pathways that are not biologically relevant to the dataset, these specific pathways would be frequently identified from the analysis of randomly selected genes.
the result showed that the false discovery rate  of identifying the “cytoskeleton remodeling/tgf/wnt” within the most significant pathways was 10% if the top  <dig> pathways were considered .
similarly, the fdr within the top ten pathways for the “cytoskeleton remodeling/cytoskeleton remodeling” and the “cell adhesion/chemokines and adhesion” pathways were 10% and 20%, respectively.
validation of the cytoskeleton/tgf/wnt pathway using rppa
as previously described, the cytoskeleton/tgf/wnt pathway was the top common pathways for the topological analysis of the up-regulated genes and up-regulated glycoproteins.
we used an orthogonal platform, rppa, to validate if the proteins involved in the “cytoskeleton remodeling/tgf/wnt” pathway were up-regulated when compared to proteins involved in other pathways.
protein lysates of the two models were analyzed by the rppa, which consists of antibodies for  <dig> unique proteins.
the numbers of up-regulated proteins in the rppa were  <dig> and  <dig> for hos/143b and saos-2/lm <dig>  respectively.
of the unique proteins in the rppa,  <dig> belonged to the “cytoskeleton remodeling/tgf/wnt” pathway  <dig> of these  <dig> proteins were identified by the topological analyses of up-regulated genes and proteins.
the probability of these “cytoskeleton remodeling/tgf/wnt” pathway proteins to be up-regulated relative to all  <dig> proteins in the rppa was found to be higher than expected in both, hos/143b  and saos-2/lm <dig>  models, with p-values of  <dig>  and  <dig> , respectively .
discussion
although cancer is a heterogeneous disease, accumulating evidence supports the idea that signals for malignant transformation, progression and invasion are likely affected by a multitude of signaling pathways converging on several key regulators  <cit> .
identifying these key regulators will be critical for the development of novel cancer therapeutics.
nonetheless, due to diverse genetic backgrounds of tumors and the intrinsic limitations of high-throughput profiling technologies, many of these key regulators remain hidden from conventional single-level or direct analyses.
in this study, we used a systems biology approach to reveal and identify topologically significant nodes by integrating transcriptomic and glycoproteomic data of two human metastatic os models.
direct pathway analyses of the mrna and glycoproteomic profiles revealed a low concordance of differentially expressed genes or proteins and their respective functional pathways between the models.
these results may in parts reflect the unique methodologies used to develop the cell line models.
the metastatic 143b subline was generated in vitro via a ki-ras oncogene transformation of the hos cell line  <cit> , while the lm <dig> subline was developed in vivo through successive cycles of pulmonary metastases selection and re-injections in mice  <cit> .
the different genetic background of the parental cell lines and the in vitro versus in vivo development of the respective metastatic sublines, may account for the differences observed in the genomic and glycoproteomic analysis of the models.
the discordance between the mrna and proteomics profiles was not surprising because of their known limited correlation  <cit> , and the specific subproteome characterized in this study, namely the n-linked glycoproteins with affinity for wga lectin.
these fundamental differences and the large number of discordant pathways between the cell line models, increase the difficulty of prioritizing and identifying the key common regulators in the metastatic process of os.
despite of the different origins, the two os cell line models share the same metastatic phenotype.
thus, we hypothesized that some key metastatic pathways are common to different tumors of the same cancer type, but remain hidden from the direct genomic and proteomic profiling analyses.
therefore, we performed further analysis and data integration to identify key nodes in the pathways that will reveal the critical players in common metastatic processes.
for this analysis, we applied a recently developed “topological scoring” algorithm to integrate the genomic and glycoproteomic profiles and identified significant hidden common pathways between the two os models.
the “topological scoring” algorithm scores nodes in a network built from the experimentally derived, condition-specific genomic and proteomic profiles  <cit> .
the output provides a series of signaling proteins associated with the metastatic phenotype and helps delineate the underlying biological processes.
one of the main advantages of the topological algorithm is that it assesses the relative contribution of every node in the condition-specific network under study relative to its role in the global network.
thus, the “hubs” that are commonly present in different pathways and have high connectivity in the global network, are penalized if they do not have any special role related to the experimentally derived set of differentially expressed genes or proteins.
on the other hand, nodes that provide significant connectivity among the differentially expressed genes or proteins are highly scored regardless of their global network interactions, rendering the results more specific.
pathway analysis of the identified topological nodes revealed many common and significant dysregulated pathways between the models, which were missed by the direct analyses.
remarkably, the analysis revealed pathways such as “cytoskeleton remodeling/tgf/wnt” and “cell adhesion/chemokines and adhesion”, which were common to both models and present at both mrna and protein levels.
all the processes represented in these common pathways have been previously associated with metastasis in various cancer types, and changes involved in these signaling networks associated to cytoskeleton remodeling, cell-cell and cell-matrix adhesion are critical for the cancer cell migration and invasion  <cit> .
