Open Access

Gene expression profiling analysis of osteosarcoma cell lines

  • Authors:
    • Lu Sun
    • Jie Li
    • Bing Yan
  • View Affiliations

  • Published online on: June 18, 2015     https://doi.org/10.3892/mmr.2015.3958
  • Pages: 4266-4272
  • Copyright: © Sun et al. This is an open access article distributed under the terms of Creative Commons Attribution License [CC BY_NC 3.0].

Metrics: Total Views: 0 (Spandidos Publications: | PMC Statistics: )
Total PDF Downloads: 0 (Spandidos Publications: | PMC Statistics: )


Abstract

Osteosarcoma (OS) is the most common type of primary bone malignancy and has a poor prognosis. To investigate the mechanisms of osteosarcoma, the present analyzed the GSE28424 microarray. GSE28424 was downloaded from the Gene Expression Omnibus, and included a collective of 19 OS cell lines and four normal bone cell lines, which were used as controls. Subsequently, the differentially expressed genes (DEGs) were screened using the Limma package in Bioconductor. Gene Ontology (GO) and pathway enrichment analysis of the DEGs was performed using the Database for Annotation, Visualization and Integrated Discovery, interactions between the proteins encoded by the DEGs were identified using STRING, and the protein‑protein interaction (PPI) network was visualized using Cytoscape. In addition, modular analysis of the PPI network was performed using the Clique Percolation Method (CPM) in CFinder. A total of 1,170 DEGs were screened, including 530 upreguated and 640 downregulated genes. The enriched functions included organelle fission, immune response and response to wounding. In addition, RPL8 was observed to be involved with the ribosomal pathway in module A of the PPI network of the DEGs. PLCG1, SYK and PLCG2 were also involved in the B‑cell receptor signaling pathway in module B and the Fc‑epsilon RI signaling pathway in module C. In addition, AURKA (degree=39), MAD2L1 (degree=38), CDCA8 (degree=38), BUB1 (degree=37) and MELK (degree=37) exhibited higher degrees of connectivity in module F. The results of the present study suggested that the RPL8, PLCG1, PLCG2, SYK, MAD2L1, AURKA, CDCA8, BUB1 and MELK genes may be involved in OS.

Introduction

Osteosarcoma (OS) is the most common type of primary bone malignancy derived from primitive mesenchymal cells (1). OS originates predominantly from bone and rarely from soft tissue (2). With a high degree of malignancy, rapid progression and poor prognosis, OS occurs predominantly in teenagers, and, in adolescents between the ages of 15 and 19 years, OS comprises 15% of all types of solid extra cranial cancer (3,4).

Several studies have been performed to investigate the molecular mechanisms underlying OS. For example, increased expression levels of vascular endothelial growth factor A (VEGFA) have been reported to induce metastasis and OS development (57), the major angiogenic factors, VEGF165 and VEGF189, may be critical for neovascularisation in OS (8), and pigment epithelium-derived factor (PEDF) not only induces apoptotic cell death in OS cells, but also suppress the expression of VEGF, resulting in the inhibition of tumor angiogenesis (9). Alterations in p53 have also been found in uncultured OS samples, with a frequency ranging between 18 and 42%, indicating that p53 may be involved in the pathogenesis of OS (10,11). In the context of bone morphogenetic protein (BMP)/small mothers of decapentaplegic (SMAD) signaling, runt-related transcription factor 2 (Runx2) can act as an inducer of apoptosis and suppressor of growth in OS and normal osteoblasts (12). There exists an important role for human epidermal growth factor receptor 2 (HER2) in the promotion of metastatic potential in OS and in aggressive tumor growth (13).

In 2012, Namløs et al (14) used an integrative microarray approach to analyze genome-wide mRNA expression patterns between a panel of 19 EuroBoNeT OS cell lines and four normal bone cell lines, according to the cut-off point of false discovery rate (FDR) <0.05, and obtained 8,982 mRNA probes, which were significantly differently expressed between the two groups. Using the same data used by Namløs et al (14), the present study aimed to further screen for differentially expressed genes (DEGs) using a more strict threshold of FDR<0.01 and |log2fold-change (FC)|>1, and to analyze the potential functions of the DEGs using Gene Ontology (GO) functional analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. In addition, the present study aimed to construct a protein-protein interaction (PPI) network of these DEGs and screen the modules of the PPI network to identify the interactions/association between the DEGs.

