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http://dx.doi.org/10.7314/APJCP.2014.15.4.1817

Screening for Metastatic Osteosarcoma Biomarkers with a DNA Microarray  

Diao, Chun-Yu (Traumatic Orthopedic Research, Department of Orthopaedics, The Second Xiangya Hospital of Central South University)
Guo, Hong-Bing (Traumatic Orthopedic Research, Department of Orthopaedics, The Second Xiangya Hospital of Central South University)
Ouyang, Yu-Rong (Traumatic Orthopedic Research, Department of Orthopaedics, The Second Xiangya Hospital of Central South University)
Zhang, Han-Cong (Traumatic Orthopedic Research, Department of Orthopaedics, The Second Xiangya Hospital of Central South University)
Liu, Li-Hong (Traumatic Orthopedic Research, Department of Orthopaedics, The Second Xiangya Hospital of Central South University)
Bu, Jie (Traumatic Orthopedic Research, Department of Orthopaedics, The Second Xiangya Hospital of Central South University)
Wang, Zhi-Hua (Chenzhou No.1 People's Hospital)
Xiao, Tao (Traumatic Orthopedic Research, Department of Orthopaedics, The Second Xiangya Hospital of Central South University)
Publication Information
Asian Pacific Journal of Cancer Prevention / v.15, no.4, 2014 , pp. 1817-1822 More about this Journal
Abstract
Objective: The aim of this study was to screen for possible biomarkers of metastatic osteosarcoma (OS) using a DNA microarray. Methods: We downloaded the gene expression profile GSE49003 from Gene Expression Omnibus database, which included 6 gene chips from metastatic and 6 from non-metastatic OS patients. The R package was used to screen and identify differentially expressed genes (DEGs) between metastatic and non-metastatic OS patients. Then we compared the expression of DEGs in the two groups and sub-grouped into up-regulated and down-regulated, followed by functional enrichment analysis using the DAVID system. Subsequently, we constructed an miRNA-DEG regulatory network with the help of WebGestalt software. Results: A total of 323 DEGs, including 134 up-regulated and 189 down-regulated, were screened out. The up-regulated DEGs were enriched in 14 subcategories and most significantly in cytoskeleton organization, while the down-regulated DEGs were prevalent in 13 subcategories, especially wound healing. In addition, we identified two important miRNAs (miR-202 and miR-9) pivotal for OS metastasis, and their relevant genes, CALD1 and STX1A. Conclusions: MiR-202 and miR-9 are potential key factors affecting the metastasis of OS and CALD1 and STX1A may be possible targets beneficial for the treatment of metastatic OS. However, further experimental studies are needed to confirm our results.
Keywords
Metastatic osteosarcoma; differential gene expression; functional enrichment; miRNA; regulatory network;
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1 Zhang B, Kirov S, Snoddy J (2005). WebGestalt: an integrated system for exploring gene sets in various biological contexts. Nucleic Acids Res, 33, W741-8.   DOI   ScienceOn
2 Thorsen K, Sorensen KD, Brems-Eskildsen AS, et al (2008). Alternative splicing in colon, bladder, and prostate cancer identified by exon array analysis. Mol Cell Proteomics, 7, 1214-24.   DOI   ScienceOn
3 Tsao MS, Lau S, Boutros P, et al (2011). Materials and methods for prognosing lung cancer survival. Google Patents.
4 Willis RC, Hogue CW (2006). Searching, viewing, and visualizing data in the Biomolecular Interaction Network Database (BIND). Curr Protoc Bioinformatics, Chapter 8: Unit 8.9.
5 Zhang B, Pan X, Cobb GP, Anderson TA (2007). microRNAs as oncogenes and tumor suppressors. Dev Biol, 302, 1-12.   DOI   ScienceOn
6 Huang G, Nishimoto K, Zhou Z, et al (2012). miR-20a encoded by the miR-17-92 cluster increases the metastatic potential of osteosarcoma cells by regulating Fas expression. Cancer Res, 72, 908-16.   DOI
7 Hwang W, Cho YR, Zhang A, Ramanathan M (2006). A novel functional module detection algorithm for protein-protein interaction networks. Algorithms Mol Biol, 1, 24.   DOI
8 Jones K B, Salah Z, Del Mare S, et al (2012). miRNA signatures associate with pathogenesis and progression of osteosarcoma. Cancer Res, 72, 1865-77.   DOI
9 Khew-Goodall Y, Goodall GJ (2010). Myc-modulated miR-9 makes more metastases. Nat Cell Biol, 12, 209-11.
10 Amsellem V, Kryszke MH, Hervy M, et al (2005). The actin cytoskeleton-associated protein zyxin acts as a tumor suppressor in Ewing tumor cells. Exp Cell Res, 304, 443-56.   DOI   ScienceOn
11 Bao Y-P, Yi Y, Peng L-L, et al (2013). Roles of microRNA-206 in osteosarcoma pathogenesis and progression. Asian Pac J Cancer Prev, 14, 3751-5.   DOI   ScienceOn
12 Benjamini Y (2010). Discovering the false discovery rate. J R Stat Soc Series B Stat Methodol, 72, 405-16.   DOI   ScienceOn
13 Breitkreutz BJ, Stark C, Tyers M (2003a). The GRID: The general repository for interaction datasets. Genome Biol, 4, R23.   DOI
14 Breitkreutz BJ, Stark C, Tyers M (2003b). Osprey: a network visualization system. Genome Biol, 4, R22.   DOI
15 Cuomo M E, Knebel A, Platt G, et al (2005). Regulation of microfilament organization by Kaposi sarcoma-associated herpes virus-cyclin.CDK6 phosphorylation of caldesmon. J Biol Chem, 280, 35844-58.   DOI   ScienceOn
16 Lau SK, Boutros PC, Pintilie M, et al (2007). Three-gene prognostic classifier for early-stage non-small-cell lung cancer. J Clin Oncol, 25, 5562-9.   DOI   ScienceOn
17 Zhang H, Cai X, Wang Y, et al (2010). microRNA-143, down-regulated in osteosarcoma, promotes apoptosis and suppresses tumorigenicity by targeting Bcl-2. Oncol Rep, 24, 1363-9.