to determine if these pathways would be identified by a random chance or nonspecifically, we performed a false discovery analysis using randomly selected genes and confirmed the specificity of these pathways in our datasets.
our results support the notion that there are common metastatic mechanisms acting downstream of different genetic aberrations or origins.
this implies the existence of common molecular therapeutic targets and disease biomarkers irrespective of the driver genetic abnormalities in the tumors.
this finding is particularly significant in tumors, such as os, in which the genomic abnormalities are known to be highly complex and it is hard to determine the driver mutations from the passengers  <cit> .
for instance, the most significantly overrepresented pathway of the topological analysis was the “cytoskeleton remodeling/tgf/wnt”, which includes the interesting wnt component.
the wnt signaling is particularly important for cancer cells as they are able to disrupt this pathway in different ways resulting in tumorigenesis and metastasis.
activation of the wnt signaling pathway is necessary for the commitment of mesenchymal stem cells to the osteoblast lineage.
in addition, aberrant wnt signaling activity has been reported in a variety of human cancers including soft tissue sarcomas and human os primary tissues and cell lines  <cit> .
abnormal activation of the canonical wnt pathway results in stabilized β-catenin that translocates into the nucleus.
subsequently, it binds to transcription factors and drives the uncontrolled expression of target genes implicated in cell proliferation, transformation, and tumor progression, such as myc, matrix metalloproteinase  <dig>  axin- <dig>  the cell adhesion molecule l1-cam, the metastasis gene s100a <dig>  and others  <cit> .
therefore, targeting the wnt signaling may have a therapeutic effect on different types of metastatic os.
similarly, our findings suggest that wnt signaling is highly relevant to the metastasis of os.
previous studies have shown that os harbors an accumulation of β-catenin either in the cytoplasm or in the nucleus  <cit> , a hallmark of wnt signaling activation.
additionally, the wnt coreceptor lrp <dig> expression in os tissue samples correlated with metastasis and a lower rate of disease-free survival in patients  <cit> .
furthermore, os cell lines have been reported to express many wnt ligands and receptors, whereas secreted wnt antagonists including secreted frizzled-related protein  and dickkopf  families are commonly absent  <cit> .
inhibition of similar mechanisms by the reintroduction of secreted wnt antagonists in os, such as the wnt inhibitory factor , has been proposed for downregulation of wnt signaling as a novel therapeutic approach.
for instance, overexpression of wif- <dig> significantly decreased tumor growth and markedly reduced the number of lung metastasis of 143b cells in vivo <cit> .
in other studies soluble lrp <dig> , which blocks wnt signaling, was able to reduce in vitro cellular invasion, and transfection of slrp <dig> in saos- <dig> caused a marked up-regulation of e-cadherin and down-regulation of n-cadherin suggesting a reversal of epithelial-mesenchymal transition  <cit> .
these findings suggest an important role for aberrant wnt signaling in the pathobiology and progression of os.
the wnt pathway may represent a promising source of novel therapeutic targets and disease progression biomarkers.
another common pathway relevant in metastasis is the “cell adhesion/chemokines and adhesion”, which was identified at both the genomic and proteomic levels.
chemokine ligands and their cognate receptors have been extensively implicated in the progression and metastasis of multiple tumors such as melanoma, breast cancer, prostate cancer, and others  <cit> .
in os, research has revealed a complex interaction between chemokine ligand/receptor axis, and their role in tumor invasion, metastasis and patient prognosis  <cit> .
our group previously reported that expression of two cxc chemokines were elevated in tumor and plasma of pediatric os patients, and their levels correlated with patient outcomes  <cit> .
several of the cxc chemokines were also identified by the topological analysis in this study, including cxcl <dig> which has been directly associated to metastasis in os and other tumors  <cit> .
identification of the significant topological nodes within such complex networks could help identify the key players and delineate their roles in cell adhesion, invasion and other important metastasis functions.
in addition to the identification of therapeutic targets, identification of tumor-derived biomarkers for early detection of disease progression and metastasis is a critical component for personalized medicine.
as previously noted, characterization of key hidden nodes could facilitate the identification of candidate biomarkers for metastatic os.
because of the diversity of the human proteome and limitations of the current proteomic methods, targeting specific subproteomes that are likely to be secreted into the blood stream, such as the glycoproteome, will improve the likelihood of identifying tumor-derived circulating biomarkers.
in this study, we characterized the n-linked glycoproteome due to their involvement in metastatic os as evidenced by the glycogene analysis, and their frequent localization in the cellular membrane and extracellular space.
in spite of the limited number of glycoproteins identified in this study, the topological analysis revealed common significant pathways between the differentially regulated glycoproteins and genes in both os models.
using this analysis, we can prioritize the up-regulated glycoproteins and up-regulated genes as well as the hidden molecules that may serve as biomarkers for the disease or metastasis in the future validation, such as proteins in the tgf beta  and map kinase  families identified by topological analysis.
