Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

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An integrative multi-platform analysis for biomarker discovery of osteosarcoma

ABSTRACT: Three osteosarcoma (OS) cell lines (MG-63, Saos-2 and U-2 OS) and 1 osteoblastic cell line (hFOB1.19) were collected for this work. MG-63 was kindly provided by Dr. Agi Grigoriadis from University College London. Saos-2, U-2 OS and hFOB1.19 were purchased from ATCC. All cells used were kept in exponential phase of growth. Total RNA was extracted using the RNeasy Total RNA Isolation kit (QIAGEN). The quality and purity of the products were controlled by Agilent 2100. The final synthesized biotinylated cDNAs were hybridized to Affymetrix GeneChip® U133A 2.0 arrays following the protocol strictly. Arrays were scanned with the Affymetrix scanner 3000. Data analysis was performed by Microarray Suite 5.0 after pre-standard procedure. Link-test on datasets from both SELDI-TOF-MS and microarray high-throughput analysis platforms can accelerate the identification of tumor biomarkers. The results confirmed that CYC-1 with important biomedical function was an effective candidate biomarker for osteosarcoma early diagnosis.

ORGANISM(S): Homo sapiens

SUBMITTER: Long Liu

PROVIDER: E-GEOD-14789 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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An integrative multi-platform analysis for discovering biomarkers of osteosarcoma.

Li Guodong G Zhang Wenjuan W Zeng Huazong H Chen Lei L Wang Wenjing W Liu Jilong J Zhang Zhiyu Z Cai Zhengdong Z

BMC cancer 20090516

<h4>Background</h4>SELDI-TOF-MS (Surface Enhanced Laser Desorption/Ionization-Time of Flight-Mass Spectrometry) has become an attractive approach for cancer biomarker discovery due to its ability to resolve low mass proteins and high-throughput capability. However, the analytes from mass spectrometry are described only by their mass-to-charge ratio (m/z) values without further identification and annotation. To discover potential biomarkers for early diagnosis of osteosarcoma, we designed an inte ...[more]

PMID: 19445706

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Project description: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. In this study we analyzed two 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/LM7. The HOS cell line, originally known as M.T. and later as TE-85, was derived from an OS of a 13 year-old girl. The 143B metastatic subline was generated from HOS by a Ki-RAS oncogene transformation [Rhim, J.S., et al., Characterization of human cells transformed in vitro by N-methyl-N'-nitro-N-nitrosoguanidine. Int J Cancer, 1977. 19(4): p. 505-10.]. The SaOS-2 cell was derived from an OS of an 11 year-old girl, and its metastatic subline LM7, was developed by multiple in vivo selection of SaOS-2 cells in mice with pulmonary metastases [Jia, S.F., L.L. Worth, and E.S. Kleinerman, A nude mouse model of human osteosarcoma lung metastases for evaluating new therapeutic strategies. Clin Exp Metastasis, 1999. 17(6): p. 501-6.]. No replicates were included.

Dataset Information

An integrative multi-platform analysis for biomarker discovery of osteosarcoma

Publications

An integrative multi-platform analysis for discovering biomarkers of osteosarcoma.

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