Identification and analysis of key genes in osteosarcoma using bioinformatics

Diao,Chunyu; Xi,Yong; Xiao,Tao

doi:10.3892/ol.2017.7649

Identification and analysis of key genes in osteosarcoma using bioinformatics

Authors:
- Chunyu Diao
- Yong Xi
- Tao Xiao
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Affiliations: Department of Orthopedics, The Second Xiangya Hospital of Central South University, Changsha, Hunan 410011, P.R. China, Department of Orthopedics, Tongchuan People's Hospital, Tongchuan, Shaanxi 727000, P.R. China
Published online on: December 19, 2017 https://doi.org/10.3892/ol.2017.7649
Pages: 2789-2794
Copyright: © Diao et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Osteosarcoma (OS) is an invasive malignant neoplasm of the bones. The present study identified and analyzed key genes associated with OS. Expression profiling of the dataset GSE49003, which included 6 metastatic and 6 non‑metastatic OS cell lines and was obtained from the Gene Expression Omnibus, was performed. Following data preprocessing, the differentially expressed genes (DEGs) were selected using the limma package in R. Subsequently, bidirectional hierarchical clustering using the pheatmap package in R and an unpaired Students' t‑test was performed for the DEGs. Based on the Search Tool for the Retrieval of Interacting Genes database and Cytoscape software, a protein‑protein interaction (PPI) network for the DEGs was constructed. Using Database for Annotation, Visualization and Integrated Discovery software and the Kyoto Encyclopedia of Genes and Genomes Orthology Based Annotation System server, functional and pathway enrichment analyses were performed for the DEGs corresponding to the proteins of the network. In addition, the transcription factors (TFs) and CpG islands of the gene promoter were searched for using the TRANSFAC database and CpG Island Searcher software, respectively. A total of 323 DEGs were identified between the metastatic and non‑metastatic samples. In the PPI network, upregulated epidermal growth factor receptor (EGFR) exhibits a high degree and was therefore highly interconnected with other proteins. Enrichment analysis revealed that EGFR was enriched in cytoskeleton organization, organic substance response and the signaling pathway of focal adhesion. The TFs early growth response 1, nuclear factor‑κB complex subunits, peroxisome proliferator activated receptor α, signal transducer and activator of transcription 3 and MYC proto‑oncogene were identified in the EGFR promoter region. Furthermore, multiple CpG islands, starting from the 400 bp of the EGFR promoter sequence, were predicted. Methylated modification of the CpG islands in the EGFR promoter may help to regulate EGFR expression. The TFs identified in the EGFR promoter may function in the progression of OS.