despite the encouraging results, we recognize that there are limitations in the current study.
for instance, the cell line models used in our study represents a significant limitation.
although the in vitro models provide a convenient and renewable platform that cannot be surpassed by clinical specimens, it may not faithfully reflect the behavior of tumor cells in vivo.
therefore, future directions include the development of orthotopic xenograft mouse models to recapitulate and validate the results obtained in this study.
in addition, the characterized n-linked glycoproteins with an affinity for the wga lectin represent a restricted number of proteins, which is typical to this type of subproteome analysis  <cit> .
this limits the number of identifiable up-regulated glycoproteins amenable for validation as candidate biomarkers.
further studies with a wider range of proteins including additional glycoproteins by different capture methods   <cit>  and other relevant subproteomes, such as the secretome  <cit>  will help to alleviate this limitation.
CONCLUSIONS
we used a systems biology approach to identify key and common metastatic mechanisms in os.
a similar approach can be applied to other genetically distinct but phenotypically similar cell lines of other tumor types.
the use of the topological analysis revealed hidden biological networks known to play a fundamental role in metastasis.
the analysis will also provide new types of information in this pediatric cancer that are not identifiable by conventional single-level analyses, and present new directions for future research.
our study shows that the systems biology analysis of metastasis through different types of genomic and proteomic characterization can shed light on the key biological processes of os invasion.
our findings have significant implications on both the development of biomarkers for disease progression and metastasis, and identification of potential targets for novel therapies.
ultimately, we believe that the results reported in this study will accelerate the development of a personalized treatment for metastatic os patients to maximize their survival and decrease treatment toxicity.

 osteosarcoma is the most common malignant bone tumor in children and adolescents, and accounts for approximately 60% of bone cancers in the first two decades of life. despite intensive research in the pathogenesis of this cancer, the outcome of os patients has not significantly improved over the past three decades. the main reason for the lack of survival improvement is that this cancer is highly prone to metastasis, which is the most consistent indicator of poor outcome. even with the most current multi-drug treatment protocols, the survival rate for patients with metastatic disease at diagnosis is only about 20%. similar to other types of cancer, metastasis in os is a complex process and a single-level of analysis alone, such as mrna expression profiling, cannot capture the complete information to fully understand the metastatic mechanism. therefore, a systems biology approach that takes into account different data sources such as gene and protein expression profiles is more likely to identify the dysfunctional molecules and pathways of cancer biology. nonetheless, previous studies have revealed that direct comparisons of transcriptomic and proteomic data are difficult. major sources of discordance between the two types of omic data exist, such as mrna degradation, alternative splicing, translational regulation, post-translational modifications, and protein degradation. this suggests that a new analytical approach is needed to identify biological pathways that are hidden from the direct analyses but commonly supported by various data sources. the goal of our study is to test if we could identify common metastatic processes or pathways that are jointly supported by both mrna and protein profiling data in os using a topological significance analysis to identify the hidden nodes. due to the low incidence and limited amounts of biopsy materials, clinical os samples available for research are particularly scarce, making it extremely difficult to analyze the tumor samples using multiple genomic and proteomic platforms. to circumvent this problem, isogenic metastatic cell lines have been developed for os research. however, many of these cell lines have different genetic origins and have not been systematically characterized at the genomic or proteomic level. therefore, in this study we applied a systems biology approach to analyze, integrate, and identify hidden common functional pathways from two commonly used human metastatic os cell lines and their parental non-metastatic lines. the two human metastatic os cell line models were hos/143b and saos-2/lm the hos cell line, originally known as m.t. and later as te- was derived from an os of a year-old girl. the 143b metastatic subline was generated from hos by a ki-ras oncogene transformation. on the other hand, the saos- cell was derived from an os of an year-old girl, and its metastatic subline lm was developed by multiple in vivo selection of saos- cells in mice with pulmonary metastases. our systems biology approach involved the analysis of the transcriptomes and glycoproteomes of the two pairs of cell lines. we validated our results using western blotting and reverse phase protein arrays, and the clinical significance of our findings are discussed.  to understand the common dysregulated processes in metastatic os, we employed a systems biology approach to characterize the transcriptomes and proteomes of two commonly used human metastatic os cell line models. the two models used were hos and saos- and their isogenic metastatic sublines, 143b and lm respectively. mrna expression data mrna expression microarray analysis was performed on both models, and the differentially expressed genes within each cell line model were identified by comparing the metastatic versus non-metastatic cell lines. the differentially expressed genes were identified using two criteria: a p-value of differential expression less than and a fold change between the two classes greater than 2-fold. we identified up-regulated genes and down-regulated genes in the 143b cell relative to the hos cell, and up-regulated genes and down-regulated genes in the lm cell relative to the saos- cell. the two models had common features in the up-regulated gene profiles and of the down-regulated genes. by comparing to the lm7/saos- results, which constitute smaller numbers of differentially expressed genes, the common features represent 16% of up-regulated genes and 21% of down-regulated genes. the overall level of concordance for all