Abstract
BackgroundOsteosarcoma (OS) 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.ResultsmRNA 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/LM7. 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/LM7 and HOS/143B models was further validated using an orthogonal Reverse Phase Protein Array platform.ConclusionsIn 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.
Highlights
Osteosarcoma (OS) is the most common malignant bone tumor in children and adolescents
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
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 (Figure 6)
Summary
Osteosarcoma (OS) is the most common malignant bone tumor in children and adolescents. Similar to other types of cancer, metastasis in OS is a complex process [4,5] and a single-level of analysis alone, such as mRNA expression profiling, cannot capture the complete information to fully understand the metastatic mechanism. 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
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