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1	Luetke A, Meyers PA, Lewis I and Juergens H: Osteosarcoma treatment - where do we stand? A state of the art review. Cancer Treat Rev. 40:523–532. 2014. View Article : Google Scholar : PubMed/NCBI
2	Moore DD and Luu HH: Osteosarcoma. Cancer Treat Res. 162:65–92. 2014. View Article : Google Scholar : PubMed/NCBI
3	Ottaviani G, Jaffe N, Eftekhari F, Raymond AK and Yasko AW: Pediatric and Adolescent Osteosarcoma. Springer; Berlin: 2011
4	Ottaviani G and Jaffe N: The epidemiology of osteosarcoma. Cancer Treat Res. 152:3–13. 2009. View Article : Google Scholar : PubMed/NCBI
5	Zhao S, Kurenbekova L, Gao Y, Roos A, Creighton CJ, Rao P, Hicks J, Man TK, Lau C, Brown AM, et al: NKD2, a negative regulator of Wnt signaling, suppresses tumor growth and metastasis in osteosarcoma. Oncogene. 34:5069–5079. 2015. View Article : Google Scholar : PubMed/NCBI
6	Fujiwara T, Takahashi RU, Kosaka N, Nezu Y, Kawai A, Ozaki T and Ochiya T: RPN2 gene confers osteosarcoma cell malignant phenotypes and determines clinical prognosis. Mol Ther Nucleic Acids. 3:e1892013. View Article : Google Scholar
7	Wang L, Zhang Q, Chen W, Shan B, Ding Y, Zhang G, Cao N, Liu L and Zhang Y: B7-H3 is overexpressed in patients suffering osteosarcoma and associated with tumor aggressiveness and metastasis. PLoS One. 8:e706892013. View Article : Google Scholar : PubMed/NCBI
8	Yang H, Zhang Y, Zhou Z, Jiang X and Shen A: Transcription factor Snai1-1 induces osteosarcoma invasion and metastasis by inhibiting E-cadherin expression. Oncol Lett. 8:193–197. 2014. View Article : Google Scholar : PubMed/NCBI
9	Yang G, Yuan J and Li K: EMT transcription factors: Implication in osteosarcoma. Med Oncol. 30:6972013. View Article : Google Scholar : PubMed/NCBI
10	Al-Romaih K, Sadikovic B, Yoshimoto M, Wang Y, Zielenska M and Squire JA: Decitabine-induced demethylation of 5′ CpG island in GADD45A leads to apoptosis in osteosarcoma cells 1. Neoplasia. 10:471–480. 2008. View Article : Google Scholar : PubMed/NCBI
11	Endo-Munoz L, Cai N, Cumming A, Macklin R, Merida de Long L, Topkas E, Mukhopadhyay P, Hill M and Saunders NA: Progression of osteosarcoma from a non-Metastatic to a metastatic phenotype is causally associated with activation of an autocrine and paracrine uPA axis. PLoS One. 10:e01335922015. View Article : Google Scholar : PubMed/NCBI
12	Barrett T, Wilhite SE, Ledoux P, Evangelista C, Kim IF, Tomashevsky M, Marshall KA, Phillippy KH, Sherman PM, Holko M, et al: NCBI GEO: Archive for functional genomics data sets-update. Nucleic Acids Res. 41(Database issue): D991–D995. 2013.PubMed/NCBI
13	Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W and Smyth GK: Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic acids Res. 43:e472015. View Article : Google Scholar : PubMed/NCBI
14	Haynes W: Benjamini-Hochberg methodEncyclopedia of Systems Biology. Springer; New York, NY: pp. 782013, View Article : Google Scholar
15	Jarabo A, Buisan R and Gutierrez D: Bidirectional clustering for scalable VPL-based global illumination. Spanish Computer Graphics Conference. Moreno JL and Sber M: Eurographics Association; pp. 1–9. 2015; http://dx.doi.org/10.2312/ceig.20151196
16	Draisma J, Horobeţ E, Ottaviani G, Sturmfels B and Thomas RR: The Euclidean distance degree of an algebraic varietyFoundations of Computational Mathematics. 16. Springer-Verlag; New York, NY: pp. 99–149, 1-51. 2016, View Article : Google Scholar
17	Kolde R: Pheatmap: Pretty Heatmaps. 2015.https://cran.r-project.org/web/packages/pheatmap/index.htmlJuly 2–2015
18	De Winter JCF: Using the Student's ‘t’-test with extremely small sample sizes. Pract Assess Res Eval. 18:122013.
19	Szklarczyk D, Franceschini A, Kuhn M, Simonovic M, Roth A, Minguez P, Doerks T, Stark M, Muller J, Bork P, et al: The STRING database in 2011: Functional interaction networks of proteins, globally integrated and scored. Nucleic Acids Res. 39(Database issue): D561–D568. 2011. View Article : Google Scholar : PubMed/NCBI
20	Shi Z, Rui W, Feng Y and Guo J: Bioinformatics Study on Relationship of Hyperlipidemia and Phospholipids based on Cytoscape Software. Atlantis Press; pp. 357–360. 2014
21	Gene Ontology Consortium, . Gene Ontology Consortium: Going forward. Nucleic Acids Res. 43(Database Issue): D1049–D1056. 2015.PubMed/NCBI
22	Kotera M, Moriya Y, Tokimatsu T, Kanehisa M and Goto S: KEGG and GenomeNet, new developments, metagenomic analysisEncyclopedia of Metagenomics. Nelson K: Springer; New York, NY: pp. 329–339. 2015
23	Jiao X, Sherman BT, Huang da W, Stephens R, Baseler MW, Lane HC and Lempicki RA: DAVID-WS: A stateful web service to facilitate gene/protein list analysis. Bioinformatics. 28:1805–1806. 2012. View Article : Google Scholar : PubMed/NCBI