Materials and methods

Microarray data

The expression profile of GSE28424, deposited by Namløs et al (14) was downloaded from the Gene Expression Omnibus (GEO; http://www.ncbi.nlm.nih.gov/geo/), which was based on the platform of the GPL13376 Illumina HumanWG-6 v2.0 expression beadchip (Illumina, San Diego, CA, USA). The GSE28424 dataset included a collective of 19 OS cell lines and four normal bone cell lines, of which the latter were used as controls.

DEGs screening

Following downloading of the GSE28424 data, the microarray data was normalized to a linear model of box plots of log2[(perfect match probe value)-MM (mismatch probe value)] using quantile normalization (15). Subsequently, the DEGs between the OS patients and normal control cell lines were analyzed using the linear models for microarray data (Limma) package in Bioconductor (http://www.bioconductor.org/packages/release/bioc/html/limma.html) (16). The adjusted P-value (FDR)<0.01 and |log2FC|>1 were used as the cut-off criteria.

Functional analysis and pathway enrichment analysis

As an integrated and high-throughput data-mining environment, the Database for Annotation, Visualization and Integrated Discovery (DAVID; http://david.abcc.ncifcrf.gov/) analyzes gene lists from high-throughput genomic experiments (17). The aim of Gene Ontology (GO; http://www.geneontology.org/) is to provide access to extensive documentation and perform functional analyses (18) KEGG (http://www.genome.ad.jp/kegg/) is a comprehensive database resource, which consists of chemical information, genomic information and systems information (19). Using the DAVID online tool, GO functional analysis and KEGG pathway enrichment analysis were performed for the DEGs. In addition, the GO functional enrichment analyses was focussed predominantly on biological process (BP). P<0.05 was used as the cut-off criterion.

PPI network and module construction

The Search Tool for the Retrieval of Interacting Genes (STRING) online software (http://string-db.org/) (20) was used to identify interactions/associations between the proteins encoded by the DEGs, and a combined score >0.7 was used as the cut-off criterion. Subsequently, Cytoscape software (http://cytoscape.org/download_old_versions.html) (21) was used to visualize the PPI network and the Clique Percolation Method (CPM) was used in CFinder software (http://cfinder.org/) (22) to screen the modules of the PPI network. The clique value (k) was set to six.

Results

DEG analysis

A total of 1,170 DEGs were screened, which included 530 upregulated genes and 640 downregulated genes. There were a higher number of downregulated genes, compared with upregulated genes.

Functional analysis and pathway enrichment analysis

The top 10 most enriched GO functions for the DEGs are listed in Table I. For the upregulated genes, the enriched functions in the BP category included organelle fission (P=1.19E-09), cell cycle process (P=3.90E-09) and nuclear division (P=8.03E-09). For the downregulated genes, the enriched functions in the BP category included immune response (P=5.15E-32), defense response (P=2.69E-28) and response to wounding (P=1.40E-15).

Table I

The top 10 most enriched GO functions for the differentially expressed genes.

Table I

The top 10 most enriched GO functions for the differentially expressed genes.

GeneCategoryIDTermNumber of genesExamples of genesP-value
UpregulatedBPGO:0048285Organelle fission56KIF23, CEP721.19E-09
GO:0022402Cell cycle process45BCAT1, KIF233.90E-09
GO:0000280Nuclear division26KIF23, CEP728.03E-09
GO:0000278Mitotic cell cycle35KIF23, BCAT14.44E-09
GO:0007067Mitosis26MAD2L1, CCNB28.03E-09
DownregulatedBPGO:0006955Immune response100AQP9, TLR25.15E-32
GO:0006952Defense response89KIR2DL3, CD300LB2.69E-28
GO:0006954Inflammatory response53MBP, CD964.47E-19
GO:0002684Positive regulation of immune system process43CCL23, HRH23.47E-17
GO:0009611Response to wounding63FGF7, ACVRL11.40E-15

[i] GO, Gene Ontology; BP, biological process.

