Anoikis-resistant subpopulations of human osteosarcoma display significant chemoresistance and are sensitive to targeted epigenetic therapies predicted by expression profiling
- Jessica M Foley†1,
- Donald J Scholten II†2, 3,
- Noel R Monks2,
- David Cherba2,
- David J Monsma2,
- Paula Davidson2,
- Dawna Dylewski2,
- Karl Dykema2,
- Mary E Winn2 and
- Matthew R Steensma1, 2, 3Email author
© Foley et al.; licensee BioMed Central. 2015
Received: 18 December 2014
Accepted: 17 March 2015
Published: 2 April 2015
Osteosarcoma (OS) is the most common type of solid bone cancer, with latent metastasis being a typical mode of disease progression and a major contributor to poor prognosis. For this to occur, cells must resist anoikis and be able to recapitulate tumorigenesis in a foreign microenvironment. Finding novel approaches to treat osteosarcoma and target those cell subpopulations that possess the ability to resist anoikis and contribute to metastatic disease is imperative. Here we investigate anchorage-independent (AI) cell growth as a model to better characterize anoikis resistance in human osteosarcoma while using an expression profiling approach to identify and test targetable signaling pathways.
Established human OS cell lines and patient-derived human OS cell isolates were subjected to growth in either adherent or AI conditions using Ultra-Low Attachment plates in identical media conditions. Growth rate was assessed using cell doubling times and chemoresistance was assessed by determining cell viability in response to a serial dilution of either doxorubicin or cisplatin. Gene expression differences were examined using quantitative reverse-transcription PCR and microarray with principal component and pathway analysis. In-vivo OS xenografts were generated by either subcutaneous or intratibial injection of adherent or AI human OS cells into athymic nude mice. Statistical significance was determined using student’s t-tests with significance set at α = 0.05.
We show that AI growth results in a global gene expression profile change accompanied by significant chemoresistance (up to 75 fold, p < 0.05). AI cells demonstrate alteration of key mediators of mesenchymal differentiation (β-catenin, Runx2), stemness (Sox2), proliferation (c-myc, Akt), and epigenetic regulation (HDAC class 1). AI cells were equally tumorigenic as their adherent counterparts, but showed a significantly decreased rate of growth in-vitro and in-vivo (p < 0.05). Treatment with the pan-histone deacetylase inhibitor vorinostat and the DNA methyltransferase inhibitor 5-azacytidine mitigated AI growth, while 5-azacytidine sensitized anoikis-resistant cells to doxorubicin (p < 0.05).
These data demonstrate remarkable plasticity in anoikis-resistant human osteosarcoma subpopulations accompanied by a rapid development of chemoresistance and altered growth rates mirroring the early stages of latent metastasis. Targeting epigenetic regulation of this process may be a viable therapeutic strategy.
Osteosarcoma (OS) is the most common type of solid bone cancer, mainly arising in children and young adults. The long term survival for non-metastatic presentations of OS is 70%, but recurrence continues to be associated with an extremely poor prognosis . The survival rate for recurrent osteosarcoma is less than 30%, and has remained relatively constant since the initiation of doxorubicin-based chemotherapy regimens in the 1980’s . Before routine use of chemotherapy, amputation was typically the treatment for patients with localized osteosarcoma, but the 5-year survival following amputation was only 17% , confirming by natural history that hematogeneous dissemination occurs early in the disease process. Even today, with modern staging methods, micrometastatic disease is not readily detectable .
Anchorage-independent (AI) growth is an essential feature of malignancy  and metastatic progression . The cancer cells must avoid anoikis, i.e., programmed cell death in the absence of attachment to extracellular matrix, for this to take place [6-8]. Anoikis is known to occur through a Bcl-2-mediated intrinsic pathway [9,10] or through an alternative extrinsic pathway in which Apo1/Fas or TRAIL (tumor necrosis factor-related apoptosis-inducing ligand) binding to cell-surface death receptors triggers apoptosis . AI growth has been associated with disease progression and metastatic potential in breast cancer, lung cancer, and melanoma . Anoikis resistance in epithelial-lines has been associated with chemotherapy resistance and enhanced tumorigenicity [12-14], and increased expression of markers – such as Oct4, Nanog, and Sox2 – that are important in stem cell maintenance and development [12,13,15,16]. Many of these markers are known to facilitate the oncogenic epithelial-to-mesenchymal transition [17,18].
