Expression profiling in canine osteosarcoma: identification of biomarkers and pathways associated with outcome

BMC Cancer

Research Article

Janet Siebert¹  Liza E O'Donoghue²  Dawn L Duval²  Debra A Kamstock²  Andrey A Ptitsyn³  Russell S Thomas⁴ 

[1] CytoAnalytics, Analytical Services, Denver, Colorado, USA;Department of Clinical Sciences, Colorado State University, Fort Collins, Colorado, USA;Department of Microbiology, Immunology and Pathology, Colorado State University, Fort Collins, Colorado, USA;The Hamner Institutes for Health Sciences, Research Triangle Park, North Carolina, USA;

关键词: Pathway Analysis;    Support Vector Machine Model;    Poor Responder;    Hedgehog Pathway;    Hedgehog Signaling Pathway;   

DOI  :  10.1186/1471-2407-10-506

 received in 2010-05-03, accepted in 2010-09-22,  发布年份 2010

来源: Springer

【 摘 要 】

BackgroundOsteosarcoma (OSA) spontaneously arises in the appendicular skeleton of large breed dogs and shares many physiological and molecular biological characteristics with human OSA. The standard treatment for OSA in both species is amputation or limb-sparing surgery, followed by chemotherapy. Unfortunately, OSA is an aggressive cancer with a high metastatic rate. Characterization of OSA with regard to its metastatic potential and chemotherapeutic resistance will improve both prognostic capabilities and treatment modalities.MethodsWe analyzed archived primary OSA tissue from dogs treated with limb amputation followed by doxorubicin or platinum-based drug chemotherapy. Samples were selected from two groups: dogs with disease free intervals (DFI) of less than 100 days (n = 8) and greater than 300 days (n = 7). Gene expression was assessed with Affymetrix Canine 2.0 microarrays and analyzed with a two-tailed t-test. A subset of genes was confirmed using qRT-PCR and used in classification analysis to predict prognosis. Systems-based gene ontology analysis was conducted on genes selected using a standard J5 metric. The genes identified using this approach were converted to their human homologues and assigned to functional pathways using the GeneGo MetaCore platform.ResultsPotential biomarkers were identified using gene expression microarray analysis and 11 differentially expressed (p < 0.05) genes were validated with qRT-PCR (n = 10/group). Statistical classification models using the qRT-PCR profiles predicted patient outcomes with 100% accuracy in the training set and up to 90% accuracy upon stratified cross validation. Pathway analysis revealed alterations in pathways associated with oxidative phosphorylation, hedgehog and parathyroid hormone signaling, cAMP/Protein Kinase A (PKA) signaling, immune responses, cytoskeletal remodeling and focal adhesion.ConclusionsThis profiling study has identified potential new biomarkers to predict patient outcome in OSA and new pathways that may be targeted for therapeutic intervention.

【 授权许可】

Unknown   
© O'Donoghue et al; licensee BioMed Central Ltd. 2010. This article is published under license to 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), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.