The top 10 enriched KEGG pathways for the DEGs are listed in Table II. For the upregulated genes, the enriched pathways included steroid biosynthesis (P=0.0023), ribosome (P=0.0294) and aminoacyl-tRNA biosynthesis (P=0.0125). For the downregulated genes, the enriched pathways included cell adhesion molecules (CAMs, P=3.74E-05), hematopoietic cell lineage (P=1.05E-04) and natural killer cell-mediated cytotoxicity (P=1.36E-04).

Table II

The top 10 most enriched Kyoto Encyclopedia of Genes and Genomes pathways for the differentially expressed genes.

Table II

The top 10 most enriched Kyoto Encyclopedia of Genes and Genomes pathways for the differentially expressed genes.

GeneNameNumber of genesExamples of genesP-value
Upregulatedhsa00100:Steroid biosynthesis5SQLE, DHCR70.0023
hsa00270:Cysteine and methionine metabolism6AHCY, DNMT10.0056
hsa03040:Spliceosome11SNRPA1, MAGOH0.0110
hsa00970:Aminoacyl-tRNA biosynthesis6YARS, NARS20.0125
hsa03010:Ribosome8RPL6, RPL80.0294
Downregulatedhsa04514:Cell adhesion molecules20HLA-DQB1, F11R3.74E-05
hsa05310:Asthma9CD2, SELE7.43E-05
hsa04640:Hematopoietic cell lineage15IL1R2, CR11.05E-04
hsa05332:Graft-versus-host disease10PRF1, KIR2DL31.21E-04
hsa04650:Natural killer cell mediated cytotoxicity19PRF1, PTPN61.36E-04
PPI network and module construction

The PPI network of the DEGs was revealed to have 590 nodes and 1,888 interactions (Fig. 1). A total of 10 modules were obtained from the PPI network, of which the top six modules (modules A-F) are shown in Fig. 2. The enriched pathways for the DEGs in the six modules are listed in Table III. Module A had 12 nodes and 50 interactions. In this module, all the DEGs were upregulated genes. The enriched KEGG pathways for DEGs in module A included ribosome (P=2.65E-12), which involved ribosomal protein L8 (RPL8).

Table III

Enriched Kyoto Encyclopedia of Genes and Genomes pathways for differentially expressed genes in modules A-F.

Table III

Enriched Kyoto Encyclopedia of Genes and Genomes pathways for differentially expressed genes in modules A-F.

ModuleNameNumber of genesExamples of genesP-value
A hsa03010:Ribosome8RPL6, RPL82.65E-12
Bhsa04662:B cell receptor signaling pathway5PLCγ2, SYK2.16E-07
hsa04664:Fc epsilon RI signaling pathway3PLCγ1, PLCγ2, SYK0.0023
hsa04666:Fc gamma R-mediated phagocytosis3PLCγ1, PLCγ2, SYK0.0033
hsa04650:Natural killer cell mediated cytotoxicity3PLCγ1, PLCγ2, SYK0.0064
hsa05340:Primary immunodeficiency2CD19, BLNK0.0340
hsa04664:Fc epsilon RI signaling pathway6PLCγ1, PLCγ2, SYK4.43E-09
hsa04650:Natural killer cell mediated cytotoxicity5PLCγ1, PLCγ2, SYK6.45E-06
Chsa04666:Fc gamma R-mediated phagocytosis3PLCγ1, PLCγ2, SYK0.0049
hsa04660:T cell receptor signaling pathway3ITK, PLCγ1, LCP20.0063
hsa04670:Leukocyte transendothelial migration3ITK, PLCγ1, PLCγ20.0075
hsa05310:Asthma2FCER1A, FCER1G0.0338
Dhsa00230:Purine metabolism6ADCY2, NME1, PKLR2.31E-08
hsa00240:Pyrimidine metabolism5NME2, NME16.15E-05
hsa03020:RNA polymerase2POLR3A, POLR3D0.0272
hsa04623:Cytosolic DNA-sensing pathway2POLR3A, POLR3D0.0529
E hsa04080:Neuroactive ligand-receptor interaction7C5AR1, PTGER3, FPR11.10E-05
hsa04062:Chemokine signaling pathway5CCL25, ADCY2, CCL21, CXCR6, CXCL96.95E-04
F hsa04060:Cytokine-cytokine receptor interaction4CCL25, CCL21, CXCR6, CXCL90.0210
hsa04114:Oocyte meiosis6
hsa04110:Cell cycle6CCNB1, CCNB2, MAD2L1, BUB1, ESPL1, AURKA CCNB1, CCNB2, MAD2L1, BUB1, PCNA, ESPL11.56E-05
2.91E-05
hsa03050:Proteasome4PSMB10, PSMD14, PSMA6, PSMC43.81E-04
hsa04914:Progesterone-mediated oocyte maturation4CCNB1, CCNB2, MAD2L1, BUB10.0022

