A systems biology approach reveals common metastatic pathways in osteosarcoma

Persistent Link:
http://hdl.handle.net/10150/610101
Title:
A systems biology approach reveals common metastatic pathways in osteosarcoma
Author:
Flores, Ricardo; Li, Yiting; Yu, Alexander; Shen, Jianhe; Rao, Pulivarthi; Lau, Serrine; Vannucci, Marina; Lau, Ching; Man, Tsz-Kwong
Affiliation:
Texas Children’s Cancer and Hematology Centers, Texas Children’s Hospital, Houston, TX, USA; Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA; Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, TX, USA; Southwest Environmental Health Science Centers, The University of Arizona, Tucson, AZ, USA; Department of Statistics, Rice University, Houston, TX, USA
Issue Date:
2012
Publisher:
BioMed Central
Citation:
Flores et al. BMC Systems Biology 2012, 6:50 http://www.biomedcentral.com/1752-0509/6/50
Journal:
BMC Systems Biology
Rights:
© 2012 Flores et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0)
Collection Information:
This item is part of the UA Faculty Publications collection. For more information this item or other items in the UA Campus Repository, contact the University of Arizona Libraries at repository@u.library.arizona.edu.
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.
EISSN:
1752-0509
DOI:
10.1186/1752-0509-6-50
Version:
Final published version
Additional Links:
http://www.biomedcentral.com/1752-0509/6/50

Full metadata record

DC FieldValue Language
dc.contributor.authorFlores, Ricardoen
dc.contributor.authorLi, Yitingen
dc.contributor.authorYu, Alexanderen
dc.contributor.authorShen, Jianheen
dc.contributor.authorRao, Pulivarthien
dc.contributor.authorLau, Serrineen
dc.contributor.authorVannucci, Marinaen
dc.contributor.authorLau, Chingen
dc.contributor.authorMan, Tsz-Kwongen
dc.date.accessioned2016-05-20T08:58:36Z-
dc.date.available2016-05-20T08:58:36Z-
dc.date.issued2012en
dc.identifier.citationFlores et al. BMC Systems Biology 2012, 6:50 http://www.biomedcentral.com/1752-0509/6/50en
dc.identifier.doi10.1186/1752-0509-6-50en
dc.identifier.urihttp://hdl.handle.net/10150/610101-
dc.description.abstractBACKGROUND: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.en
dc.language.isoenen
dc.publisherBioMed Centralen
dc.relation.urlhttp://www.biomedcentral.com/1752-0509/6/50en
dc.rights© 2012 Flores et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0)en
dc.titleA systems biology approach reveals common metastatic pathways in osteosarcomaen
dc.typeArticleen
dc.identifier.eissn1752-0509en
dc.contributor.departmentTexas Children’s Cancer and Hematology Centers, Texas Children’s Hospital, Houston, TX, USAen
dc.contributor.departmentDepartment of Pediatrics, Baylor College of Medicine, Houston, TX, USAen
dc.contributor.departmentDan L. Duncan Cancer Center, Baylor College of Medicine, Houston, TX, USAen
dc.contributor.departmentSouthwest Environmental Health Science Centers, The University of Arizona, Tucson, AZ, USAen
dc.contributor.departmentDepartment of Statistics, Rice University, Houston, TX, USAen
dc.identifier.journalBMC Systems Biologyen
dc.description.collectioninformationThis item is part of the UA Faculty Publications collection. For more information this item or other items in the UA Campus Repository, contact the University of Arizona Libraries at repository@u.library.arizona.edu.en
dc.eprint.versionFinal published versionen
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