differentially regulated genes between the two models was 19% of the differentially expressed genes in the lm7/saos- model. to identify the pathways that are enriched in the mrna expression data, the differentially regulated genes from the cell line models were analyzed by calculating enrichment in functional pathways. the most significant pathways from the hos/143b model were associated with important biological functions such as cell cycle regulation and g-protein signaling in the up-regulated genes, and cell adhesion and immune response in the down-regulated genes. for the saos-2/lm model, the most significant pathways were associated with development and cell adhesion in the up-regulated genes, and muscle contraction and cytoskeleton remodeling in the down-regulated genes. by analyzing all significant pathways, we found only one common pathway, the “development_wnt signaling pathway”, in the up-regulated genes between the two models. similarly, there were common pathways in the down-regulated genes, namely the “muscle contraction_gpcrs in the regulation of smooth muscle tone”, “cytoskeleton remodeling_regulation of actin cytoskeleton by rho gtpases”, and “development_mag-dependent inhibition of neurite outgrowth”. glycogenes expression and pathway analysis since there is no single platform that can characterize the whole proteome in the cell and many of the clinically applicable tumor biomarkers, such as prostate-specific antigen and carcinoembryonic antigen, are glycoproteins, we tested if glycosylation was aberrantly regulated in the human metastatic cell line models. the mrna expression data of known human glycogenes in the metastatic and non-metastatic cell lines were investigated. this panel of glycogenes represents a comprehensive group of known human genes related to glycosylation and glycan synthesis pathways. the analysis showed that and glycogenes had a p-value of differential expression less than and a fold change greater than 2-fold in the hos/143b and saos-2/lm models, respectively. pathway analysis using all glycogenes identified the baseline pathways involved in these glycogenes. the top significant baseline pathways are o-glycan biosynthesis, neolacto-series glycosphingolipids metabolism, and keratan sulfate metabolism. then, the pathway analysis was performed on the differentially regulated glycogenes from both cell line models and the results were compared to the baseline glyco-pathways. results showed that “n-glycan biosynthesis” was the top common pathway between the two models. these results suggest that the n-linked glycosylation may be involved in the metastatic process of os. analysis of the n-linked glycoproteome will likely provide important information in metastatic os. glycoproteomic analysis by a lectin column followed by multidimensional protein identification technology next, we used an affinity-based mass spectrometry approach to identify the differentially expressed glycoproteins in the two cell line models. first, we used wheat germ agglutinin lectin chromatography to selectively capture n-linked glycoproteins. subsequently, mudpit analysis was performed on the captured glycoproteins from the two pairs of human metastatic os cell line models. totally, spectra were detected in the hos/143b model, and spectra in the saos-2/lm model. these spectra corresponded to and unique peptides, which gave rise to and unique proteins identified in the hos/143b and saos-2/lm models, respectively. the protein identification criteria were at least two unique peptides, and higher than 99% confidence of protein identification. to measure the expressions or abundances of the glycoproteins in the metastatic os cell line models, a spectral counting method was employed. spectral counting entails averaging the spectrum counts across the different protein samples tested, followed by calculating the quantitative value for each identified protein. the quantitative values of the identified proteins in the metastatic versus non-metastatic cell lines were then compared and their respective fold change was calculated. then, the p-value for the differential expression of each protein between the two classes was calculated by fisher’s exact test and corrected for multiple testing using a step-up method. proteins with a corrected p-value less than and a greater than 2-fold expression were considered significantly differentially expressed. the analysis showed that and glycoproteins were differentially regulated in the hos/143b and saos-2/lm models, respectively. glycoproteins were up-regulated and glycoproteins down-regulated in the hos/143b model; and glycoproteins were up-regulated and glycoproteins were down-regulated in the saos-2/lm model. the models had common proteins in the up-regulated glycoprotein profiles and common proteins in the down-regulated glycoprotein profiles. by comparing to the results of lm7/saos- the common differentially expressed proteins represent 11% of up-regulated glycoproteins and 6% of down-regulated glycoproteins. the overall level of concordance for all differentially regulated glycoproteins between the two models was 9% of the differently expressed glycoproteins in the lm7/saos- model. canx gene to show that the identified proteins were likely to be glycosylated, the top up-regulated proteins from each cell line model were analyzed using a post-translational modification prediction algorithm. the selected proteins were modeled using net-n-glyc for prediction of n-glycosylation sites in human proteins. the threshold was established at greater than 50% potential of glycosylation for each identified site. results showed that 70% and 75% of the top proteins in hos/143b and saos-2/lm respectively, have asparagines predicted to be n-glycosylated. up-regulated proteins in each model included 14-3- protein theta and alpha enolase from hos/143b, and nestin and vimentin from saos-2/lm all of which have been implicated in cancer progression and metastasis. nonetheless, when the differentially regulated proteins were interrogated by the pathway analysis, only one common significant pathway, the “immune response_antigen presentation by mhc class i”, was found in the up-regulated proteins. in the down-regulated proteins there were common significant pathways, namely the “proteolysis_role of parikin in the ubiquitin-proteasomal pathway”, “cytoskeleton remodeling_keratin filaments”, and “regulation of cftr activity”. notably, none of these pathways was common at both the mrna and protein levels in the two models. validation of the differentially expressed glycoproteins by western blot two candidate glycoproteins were selected for validation based on criteria that