Module B had six nodes and 15 interactions. In this module, the number of upregulated genes was considerably lower than the number of downregulated genes. For the DEGs in module B, the enriched KEGG pathways included the B-cell receptor signaling pathway (P=2.16E-07), Fc epsilon RI signaling pathway (P=0.0023) and natural killer cell-mediated cytotoxicity (P=0.0064). In module B, the B -cell receptor signaling pathway involved phospholipase Cγ1 (PLCγ1), spleen tyrosine kinase (SYK) and phospholipase Cγ2 (PLCγ2).

Module C had seven nodes and 20 interactions. In this module, the number of upregulated genes was markedly lower than the number of downregulated genes. The enriched KEGG pathways for the DEGs in module C included the Fc epsilon RI signaling pathway (P=4.43E-09), FcγR-mediated phagocytosis (P=0.0049) and leukocyte transendothelial migration (P=0.0075). In module C, the Fc-epsilon RI signaling pathway also involved PLCγ1, SYK and PLCγ2.

Module D had seven nodes and 20 interactions. In this module, the number of upregulated genes was higher than the number of downregulated genes. For the DEGs in module D, the enriched KEGG pathways included purine metabolism (P=2.31E-08), pyrimidine metabolism (P=6.15E-05) and the cytosolic DNA-sensing pathway (P=0.0529). The purine metabolism pathway involved adenylate cyclase 2 (ADCY2), NME/NM23 nucleoside diphosphate kinase 1 (NME1) and pyruvate kinase, liver and red blood cell (PKLR).

Module E had 15 nodes and 102 interactions. In this module, the number of upregulated genes was significantly lower than the number of downregulated genes. The enriched KEGG pathways for DEGs in module E included neuroactive ligand-receptor interaction (P=1.10E-05), chemokine signaling pathway (P=6.95E-04) and cytokine-cytokine receptor interaction (P=0.0210). The neuroactive ligand-receptor interaction pathway involved complement component 5a receptor 1 (C5AR1), prostaglandin E receptor 3 (PTGER3) and formyl peptide receptor 1 (FPR1).

Module F had 45 nodes and 609 interactions. In this module, the number of upregulated genes was significantly more than the number of downregulated genes. For the DEGs in module F, the enriched KEGG pathways included oocyte meiosis (P=1.56E-05), cell cycle (P=2.91E-05) and progesterone-mediated oocyte maturation (P=0.0022). Notably, cyclin B1 (CCNB1; degree=40), aurora kinase A (AURKA; degree=39), MAD2 mitotic arrest defective-like 1 (MAD2L1; degree=38), cell division cycle associated 8 (CDCA8; degree=38), budding uninhibited by benzimidazoles 1 (BUB1; degree=37) and maternal embryonic leucine zipper kinase (MELK; degree=37) exhibited higher degrees of connectivity. In addition, several interactive associations were identified, including MAD2L1-AURKA, MAD2L1-CDCA8, MAD2L1-BUB1 and MAD2L1-MELK.