18 Zhao Y, Li C, Wang M, et al (2013). Decrease of miR-202-3p expression, a novel tumor suppressor, in gastric cancer. PloS One, 8, e69756.   DOI
19 Zhou G, Shi X, Zhang J, et al (2013). MicroRNAs in osteosarcoma: From biological players to clinical contributors, a review. J Int Med Res, 41, 1-12.   DOI   ScienceOn
20 Lu J, Luo H, Liu X, et al (2013). miR-9 targets CXCR4 and functions as a potential tumor suppressor in nasopharyngeal carcinoma. Carcinogenesis, ????????.
21 Ma L, Young J, Prabhala H, et al (2010). miR-9, a MYC/MYCNactivated microRNA, regulates E-cadherin and cancer metastasis. Nat Cell Biol, 12, 247-56.
22 Rao-Bindal K, Rao CK, Yu L, Kleinerman ES (2012). Expression of c-FLIP in pulmonary metastases in osteosarcoma patients and human xenografts. Pediatr Blood Cancer, 60, 575-9.
23 Ren L, Hong S, Cassavaugh J, et al (2008). The actincytoskeleton linker protein ezrin is regulated during osteosarcoma metastasis by PKC. Oncogene, 28, 792-802.
24 Salinas-Souza C, De Oliveira R, Alves MT, et al (2013). The metastatic behavior of osteosarcoma by gene expression and cytogenetic analyses. Hum Pathol, 44, 2188-98.   DOI   ScienceOn
25 Smyth GK (2005). Limma: linear models for microarray data. Bioinformatics and computational biology solutions using R and Bioconductor. Springer.
26 Sun C, Li N, Yang Z, et al (2013). mir-9 regulation of BrcA1 and Ovarian cancer Sensitivity to cisplatin and PArP inhibition. J Natl Cancer Inst, 105, 1750-8.   DOI   ScienceOn
27 Densmore C L, Kleinerman ES, Gautam A, et al (2001). Growth suppression of established human osteosarcoma lung metastases in mice by aerosol gene therapy with PEI-p53 complexes. Cancer Gene Therapy, 8, 619-27.   DOI
28 Duncan D, Prodduturi N, Zhang B (2010). WebGestalt2: an updated and expanded version of the Web-based Gene Set Analysis Toolkit. Bmc Bioinformatics, 11, P10.   DOI
29 Emmrich S, Katsman-Kuipers J, Henke K, et al (2013). miR- 9 is a tumor suppressor in pediatric AML with t (8; 21). Leukemia, ????????
30 Smyth GK, Speed T (2003). Normalization of cDNA microarray data. Methods, 31, 265-73.   DOI   ScienceOn
31 Tanay A, Sharan R, Kupiec M, Shamir R (2004). Revealing modularity and organization in the yeast molecular network by integrated analysis of highly heterogeneous genomewide data. Proc Natl Acad Sci USA, 101, 2981-6.   DOI   ScienceOn
32 Hu S, Xu C, Guan W, et al (2013). Texture feature extraction based on wavelet transform and gray-level co-occurrence matrices applied to osteosarcoma diagnosis. Biomed Mater Eng, 23, S129-S43.
33 Fan WD, Zhang XQ, Guo HL, et al (2012). Bioinformatics analysis reveals connection of squamous cell carcinoma and adenocarcinoma of the lung. Asian Pac J Cancer Prev, 13, 1477-82.   DOI   ScienceOn
34 Ferrari S, Smeland S, Mercuri M, et al (2005). Neoadjuvant chemotherapy with high-dose Ifosfamide, high-dose methotrexate, cisplatin, and doxorubicin for patients with localized osteosarcoma of the extremity: a joint study by the Italian and Scandinavian Sarcoma Groups. J Clin Oncol, 23, 8845-52.   DOI   ScienceOn
35 Hoffman A E, Liu R, Fu A, et al (2013). Targetome profiling, pathway analysis and genetic association study implicate miR-202 in lymphomagenesis. Cancer Epidemiol Biomarkers Prev, 22, 327-36.   DOI   ScienceOn
36 Huang da W, Sherman BT, Lempicki RA (2009). Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc, 4, 44-57.   DOI
37 Di Cristofano C, Leopizzi M, Miraglia A, et al (2010). Phosphorylated ezrin is located in the nucleus of the osteosarcoma cell. Mod Pathol, 23, 1012-20.   DOI   ScienceOn
38 Ragland BD, Bell WC, Lopez RR, Siegal GP (2002). Cytogenetics and molecular biology of osteosarcoma. Lab Invest, 82, 365-73.   DOI   ScienceOn
39 Benjamini Y, Hochberg Y (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Series B Stat Methodol, 289-300.