required the candidates to be among the top significantly up-regulated glycoproteins from mudpit, be supported by the transcriptomic data, and have a commercially available antibody for validation experiments. the first candidate glycoprotein selected for validation was amidophosphoribosyltransferase, which was up-regulated by 14-fold in the metastatic 143b relative to the non-metastatic hos in the mudpit analysis. the other candidate was vimentin, which was up-regulated by 350-fold in the metastatic lm relative to the non-metastatic saos- in the mudpit analysis. the results confirmed that both proteins were overexpressed in the metastatic sublines compared to the parental cell line. topological significance scoring analysis of genomic and glycoproteomic data although the direct analyses indicated that no dysregulated pathways were common between the two cell line models at both the mrna and protein levels, we hypothesized that common metastatic processes could be uncovered by identifying the hidden nodes in the data. we applied the “shortest-path” algorithm of topological scoring to the differentially regulated genes and glycoproteins. this algorithm has been shown previously to be able to identify significant topological nodes with respect to the differentially expressed genes or proteins by combining the high-throughput data with the global network of protein interactions. these significant nodes represent a common set of signaling proteins responsible for changes in the expression of target genes and proteins. the activity of such nodes often remains hidden from direct genomic and proteomic analyses due to posttranslational modifications, binding to second messengers, recruitment to sub-cellular compartments, or specific limitations of the approach used, such as glycoproteomics focusing on a specific subproteome. in our study, the identified hidden nodes may reveal common dysregulated pathways between the two models at the genomic and proteomic levels associated to the metastatic phenotype. in the hos/143b model, the topological analysis revealed and significant nodes for the up-regulated genes and down-regulated genes respectively, and and nodes for the up-regulated proteins and down-regulated proteins respectively. for the saos-2/lm model, there are and significant nodes for the up-regulated genes and down-regulated genes respectively, and and significant nodes for up-regulated proteins and down-regulated proteins respectively. the numbers of common genes between the models for the gene topological profiles were for up-regulated genes and for down-regulated genes. for the protein topological sets, there are common proteins for the up-regulated proteins and for the down-regulated proteins. when compared to the direct analyses, the level of concordance in the differentially regulated molecules between the two models increased in both, the genomic profiles and proteomic profiles following the topological analysis. additionally, the majority of the topological significant nodes identified were hidden molecules that were not present in the differentially regulated genes and proteins sets. in the transcriptomic profiles, 85% and 95% of the topological nodes from hos/143b and lm7/saos respectively, represent hidden molecules that were not identified by the direct mrna analysis. similarly, in the proteomic profiles 93% and 91% of the topological nodes represent hidden molecules in the hos/143b and lm7/saos respectively. significant topological nodes that had been already identified by the direct transcriptomic analysis, and were therefore not hidden, include molecules such as chemokine, oncogenes suppressor, growth factor receptor, and downstream wnt signaling factor. conversely, topological nodes that were not hidden to the direct proteomic analysis include factors involved in cell division cycle, eukaryotic translation elongation, and interleukin enhancement. on the other hand, the topological nodes that remained hidden to the direct transcriptomic and proteomic analyses include a much larger number of molecules, many of which also belong to important cancer associated families such as smad, fox, gata, and mmp. these hidden nodes may carry additional information of functional processes and pathways that were unidentifiable in the direct analyses of differentially regulated genes and glycoproteins. therefore, after the statistically significant nodes were identified, they were further interrogated to identify the associated pathways. in contrast to the previous pathway analysis results, the topological nodes analysis revealed a remarkable concordance between the two metastatic models. in the top significant pathways of the genomic data, we identified that and pathways were common between the models from the topological analysis of up-regulated genes and down-regulated genes, respectively. the common pathways from the differentially regulated genes are the “cytoskeleton remodeling/tgf/wnt”, “cytoskeleton remodeling/cytoskeleton remodeling”, “cell adhesion/chemokines and adhesion”, and “cell adhesion/ecm remodeling”. for the proteomic data, the two models shared common pathways for the up-regulated and none in the down-regulated proteins in the top most significant pathways. the common pathways from the differentially regulated glycoproteins nodes are the “cytoskeleton remodeling/tgf/wnt”, “cytoskeleton remodeling/cytoskeleton remodeling”, and “cell adhesion/chemokines and adhesion”. together, we identified the “cytoskeleton remodeling/tgf/wnt” pathway to be the most significant common pathway of the topological analysis of the up-regulated mrna and glycoprotein profiles from both cell line models. to test the specificity of our pathway analysis and if the common pathways could be identified by a random chance, we created sets of randomly selected genes. the number of selected genes was chosen based on the topological proteomic analysis, where approximately significant molecules were identified. we then analyzed these gene sets in an identical fashion using the topological scoring algorithm and the pathway analysis. if the topological significance analysis or the pathway analysis would bias certain pathways that are not biologically relevant to the dataset, these specific pathways would be frequently identified from the analysis of randomly selected genes. the result showed that the false discovery rate of identifying the “cytoskeleton remodeling/tgf/wnt” within the most significant pathways was 10% if the top pathways were considered. similarly, the fdr within the top ten pathways for the “cytoskeleton remodeling/cytoskeleton remodeling” and the “cell adhesion/chemokines and adhesion” pathways