Discussion

In the present study, a total of 1,170 DEGs were screened, including 530 upregulated genes and 640 downregulated genes. The enriched pathways for the DEGs included steroid biosynthesis and ribosome. In particular, AURKA (degree=39), MAD2L1 (degree=38), CDCA8 (degree=38), BUB1 (degree=37) and MELK (degree=37) exhibited higher degrees of connectivity in module F of the PPI network of the DEGs.

As a member of the L2P family of ribosomal proteins, RPL8 is a component of the 60S ribosomal subunit in eucaryotic cells (23). Amplification of RPL8 may be associated with the pathogenesis of OS (24). RPL8 can respond to the chemotherapy in conventional OS, and it may be beneficial in the assessment at diagnosis and for stratifying participants of randomized trials (25). A daily-repeated ribosome biogenesis inhibition can result in progressive reduction of ribosome content and extinction of protein- and p53-deficient human OS cell lines (26). In module A, the enriched ribosome pathway involved RPL8, indicating that RPL8 may be involved in OS.

PLC is important in mediating intracellular signal transduction (27). As a member of the PLC family, PLCγ1 is upregulated in several cancer cell lines and cancer tissues (28). It is also reported that PLCγ1 promotes the invasion of several types of tumor (29-31). PLCγ2 translocation is essential in transmitting TPA signal to PKCα and PKCα can directly promote the apoptosis of human cancer cell lines, thus, PLCγ2 is critical in the process of inducting apoptosis (32). The SYK gene can be reactivated by inhibition of DNA promoter methylation in human cancer and may act as a tumor suppressor (33). The B-cell receptor signaling pathway has been correlated with OS (34). In module B in the present study, the B-cell receptor signaling pathway involved PLCγ1, SYK and PLCγ2. The Fc-epsilon RI signaling pathway involves multiple component genes that are altered at the chromosome level, and may be associated with the pathogenesis of OS (5). In module C, the Fc-epsilon RI signaling pathway also involved PLCγ1, SYK and PLCγ2. Thus, it was hypothesized that PLCγ1, SYK and PLCγ2 may be closely correlated with OS. In addition, in modules B and C, the upregulated PLCγ1 was observed to have an interactive association with down-regulated SYK and PLCγ2, indicating that PLCγ1 may also be involved in OS by mediating SYK and PLCγ2.

The overexpression of MAD2 can be caused by retinoblastoma pathway inactivation and is associated with carcinogenesis (35). The expression of MAD2 is upregulated in human OS, and its overexpression is involved in earlier metastasis and poorer survival rates in patients with human OS (36). Knockdown of MAD2 can induce OS cell apoptosis, involving the cleavage of Rad21 (37). This suggests that MAD2L1 may be closely correlated with OS. The mitotic checkpoint gene, BUB1, may also drive tumor metastasis and progression (38). CDCA8 and aurora kinase B (AURKB) are overexpressed in tumor cells (39,40) and selective suppression of the CDCA8-AURKB pathway may be a promising therapeutic strategy in the treatment of cancer (41). MELK is a protein kinase and candidate oncoprotein, which is dysregulated in several types of cancer (4244), as well as being involved in resistance to apoptosis (43). In module F in the present study, MAD2L1 had interactive associations seperately with AURKA, CDCA8, BUB1 and MELK, suggesting that MAD2L1 may also be involved in OS by mediating AURKA, CDCA8, BUB1 and MELK.

In conclusion, the present study performed a comprehensive bioinformatics analysis of genes, which may be involved in OS. A total of 1,170 DEGs were screened, which including 530 upregulated genes and 640 downregulated genes. The results of the subsequent analyses suggested that RPL8, PLCγ1, PLCγ2, SYK, MAD2L1, AURKA, CDCA8, BUB1 and MELK may be correleted with OS. However, further investigations are required to elucidate their mechanisms of action in OS.