Much work using AI cell growth has been coupled with manipulation of growth factor concentrations to promote an undifferentiated and cancer stem cell-like state, without paying close attention to processes driving a cell’s ability to resist anoikis, and the phenotypic consequences of those processes. Using a model of AI cell growth, we sought to characterize the functional consequences of anoikis resistance in human osteosarcoma, particularly as they relate to the promotion of therapy resistance and recurrence, while predicting and validating novel therapies to target these cell subpopulations. We demonstrate that AI growth of established and patient-derived osteosarcoma cells alters the cellular growth rate and promotes chemotherapy resistance, while AI induced changes in growth rate persisted in-vivo. AI growth resulted in a rapid change in the gene expression profile including upregulation of the stem cell factor, sex determining region Y-box 2 (Sox2). c-Myc, β-catenin, Akt, and histone deacetylases were confirmed to be a central part of the AI growth molecular signature. Lastly, treatment with the pan-histone deacetylase inhibitor vorinostat and the DNA methyltransferase inhibitor 5-azacytidine decreased AI cell growth, while 5-azacytidine sensitized anoikis-resistant cells to doxorubicin. These data highlight the importance of epigenetic regulation in anoikis resistance and altered cell growth during osteosarcoma disease progression.
Anchorage-independent culture technique
Statistical significance was determined using a two-tailed two-sample t-test using R with significance set at α = 0.05 .
Quantitative reverse transcription-PCR (qrt-PCR)
Total RNA was isolated using TRIzol reagent (Invitrogen). Complementary DNA synthesis was performed using 500 ng RNA according to the instructions with the High Capacity cDNA Reverse Transcription Kit (Invitrogen). Primer sequences for human genes of interest are as follows (shown here, 5′ to 3′): Oct4 primers (F:TAAGCTGCGGCCCTTGCTGC; R:CGGGCCTGCACGAGGGTTTC), Sox2 primers (F:GGGGAAAGTAGTTTGCTGCCTCTT; R:TGCCGCCGCCGATGATTGT), Axin2 primers (F:AAGCAAGCGATGAGTTTGCCTGTG; R:ACAGCCAAGACAGTTCACAAGAGC), GAPDH primers (F:AACTTTGGCATTGTGGAAGG; R:GGATGCAGGGATGATGTTCT). Quantitative polymerase chain reactions were performed using SYBR Select Mastermix (Applied Biosystems, Carlsbad, CA) in 10 μL reactions. Polymerase chain reaction was performed according to the manufacturer’s instructions using a StepOnePlus cycler (Applied Biosystems). Cross-threshold (CT) values were calculated using the version 2.1 software (Applied Biosystems). Data were analyzed using the 2-ΔΔC T method . Data were presented as mean and standard deviation using Microsoft Excel, with statistical significance being determined using a two-tailed, one-sample t-test with μ = 1 using R and significance set at α = 0.05 .
Western blot analysis
Cells were washed with PBS and lysed on ice in lysis buffer (RIPA lysis buffer) supplemented with complete Protease Inhibitor Cocktail (Roche). Protein concentration was measured using the BCA assay (Pierce), and 20 μg of whole-cell lysate was run on a 10% SDS/polyacrylamide gel. The proteins were transferred onto a nitrocellulose membrane, and membranes were probed overnight at 4°C with the appropriate primary antibody; antibodies used were as follows: active β-catenin, β-catenin, Runx2 (Cell Signaling Technologies); Ki67 (Spring Bioscience); and Actin (Millipore). Membranes were then probed with a horseradish-peroxidase-conjugated secondary antibody for 1.5 hours at room temperature before detection using an enhanced chemiluminescence (ECL) detection system (Pierce). Because western blot analysis was used to support microarray and cell growth findings, images shown are representative of one independent experiment for each cell line depicted.