were 10% and 20%, respectively. validation of the cytoskeleton/tgf/wnt pathway using rppa as previously described, the cytoskeleton/tgf/wnt pathway was the top common pathways for the topological analysis of the up-regulated genes and up-regulated glycoproteins. we used an orthogonal platform, rppa, to validate if the proteins involved in the “cytoskeleton remodeling/tgf/wnt” pathway were up-regulated when compared to proteins involved in other pathways. protein lysates of the two models were analyzed by the rppa, which consists of antibodies for unique proteins. the numbers of up-regulated proteins in the rppa were and for hos/143b and saos-2/lm respectively. of the unique proteins in the rppa, belonged to the “cytoskeleton remodeling/tgf/wnt” pathway of these proteins were identified by the topological analyses of up-regulated genes and proteins. the probability of these “cytoskeleton remodeling/tgf/wnt” pathway proteins to be up-regulated relative to all proteins in the rppa was found to be higher than expected in both, hos/143b and saos-2/lm models, with p-values of and, respectively. discussion although cancer is a heterogeneous disease, accumulating evidence supports the idea that signals for malignant transformation, progression and invasion are likely affected by a multitude of signaling pathways converging on several key regulators. identifying these key regulators will be critical for the development of novel cancer therapeutics. nonetheless, due to diverse genetic backgrounds of tumors and the intrinsic limitations of high-throughput profiling technologies, many of these key regulators remain hidden from conventional single-level or direct analyses. in this study, we used a systems biology approach to reveal and identify topologically significant nodes by integrating transcriptomic and glycoproteomic data of two human metastatic os models. direct pathway analyses of the mrna and glycoproteomic profiles revealed a low concordance of differentially expressed genes or proteins and their respective functional pathways between the models. these results may in parts reflect the unique methodologies used to develop the cell line models. the metastatic 143b subline was generated in vitro via a ki-ras oncogene transformation of the hos cell line, while the lm subline was developed in vivo through successive cycles of pulmonary metastases selection and re-injections in mice. the different genetic background of the parental cell lines and the in vitro versus in vivo development of the respective metastatic sublines, may account for the differences observed in the genomic and glycoproteomic analysis of the models. the discordance between the mrna and proteomics profiles was not surprising because of their known limited correlation, and the specific subproteome characterized in this study, namely the n-linked glycoproteins with affinity for wga lectin. these fundamental differences and the large number of discordant pathways between the cell line models, increase the difficulty of prioritizing and identifying the key common regulators in the metastatic process of os. despite of the different origins, the two os cell line models share the same metastatic phenotype. thus, we hypothesized that some key metastatic pathways are common to different tumors of the same cancer type, but remain hidden from the direct genomic and proteomic profiling analyses. therefore, we performed further analysis and data integration to identify key nodes in the pathways that will reveal the critical players in common metastatic processes. for this analysis, we applied a recently developed “topological scoring” algorithm to integrate the genomic and glycoproteomic profiles and identified significant hidden common pathways between the two os models. the “topological scoring” algorithm scores nodes in a network built from the experimentally derived, condition-specific genomic and proteomic profiles. the output provides a series of signaling proteins associated with the metastatic phenotype and helps delineate the underlying biological processes. one of the main advantages of the topological algorithm is that it assesses the relative contribution of every node in the condition-specific network under study relative to its role in the global network. thus, the “hubs” that are commonly present in different pathways and have high connectivity in the global network, are penalized if they do not have any special role related to the experimentally derived set of differentially expressed genes or proteins. on the other hand, nodes that provide significant connectivity among the differentially expressed genes or proteins are highly scored regardless of their global network interactions, rendering the results more specific. pathway analysis of the identified topological nodes revealed many common and significant dysregulated pathways between the models, which were missed by the direct analyses. remarkably, the analysis revealed pathways such as “cytoskeleton remodeling/tgf/wnt” and “cell adhesion/chemokines and adhesion”, which were common to both models and present at both mrna and protein levels. all the processes represented in these common pathways have been previously associated with metastasis in various cancer types, and changes involved in these signaling networks associated to cytoskeleton remodeling, cell-cell and cell-matrix adhesion are critical for the cancer cell migration and invasion. to determine if these pathways would be identified by a random chance or nonspecifically, we performed a false discovery analysis using randomly selected genes and confirmed the specificity of these pathways in our datasets. our results support the notion that there are common metastatic mechanisms acting downstream of different genetic aberrations or origins. this implies the existence of common molecular therapeutic targets and disease biomarkers irrespective of the driver genetic abnormalities in the tumors. this finding is particularly significant in tumors, such as os, in which the genomic abnormalities are known to be highly complex and it is hard to determine the driver mutations from the passengers. for instance, the most significantly overrepresented pathway of the topological analysis was the “cytoskeleton remodeling/tgf/wnt”, which includes the interesting wnt component. the wnt signaling is particularly important for cancer cells as they are able to disrupt this pathway in different ways resulting in tumorigenesis and metastasis. activation of the wnt signaling pathway is necessary for the commitment of mesenchymal stem cells to the osteoblast lineage. in addition, aberrant wnt signaling activity has been reported