References

1 

Ottaviani G and Jaffe N: The epidemiology of osteosarcoma. Cancer Treat Res. 152:3–13. 2009. View Article : Google Scholar

2 

Ritter J and Bielack SS: Osteosarcoma. Ann Oncol. 21(Suppl 7): vii320–vii325. 2010. View Article : Google Scholar : PubMed/NCBI

3 

Stiller CA, Bielack SS, Jundt G and Steliarova-Foucher E: Bone tumours in European children and adolescents, 1978–1997. Report from the automated childhood cancer information system project. Eur J Cancer. 42:2124–2135. 2006. View Article : Google Scholar : PubMed/NCBI

4 

Stiller CA, Craft AW and Corazziari I: EUROCARE Working Group: Survival of children with bone sarcoma in Europe since 1978: results from the EUROCARE study. Eur J Cancer. 37:760–766. 2001. View Article : Google Scholar : PubMed/NCBI

5 

Yang J, Yang D, Sun Y, et al: Genetic amplification of the vascular endothelial growth factor (VEGF) pathway genes, including VEGFA, in human osteosarcoma. Cancer. 117:4925–4938. 2011. View Article : Google Scholar : PubMed/NCBI

6 

Quan GM and Choong PF: Anti-angiogenic therapy for osteosarcoma. Cancer Metast Rev. 25:707–713. 2006. View Article : Google Scholar

7 

Dvorak HF: Vascular permeability factor/vascular endothelial growth factor: A critical cytokine in tumor angiogenesis and a potential target for diagnosis and therapy. J Clin Oncol. 20:4368–4380. 2002. View Article : Google Scholar : PubMed/NCBI

8 

Lee Y, Tokunaga T, Oshika Y, et al: Cell-retained isoforms of vascular endothelial growth factor (VEGF) are correlated with poor prognosis in osteosarcoma. Eur J Cancer. 35:1089–1093. 1999. View Article : Google Scholar : PubMed/NCBI

9 

Takenaka K, Yamagishi SI, Jinnouchi Y, Nakamura K, Matsui T and Imaizumi T: Pigment epithelium-derived factor (PEDF)-induced apoptosis and inhibition of vascular endothelial growth factor (VEGF) expression in MG63 human osteosarcoma cells. Life Sci. 77:3231–3241. 2005. View Article : Google Scholar : PubMed/NCBI

10 

Miller CW, Aslo A, Tsay C, et al: Frequency and structure of p53 rearrangements in human osteosarcoma. Cancer Res. 50:7950–7954. 1990.PubMed/NCBI

11 

Mousses S, Mcauley L, Bell RS, Kandel R and Andrulis IL: Molecular and immunohistochemical identification of p53 alterations in bone and soft tissue sarcomas. Mod Pathol. 9:1–6. 1996.PubMed/NCBI

12 

Eliseev R, Dong Y, Sampson E, et al: Runx2-mediated activation of the Bax gene increases osteosarcoma cell sensitivity to apoptosis. Oncogene. 27:3605–3614. 2008. View Article : Google Scholar : PubMed/NCBI

13 

Onda M, Matsuda S, Higaki S, et al: ErbB-2 expression is correlated with poor prognosis for patients with osteosarcoma. Cancer. 77:71–78. 1996. View Article : Google Scholar : PubMed/NCBI

14 

Namløs HM, Meza-Zepeda LA, et al: Modulation of the osteosarcoma expression phenotype by microRNAs. PloS One. 7:e480862012. View Article : Google Scholar : PubMed/NCBI

15 

Irizarry RA, Hobbs B, Collin F, et al: Exploration, normalization and summaries of high density oligonucleotide array probe level data. Biostatistics. 4:249–264. 2003. View Article : Google Scholar : PubMed/NCBI

16 

Gentleman R, Carey VJ, Huber W, Irizarry RA and Dudoit S: Bioinformatics and computational biology solutions using R and Bioconductor. 746718470. Springer; New York NY: 2005, View Article : Google Scholar

17 

Huang da W, Sherman BT and Lempicki RA: Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 4:44–57. 2008. View Article : Google Scholar

18 

Harris MA, Clark J, Ireland A, et al: The Gene Ontology (GO) database and informatics resource. Nucleic Acids Res. 32:D258–D261. 2004. View Article : Google Scholar

19 

Lu M, Zhang Q, Deng M, et al: An analysis of human microRNA and disease associations. PloS One. 3:e34202008. View Article : Google Scholar : PubMed/NCBI

20 

Franceschini A, Szklarczyk D, Frankild S, et al: STRING v9.1: Protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res. 41:D808–D815. 2013. View Article : Google Scholar :

21 

Saito R, Smoot ME, Ono K, et al: A travel guide to Cytoscape plugins. Nat Med. 9:1069–1076. 2012.