24	Xie C, Mao X, Huang J, Ding Y, Wu J, Dong S, Kong L, Gao G, Li CY and Wei L: KOBAS 2.0: A web server for annotation and identification of enriched pathways and diseases. Nucleic Acids Res. 39:W316–W322. 2011. View Article : Google Scholar : PubMed/NCBI
25	Matys V, Kel-Margoulis OV, Fricke E, Liebich I, Land S, Barre-Dirrie A, Reuter I, Chekmenev D, Krull M, Hornischer K, et al: TRANSFAC and its module TRANSCompel: Transcriptional gene regulation in eukaryotes. Nucleic Acids Res. 34(Database issue): D108–D110. 2006. View Article : Google Scholar : PubMed/NCBI
26	Blackledge NP, Thomson JP and Skene PJ: CpG island chromatin is shaped by recruitment of ZF-CxxC proteins. Cold Spring Har Perspect Biol. 5:a0186482013. View Article : Google Scholar
27	Sproul D, Kitchen RR, Nestor CE, Dixon JM, Sims AH, Harrison DJ, Ramsahoye BH and Meehan RR: Tissue of origin determines cancer-associated CpG island promoter hypermethylation patterns. Genome Biol. 13:R842012. View Article : Google Scholar : PubMed/NCBI
28	Parker A, Proctor G and Spudich G: Ensembl 2012. Advances in Immunology. 2011.
29	Takai D and Jones PA: The CpG island searcher: A new WWW resource. In Silico Biol. 3:235–340. 2003.PubMed/NCBI
30	Freeman SS, Allen SW, Ganti R, Wu J, Ma J, Su X, Neale G, Dome JS, Daw NC and Khoury JD: Copy number gains in EGFR and copy number losses in PTEN are common events in osteosarcoma tumors. Cancer. 113:1453–1461. 2008. View Article : Google Scholar : PubMed/NCBI
31	Wang Q, Cai J, Wang J, Xiong C and Zhao J: miR-143 inhibits EGFR-signaling-dependent osteosarcoma invasion. Tumour Biol. 35:12743–12748. 2014. View Article : Google Scholar : PubMed/NCBI
32	Hughes DP, Thomas DG, Giordano TJ, Baker LH and McDonagh KT: Cell surface expression of epidermal growth factor receptor and Her-2 with nuclear expression of Her-4 in primary osteosarcoma. Cancer Res. 64:2047–2053. 2004. View Article : Google Scholar : PubMed/NCBI
33	Do SI, Jung WW, Kim HS and Park YK: The expression of epidermal growth factor receptor and its downstream signaling molecules in osteosarcoma. Int J Oncol. 34:797–803. 2009.PubMed/NCBI
34	Yu DH, Ware C, Waterland RA, Zhang J, Chen MH, Gadkari M, Kunde-Ramamoorthy G, Nosavanh LM and Shen L: Developmentally programmed 3′ CpG island methylation confers tissue- and cell-type-specific transcriptional activation. Mol Cell Biol. 33:1845–1858. 2013. View Article : Google Scholar : PubMed/NCBI
35	Mcmahon AP, Champion JE, McMahon JA and Sukhatme VP: Developmental expression of the putative transcription factor Egr-1 suggests that Egr-1 and c-fos are coregulated in some tissues. Development. 108:281–287. 1990.PubMed/NCBI
36	Windischhofer W, Huber E, Rossmann C, Semlitsch M, Kitz K, Rauh A, Devaney T, Leis HJ and Malle E: LPA-induced suppression of periostin in human osteosarcoma cells is mediated by the LPA(1)/Egr-1 axis. Biochimie. 94:1997–2005. 2012. View Article : Google Scholar : PubMed/NCBI
37	Zhao Z, Wu MS, Zou C, Tang Q, Lu J, Liu D, Wu Y, Yin J, Xie X, Shen J, et al: Downregulation of MCT1 inhibits tumor growth, metastasis and enhances chemotherapeutic efficacy in osteosarcoma through regulation of the NF-κB pathway. Cancer Lett. 342:150–158. 2013. View Article : Google Scholar : PubMed/NCBI
38	Wagner ER, He BC, Chen L, Zuo GW, Zhang W, Shi Q, Luo Q, Luo X, Liu B, Luo J, et al: Therapeutic Implications of PPARgamma in Human Osteosarcoma. PPAR Res. 2010:9564272010. View Article : Google Scholar : PubMed/NCBI
39	Lai R, Navid F, Rodriguez-Galindo C, Liu T, Fuller CE, Ganti R, Dien J, Dalton J, Billups C and Khoury JD: STAT3 is activated in a subset of the Ewing sarcoma family of tumours. J Pathol. 208:624–632. 2006. View Article : Google Scholar : PubMed/NCBI
40	Chen CL, Loy A, Ling C, Chan C, Hsieh FC, Cheng G, Wu B, Qualman SJ, Kunisada K, Keiko YT and Lin J: Signal transducer and activator of transcription 3 is involved in cell growth and survival of human rhabdomyosarcoma and osteosarcoma cells. BMC Cancer. 7:1112007. View Article : Google Scholar : PubMed/NCBI
41	Ryu K, Susa M, Choy E, Yang C, Hornicek FJ, Mankin HJ and Duan Z: Oleanane triterpenoid CDDO-Me induces apoptosis in multidrug resistant osteosarcoma cells through inhibition of Stat3 pathway. BMC Cancer. 10:1872010. View Article : Google Scholar : PubMed/NCBI
42	Scionti I, Michelacci F, Pasello M, Hattinger CM, Alberghini M, Manara MC, Bacci G, Ferrari S, Scotlandi K, Picci P and Serra M: Clinical impact of the methotrexate resistance-associated genes C-MYC and dihydrofolate reductase (DHFR) in high-grade osteosarcoma. Ann Oncol. 19:1500–1508. 2008. View Article : Google Scholar : PubMed/NCBI
43	Wu X, Cai ZD, Lou LM and Zhu YB: Expressions of p53, c-MYC, BCL-2 and apoptotic index in human osteosarcoma and their correlations with prognosis of patients. Cancer Epidemiol. 36:212–216. 2012. View Article : Google Scholar : PubMed/NCBI