Gene expression profiling and analysis
RNA was isolated as described above from established (n = 3) and patient-derived (n = 7) human osteosarcoma cell lines that were cultured in adherent or anchorage-independent conditions. Total RNA was prepared separately from each individual cell line grown under adherent or anchorage-independent conditions. Established cell lines used included 143B, MNNG/HOS, and MG-63, while patient-derived cell lines included MS124, MS124-2, MS145, MS145-2, MS206, MS206-2, and MS088. Human U133 Plus 2.0 arrays were performed at Clinical Reference Laboratories (CRL, Lenexa, KS). Purified RNA (5 ng) was used for amplification of cDNA (NuGen Ovation Pico WTA System). cDNA was then fragmented and labeled (NuGen Encore Biotin Module) and hybridized to GeneChip Human Genome U133 Plus 2.0 Array (GeneChip Hybridization, Wash and Stain Kit, Affymetrix). Standard quality control metrics for percent present, scale factor, and RNA quality were evaluated for each sample. Principal component analysis (PCA) was completed using an R (ver 3.10) workflow using the BioConductor affy (ver 1.40.0) package for RMA normalization and the “prcomp” function from the stats package. Two analysis approaches were taken for differential expression analysis. Approach 1: Affymetrix CEL files for both patient-derived and established cell lines were processed with Affymetrix Expression Console using MAS5.0 normalization for the differential expressed top 300 gene list using a Welch’s T-test applied to log base 2 transformed data. The top 300 genes were imported into MetaCore from Thomson Reuters (version 6.19 build 65960) for pathway and network analysis. The top two ranked pathways identified by the Analyze network feature are shown in Additional file 1: Figure S1a and b. The Shortest paths feature with length = 1 and canonical pathways disabled was used for shortest pathway analysis. The top 300 genes are supplied in Additional file 2: Table S1, split into upregulated and downregulated groups ordered by t-statistic value. No false discovery rate correction was used because the intended purpose of the gene list was for a discovery investigation of pathways utilizing the GeneGo database. Additional file 1: Figure S1a and b shows an interaction network captured using MetaCore derived from a significant gene list. The lines that connect the gene symbols on the MetaCore image represent the direction of interaction and the type of interaction. The arrow points to the gene that is affected and the type of interaction is indicated by the color of the line. Lines with color red means inhibition, green means activation, and grey indicates an unspecified type of interaction. The concentric circles with red centers show that the gene was in the gene list and up regulated. The concentric circles with blue indicates the gene was in the gene list and was down regulated. The various gene symbols represent classes of gene types. Generic binding genes are blue “S” shaped, proteins are shown as three filled blue circles overlapping, gold arrow shapes indicate generic kinase genes and gold arrows with a hole in the center indicate a generic protease. Transcription factors are shown in red with two points on top and three on the bottom. For the official legend refer to https://ftp.genego.com/files/MC_legend.pdf. Approach 2: Affymetrix CEL files for patient-derived cell lines were imported into Bioconductor/R for processing via 3 normalization procedures (RMA, FRMA, and MAS5.0 background correction; affy package) and differential expression analysis via paired t-test (limma package). Significantly altered genes were identified as those with p < 0.05 using a Benjamini & Hochberg false discovery rate correction  across the ensemble of normalization methods.
Chemotherapy resistance assays
Passaged cells (minimum 2 passages) were dissociated and plated into 96-well Ultra Low Attachment plates (Corning) and allowed to grow for 4 days before chemotherapy exposure. Adherent cells were dissociated around 70% confluence. Cells were plated into 96-well white-walled plates (Greiner Bio-One) at 1 × 103 cells/well and allowed to adhere for 24 hr before drug treatments. The cells were then exposed to one concentration from a serial dilution of doxorubicin (0-10 μM; LC Labs) or cisplatin (0-100 μM; Sigma) for 72 hr. Cell viability was then determined using the CellTiter-Glo Luminescent Cell Viability assay (Promega). Data were normalized to an untreated control well and graphed using GraphPad Prism software, and half maximal inhibitory concentration (IC50) values were calculated from the dose-response curve as the concentration of doxorubicin or cisplatin that produced a 50% decrease in the mean cell viability relative to untreated control wells. This is a standard method to assess resistance to chemotherapy-mediated toxicity in anchorage-independent cells as done in the literature [12,13]. Data normality was determined using the Shapiro-Wilk test. Statistical tests (two-tailed paired t-test and wilcoxon signed-rank test) were performed using R  with significance set at α = 0.05.
Vorinostat and 5-azacytidine treatment
Adherent cells were dissociated around 70% confluence. Cells were plated into 96-well Ultra-Low Attachment plates (Corning) or adherent tissue culture plates at 1 × 103 cells/well in the presence of 2 μM Vorinostat (LC Labs), with dimethyl sulfoxide (DMSO; Fischer) alone used as a control. Cell viability was assessed after 4 days using the CellTiter-Glo Luminescent Cell Viability assay (Promega). For chemoresistance assays, cells were treated after 24 hours with one concentration from a serial dilution of doxorubicin, with cell viability being measured after an additional 72 hours as described above. Pretreatment used either 2 μM Vorinostat or DMSO alone for 12 days in adherent conditions before plating the cells in 96-well Ultra Low Attachment plates or adherent plates. 5-azacytidine (5-azaC, Sigma) was dissolved in 50 mM sodium phosphate (vehicle), and adherent OS cells were treated with 2 μM 5-azaC or the same dilution of vehicle alone for 24 hours before being plated in 96-well Ultra-Low Attachment plates or adherent tissue culture plates in the absence of 5-azaC or vehicle. Cell viability or chemoresistance was then assessed as described above. Data were graphed using GraphPad Prism software, and two-tailed one-sample t-tests were performed using R with significance set at α = 0.05 .