22 

Palla G, Derényi I, Farkas I and Vicsek T: Uncovering the overlapping community structure of complex networks in nature and society. Nature. 435:814–818. 2005. View Article : Google Scholar : PubMed/NCBI

23 

Cui XR, Hou WR, Yang J, Hou Y and Peng ZS: Cloning and sequence analysis of ribosomal protein L8 gene (RPL8) from the (Ailuropoda melanoleuca). 2011 4th International Conference on Biomedical Engineering and Informatics; Shanghai, China. pp. 1407–1411. 2011

24 

Yang J and Zhang W: New molecular insights into osteosarcoma targeted therapy. Curr Opin Oncol. 25:398–406. 2013. View Article : Google Scholar : PubMed/NCBI

25 

Salas S, Jézéquel P, Campion L, Deville JL, Chibon F, Bartoli C, Gentet JC, Charbonnel C, Gouraud W, Voutsinos-Porche B, et al: Molecular characterization of the response to chemotherapy in conventional osteosarcomas: Predictive value of HSD17B10 and IFITM2. Int J Cancer. 125:851–860. 2009. View Article : Google Scholar : PubMed/NCBI

26 

Montanaro L, Mazzini G, Barbieri S, et al: Different effects of ribosome biogenesis inhibition on cell proliferation in retinoblastoma protein-and p53-deficient and proficient human osteosarcoma cell lines. Cell Prolif. 40:532–549. 2007. View Article : Google Scholar : PubMed/NCBI

27 

Fukami K: Structure, regulation and function of phospholipase C isozymes. J Biochem. 131:293–299. 2002. View Article : Google Scholar : PubMed/NCBI

28 

Noh DY, Kang H, Kim YC, et al: Expression of phospholipase C-gamma 1 and its transcriptional regulators in breast cancer tissues. Anticancer Res. 18:2643–2648. 1997.

29 

Mamoune A, Kassis J, Kharait S, Kloeker S, Manos E, Jones DA and Wells A: DU145 human prostate carcinoma invasiveness is modulated by urokinase receptor (uPAR) downstream of epidermal growth factor receptor (EGFR) signaling. Exp Cell Res. 299:91–100. 2004. View Article : Google Scholar : PubMed/NCBI

30 

Kochhar KS and Iyer AP: Hepatocyte growth factor induces activation of Nck and phospholipase C-γ in lung carcinoma cells. Cancer Lett. 104:163–169. 1996. View Article : Google Scholar : PubMed/NCBI

31 

Kassis J, Radinsky R and Wells A: Motility is rate-limiting for invasion of bladder carcinoma cell lines. Int J Biochem Cell Biol. 34:762–775. 2002. View Article : Google Scholar : PubMed/NCBI

32 

Zhang B, Wu Q, Ye XF, Liu S, Lin XF and Chen MC: Roles of PLC-gamma2 and PKCalpha in TPA-induced apoptosis of gastric cancer cells. World J Gastroenterol. 9:2413–2418. 2003.PubMed/NCBI

33 

Dong S, Ma L, Xu N, et al: Research on the reactivation of Syk expression caused by the inhibition of DNA promoter methylation in the lung cancer. Neoplasma. 58:89–95. 2010. View Article : Google Scholar : PubMed/NCBI

34 

Luo P, Yang X, Ying M, et al: Retinoid-suppressed phosphorylation of RARα mediates the differentiation pathway of osteosarcoma cells. Oncogene. 29:2772–2783. 2010. View Article : Google Scholar : PubMed/NCBI