in a variety of human cancers including soft tissue sarcomas and human os primary tissues and cell lines. abnormal activation of the canonical wnt pathway results in stabilized β-catenin that translocates into the nucleus. subsequently, it binds to transcription factors and drives the uncontrolled expression of target genes implicated in cell proliferation, transformation, and tumor progression, such as myc, matrix metalloproteinase axin- the cell adhesion molecule l1-cam, the metastasis gene s100a and others. therefore, targeting the wnt signaling may have a therapeutic effect on different types of metastatic os. similarly, our findings suggest that wnt signaling is highly relevant to the metastasis of os. previous studies have shown that os harbors an accumulation of β-catenin either in the cytoplasm or in the nucleus, a hallmark of wnt signaling activation. additionally, the wnt coreceptor lrp expression in os tissue samples correlated with metastasis and a lower rate of disease-free survival in patients. furthermore, os cell lines have been reported to express many wnt ligands and receptors, whereas secreted wnt antagonists including secreted frizzled-related protein and dickkopf families are commonly absent. inhibition of similar mechanisms by the reintroduction of secreted wnt antagonists in os, such as the wnt inhibitory factor, has been proposed for downregulation of wnt signaling as a novel therapeutic approach. for instance, overexpression of wif- significantly decreased tumor growth and markedly reduced the number of lung metastasis of 143b cells in vivo. in other studies soluble lrp, which blocks wnt signaling, was able to reduce in vitro cellular invasion, and transfection of slrp in saos- caused a marked up-regulation of e-cadherin and down-regulation of n-cadherin suggesting a reversal of epithelial-mesenchymal transition. these findings suggest an important role for aberrant wnt signaling in the pathobiology and progression of os. the wnt pathway may represent a promising source of novel therapeutic targets and disease progression biomarkers. another common pathway relevant in metastasis is the “cell adhesion/chemokines and adhesion”, which was identified at both the genomic and proteomic levels. chemokine ligands and their cognate receptors have been extensively implicated in the progression and metastasis of multiple tumors such as melanoma, breast cancer, prostate cancer, and others. in os, research has revealed a complex interaction between chemokine ligand/receptor axis, and their role in tumor invasion, metastasis and patient prognosis. our group previously reported that expression of two cxc chemokines were elevated in tumor and plasma of pediatric os patients, and their levels correlated with patient outcomes. several of the cxc chemokines were also identified by the topological analysis in this study, including cxcl which has been directly associated to metastasis in os and other tumors. identification of the significant topological nodes within such complex networks could help identify the key players and delineate their roles in cell adhesion, invasion and other important metastasis functions. in addition to the identification of therapeutic targets, identification of tumor-derived biomarkers for early detection of disease progression and metastasis is a critical component for personalized medicine. as previously noted, characterization of key hidden nodes could facilitate the identification of candidate biomarkers for metastatic os. because of the diversity of the human proteome and limitations of the current proteomic methods, targeting specific subproteomes that are likely to be secreted into the blood stream, such as the glycoproteome, will improve the likelihood of identifying tumor-derived circulating biomarkers. in this study, we characterized the n-linked glycoproteome due to their involvement in metastatic os as evidenced by the glycogene analysis, and their frequent localization in the cellular membrane and extracellular space. in spite of the limited number of glycoproteins identified in this study, the topological analysis revealed common significant pathways between the differentially regulated glycoproteins and genes in both os models. using this analysis, we can prioritize the up-regulated glycoproteins and up-regulated genes as well as the hidden molecules that may serve as biomarkers for the disease or metastasis in the future validation, such as proteins in the tgf beta and map kinase families identified by topological analysis. despite the encouraging results, we recognize that there are limitations in the current study. for instance, the cell line models used in our study represents a significant limitation. although the in vitro models provide a convenient and renewable platform that cannot be surpassed by clinical specimens, it may not faithfully reflect the behavior of tumor cells in vivo. therefore, future directions include the development of orthotopic xenograft mouse models to recapitulate and validate the results obtained in this study. in addition, the characterized n-linked glycoproteins with an affinity for the wga lectin represent a restricted number of proteins, which is typical to this type of subproteome analysis. this limits the number of identifiable up-regulated glycoproteins amenable for validation as candidate biomarkers. further studies with a wider range of proteins including additional glycoproteins by different capture methods and other relevant subproteomes, such as the secretome will help to alleviate this limitation.  we used a systems biology approach to identify key and common metastatic mechanisms in os. a similar approach can be applied to other genetically distinct but phenotypically similar cell lines of other tumor types. the use of the topological analysis revealed hidden biological networks known to play a fundamental role in metastasis. the analysis will also provide new types of information in this pediatric cancer that are not identifiable by conventional single-level analyses, and present new directions for future research. our study shows that the systems biology analysis of metastasis through different types of genomic and proteomic characterization can shed light on the key biological processes of os invasion. our findings have significant implications on both the development of biomarkers for disease progression and metastasis, and identification of potential targets for novel therapies. ultimately, we believe that the results reported in this study will accelerate the development of a personalized treatment for metastatic os patients to maximize their survival and decrease treatment toxicity. 
