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Open Access Highly Accessed Research article

A systems biology approach reveals common metastatic pathways in osteosarcoma

Ricardo J Flores12, Yiting Li12, Alexander Yu12, Jianhe Shen12, Pulivarthi H Rao12, Serrine S Lau4, Marina Vannucci5, Ching C Lau123 and Tsz-Kwong Man123*

Author Affiliations

1 Texas Children’s Cancer and Hematology Centers, Texas Children’s Hospital, Houston, TX, USA

2 Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA

3 Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, TX, USA

4 Southwest Environmental Health Science Centers, The University of Arizona, Tucson, AZ, USA

5 Department of Statistics, Rice University, Houston, TX, USA

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BMC Systems Biology 2012, 6:50  doi:10.1186/1752-0509-6-50

Published: 28 May 2012

Abstract

Background

Osteosarcoma (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.

Results

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/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.

Conclusions

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.