35 

Agoston AT, Argani P, De Marzo AM, Hicks JL and Nelson WG: Retinoblastoma pathway dysregulation causes DNA methyltrans-ferase 1 overexpression in cancer via MAD2-mediated inhibition of the anaphase-promoting complex. Am J Pathol. 170:1585–1593. 2007. View Article : Google Scholar : PubMed/NCBI

36 

Yu L, Guo WC, Zhao SH, Tang J and Chen JL: Mitotic arrest defective protein 2 expression abnormality and its clinicopathologic significance in human osteosarcoma. Apmis. 118:222–229. 2010. View Article : Google Scholar : PubMed/NCBI

37 

Yu L, Guo W, Zhao S, Tang J and Liu J: Knockdown of Mad2 induces osteosarcoma cell apoptosis-involved Rad21 cleavage. J Orthop Sci. 16:814–820. 2011. View Article : Google Scholar : PubMed/NCBI

38 

Shichiri M, Yoshinaga K, Hisatomi H, Sugihara K and Hirata Y: Genetic and epigenetic inactivation of mitotic checkpoint genes hBUB1 and hBUBR1 and their relationship to survival. Cancer Res. 62:13–17. 2002.PubMed/NCBI

39 

Bischoff JR, Anderson L, Zhu Y, et al: A homologue of Drosophila aurora kinase is oncogenic and amplified in human colorectal cancers. EMBO J. 17:3052–3065. 1998. View Article : Google Scholar : PubMed/NCBI

40 

Branca M, Giorgi C, Santini D, et al: Survivin as a marker of cervical intraepithelial neoplasia and high-risk human papillomavirus and a predictor of virus clearance and prognosis in cervical cancer. Am J Clin Pathol. 124:113–121. 2005. View Article : Google Scholar : PubMed/NCBI

41 

Hayama S, Daigo Y, Yamabuki T, et al: Phosphorylation and activation of cell division cycle associated 8 by aurora kinase B plays a significant role in human lung carcinogenesis. Cancer Res. 67:4113–4122. 2007. View Article : Google Scholar : PubMed/NCBI

42 

Pickard MR, Green AR, Ellis IO, et al: Dysregulated expression of Fau and MELK is associated with poor prognosis in breast cancer. Breast Cancer Res. 11:R602009. View Article : Google Scholar : PubMed/NCBI

43 

Lin ML, Park JH, Nishidate T, Nakamura Y and Katagiri T: Involvement of maternal embryonic leucine zipper kinase (MELK) in mammary carcinogenesis through interaction with Bcl-G, a pro-apoptotic member of the Bcl-2 family. Breast Cancer Res. 9:R172007. View Article : Google Scholar : PubMed/NCBI

44 

Gray D, Jubb AM, Hogue D, et al: Maternal embryonic leucine zipper kinase/murine protein serine-threonine kinase 38 is a promising therapeutic target for multiple cancers. Cancer Res. 65:9751–9761. 2005. View Article : Google Scholar : PubMed/NCBI

Related Articles

Journal Cover

September-2015
Volume 12 Issue 3

Print ISSN: 1791-2997
Online ISSN:1791-3004

Sign up for eToc alerts

Recommend to Library

Copy and paste a formatted citation
x
Spandidos Publications style
Sun L, Li J and Yan B: Gene expression profiling analysis of osteosarcoma cell lines. Mol Med Rep 12: 4266-4272, 2015.
APA
Sun, L., Li, J., & Yan, B. (2015). Gene expression profiling analysis of osteosarcoma cell lines. Molecular Medicine Reports, 12, 4266-4272. https://doi.org/10.3892/mmr.2015.3958
MLA
Sun, L., Li, J., Yan, B."Gene expression profiling analysis of osteosarcoma cell lines". Molecular Medicine Reports 12.3 (2015): 4266-4272.
Chicago
Sun, L., Li, J., Yan, B."Gene expression profiling analysis of osteosarcoma cell lines". Molecular Medicine Reports 12, no. 3 (2015): 4266-4272. https://doi.org/10.3892/mmr.2015.3958