Orthotopic and non-orthotopic xenograft experiments
Following the approval of the research protocol by the IACUC committee at the Van Andel Institute (IACUC protocol #: 13-09-031 and XPA-14-02-004), cells subjected to either AI or adherent growth were dissociated into single-cell suspensions and were injected into the flank of or intratibially into athymic nude mice. For intratibial injection, mice were anesthetized via inhaled isoflurane and cells were injected in a total volume of 20 μL through the tibial plateau. All efforts were made to minimize suffering. Tumors were allowed to grow to 1500 mm3 or until euthanasia criteria were met. Tumor volume calculations of intratibial tumors were done using the formula V = (length × width2)/2 with the contralateral leg as a control. Volumes were statistically compared at each time point using a two-tailed two-sample t-test with significance set at α = 0.05.
Anoikis-resistant OS cells robustly maintain viability and exhibit sustained but down-regulated rates of growth
Anoikis-resistant OS cells have increased resistance to chemotherapy
72 hr IC 50 for AI and adherent OS cells in the presence of doxorubicin or cisplatin
Mean IC50 (uM)
Mean IC50 (uM)
0.062 ± 0.01
4.38 ± 0.98
1.79 ± 0.4
25.2 ± 6.9
0.025 ± 0.01
1.97 ± 0.19
0.31 ± 0.1
3.12 ± 0.10
0.085 ± 0.013
3.10 ± 0.53
2.97 ± 1.9
21.4 ± 10.
0.13 ± 0.06
2.68 ± 1.6
16.9 ± 5.4
Anoikis-resistant OS cells are molecularly distinct
Significantly altered genes in AI OS cells compared to adherent OS cells
Anoikis-resistant OS cells initiate tumors equal to adherent OS cells in both orthotopic and non-orthotopic in-vivo xenograft models, but maintain a lower growth rate
Proportion of mice that formed tumors after xenotransplantation of AI or adherent OS cells
Number of cells injected
1 × 10 4
1 × 10 5
5 × 10 5
Targeted epigenetic therapies decrease OS cell anchorage-independent growth and sensitize to doxorubicin
The goal of our work was to use gene expression analysis to predict and test targeted therapies to mitigate aspects of OS anoikis resistance. Many of the alterations we noticed on expression profiling can be regulated through epigenetic modifiers, raising the potential for the use of existing FDA-approved epigenetic therapies to target anoikis-resistant OS cells. Proliferation of c-myc overexpressing lymphomas is reduced by HDAC inhibition, and it has been demonstrated in osteosarcoma cells that chromatin-modifying drugs such as vorinostat can increase miRNA expression that leads to decreased c-myc protein and induction of apoptosis [31,34]. 5-azaC treatment inhibits proliferation of pancreatic cancer cells through suppression of β-catenin/c-myc, while studies in OS cells show that 5-azaC treatment increases expression of the PI3K/Akt inhibitor PTEN [32,33]. Our gene expression profiling identified c-myc, β-catenin, Akt and class I HDACs as important contributors to AI growth, thus strengthening the rationale for the use of HDAC inhibitors and 5-azaC in targeting these cell subpopulations. We found that treatment with the HDAC inhibitor vorinostat resulted in decreased AI cell growth. Additionally, pretreatment of adherent cells with vorinostat inhibited their ability to subsequently grow in AI conditions. More work is needed to determine which HDACs are involved, as well as the specific histone acetylation changes taking place that are important for anchorage-independent growth and anoikis resistance. We also found that treatment with 5-azaC reduced AI cell growth and sensitized anoikis-resistant cells to doxorubicin-mediated toxicity. The ability of 5-azaC to sensitize cancer cells to chemotherapy has been observed, particularly in ovarian cancer . Additionally, 5-azaC has been shown to inhibit the Wnt/β-catenin signaling pathway, an important contributor to chemoresistance in human OS [33,36,37]. Future studies include examining changes in levels of DNA methyltransferases as well as determining important genomic loci of DNA methylation in anoikis-resistant OS cells. In-vivo assessment of the efficacy of vorinostat and 5-azaC in OS xenografts will also be an area of future study.