to address this issue, we used a topological significance analysis based on a “shortest-path” algorithm to identify topological nodes, which uncovered additional biological information with respect to the genomic and glycoproteomic profiles but remained hidden from the direct analyses. of these, the “cytoskeleton remodeling/tgf/wnt” was the top ranked common pathway from the topological analysis of the genomic and proteomic profiles in the two metastatic models. pathway analysis of the significant topological nodes revealed a striking concordance between the models and identified significant common pathways, including “cytoskeleton remodeling/tgf/wnt”, “cytoskeleton remodeling/cytoskeleton remodeling”, and “cell adhesion/chemokines and adhesion”. further characterization of this common pathway and other topological pathways identified from this study may lead to a novel therapeutic strategy for the treatment of metastatic os. the use of the topological analysis revealed hidden biological pathways that are known to play critical roles in metastasis. mrna expression microarray and n-linked glycoproteomic analyses were performed on two commonly used isogenic pairs of human metastatic os cell lines, namely hos/143b and saos-2/lm pathway analysis of the differentially regulated genes and glycoproteins separately revealed pathways associated to metastasis including cell cycle regulation, immune response, and epithelial-to-mesenchymal-transition. however, no common significant pathway was found at both genomic and proteomic levels between the two metastatic models, suggesting a very different biological nature of the cell lines. the survival rate of patients with metastatic disease remains very dismal. in this study, we used a systems biology approach to identify common metastatic pathways that are jointly supported by both mrna and protein expression data in two distinct human metastatic os models. in this study, we used a systems biology approach by integrating genomic and proteomic data to identify key and common metastatic mechanisms in os. osteosarcoma is the most common malignant bone tumor in children and adolescents.
 osteosarcoma is the most common malignant bone tumor in children and adolescents. the survival rate of patients with metastatic disease remains very dismal. nevertheless, metastasis is a complex process and a single-level analysis is not likely to identify its key biological determinants. in this study, we used a systems biology approach to identify common metastatic pathways that are jointly supported by both mrna and protein expression data in two distinct human metastatic os models.  mrna expression microarray and n-linked glycoproteomic analyses were performed on two commonly used isogenic pairs of human metastatic os cell lines, namely hos/143b and saos-2/lm pathway analysis of the differentially regulated genes and glycoproteins separately revealed pathways associated to metastasis including cell cycle regulation, immune response, and epithelial-to-mesenchymal-transition. however, no common significant pathway was found at both genomic and proteomic levels between the two metastatic models, suggesting a very different biological nature of the cell lines. to address this issue, we used a topological significance analysis based on a “shortest-path” algorithm to identify topological nodes, which uncovered additional biological information with respect to the genomic and glycoproteomic profiles but remained hidden from the direct analyses. pathway analysis of the significant topological nodes revealed a striking concordance between the models and identified significant common pathways, including “cytoskeleton remodeling/tgf/wnt”, “cytoskeleton remodeling/cytoskeleton remodeling”, and “cell adhesion/chemokines and adhesion”. of these, the “cytoskeleton remodeling/tgf/wnt” was the top ranked common pathway from the topological analysis of the genomic and proteomic profiles in the two metastatic models. the up-regulation of proteins in the “cytoskeleton remodeling/tgf/wnt” pathway in the saos-2/lm and hos/143b models was further validated using an orthogonal reverse phase protein array platform.  in this study, we used a systems biology approach by integrating genomic and proteomic data to identify key and common metastatic mechanisms in os. the use of the topological analysis revealed hidden biological pathways that are known to play critical roles in metastasis. wnt signaling has been previously implicated in os and other tumors, and inhibitors of wnt signaling pathways are available for clinical testing. further characterization of this common pathway and other topological pathways identified from this study may lead to a novel therapeutic strategy for the treatment of metastatic os. 