Intuitively, acquired chemotherapy resistance to anti-proliferative agents can be greatly affected by the rate of cell division. We noticed that OS cells exhibit a decreased growth rate under AI conditions. Indeed, the literature supports mechanisms of altered growth rate in response to AI growth that involve changes in key cell cycle regulators such as cyclin D1 . Another study in ovarian cancer found that not all cells respond to AI growth with cell cycle arrest, suggesting heterogenous and complex upstream molecular mechanisms . Although more work is required to define the specific mechanism for this growth alteration, we found a variety of candidate mechanisms that would also account for rapid acquisition of chemoresistance beyond a decreased growth rate. We observed increased expression of Sox2 in response to AI growth, which is essential for self-renewal and tumorigenicity in OS tumor-initiating cells . Sox2 has also been implicated in conveying chemoresistance to cancer stem cell-like cells in other cancers through ABCG2 expression levels . Comparison of the gene expression profiles of AI and adherent OS cells from freshly harvested patient tumors confirms a common adaptation process underlying resistance to anoikis and chemotherapy. Unbiased molecular pathway analysis of the top 300 differentially expressed genes identified consistent signaling convergence on Wnt signaling, Runx2, c-myc, and components of histone modification. Aberrant Wnt signaling contributes to OS tumorigenesis, and inhibition of Wnt signaling effectively sensitizes OS cells to chemotherapy [36,37,42]. c-myc overexpression is transformative to bone marrow stromal cells and conveys invasive properties to OS cells and resistance to cisplatin in other cancer cell lines [43-45], while many signaling events that are mediated by c-myc require functioning HDACs [29,30]. Furthermore, increased expression of Runx2 can suppress proliferation and chemosensitivity of OS cells and has been shown to inhibit p53-dependent apoptosis together with HDACs in response to DNA damage [46-48]. Taken together, these pathways represent candidate contributors to the chemoresistance of anchorage-independent human OS cells observed in our study beyond altered cell growth. It is worth noting that the expression profile of the chemically transformed, highly clonal established OS cells showed less dramatic changes in the anoikis-resistant gene signature. There was marked agreement between the findings of the pathway analysis and the individual differentially expressed genes identified, particularly as they relate to overall processes such as cell growth and oncogenic signaling. In fact, Runx2 was identified in both analyses as a significant contributor to OS anchorage-independent growth. Future work will include interrogation of the pathway and gene alterations identified to determine their specific contributions towards the phenotypic consequences of OS anoikis resistance.
We found that adherent and AI-grown OS cells were equally tumorigenic in subcutaneous and intratibial settings; the latter is arguably more similar to the osteosarcoma tumor microenvironment. Prior work shows that AI-grown OS cells are more capable of tumor initiation, and the reasons for this difference are unclear [12,14]. Though we were unable to demonstrate a greater tumor-initiating potential, cells cultivated in AI conditions maintain their decreased growth rate relative to adherent OS cells in-vivo. It will be important in future studies to examine metastatic rates between adherent and AI grown OS cells. These data indicate that the phenotypic behavior of AI-grown cells is not a transient finding.
The cultivation of human osteosarcoma cells through anchorage-independent growth alters the growth rate, promotes chemotherapy resistance, and produces a distinct molecular signature both in-vitro and in-vivo. We show that acquisition of anoikis resistance in human osteosarcoma subpopulations through anchorage-independent growth results in a rapid development of chemoresistance and altered growth rate, mirroring the early stages of latent metastasis. Targeting epigenetic regulation of this process may be a viable therapeutic strategy.
The authors thank the VARI Vivarium staff, the VARI Bioinformatics core, Amanda Schuiling for help with data analysis, Anthony Popkie for the 5-azaC, and David Nadziejka for technical writing assistance. We also thank the Helen DeVos Children’s Hospital Foundation, the Hyundai Hope on Wheels Foundation, and Spectrum Health for financial support of this work.
- Geller DS, Gorlick R. Osteosarcoma: a review of diagnosis, management, and treatment strategies. Clin Adv Hematol Oncol. 2010;8:705–18.PubMedGoogle Scholar
- Marcove RC, Mike V, Hajek JV, Levin AG, Hutter RV. Osteogenic sarcoma under the age of twenty-one. A review of one hundred and forty-five operative cases. J Bone Joint Surg Am. 1970;52:411–23.PubMedGoogle Scholar
- Marina N, Gebhardt M, Teot L, Gorlick R. Biology and therapeutic advances for pediatric osteosarcoma. Oncologist. 2004;9:422–41.View ArticlePubMedGoogle Scholar
- Freedman VH, Shin SI. Cellular tumorigenicity in nude mice: correlation with cell growth in semi-solid medium. Cell. 1974;3:355–9.View ArticlePubMedGoogle Scholar
- Gassmann P, Haier J. The tumor cell-host organ interface in the early onset of metastatic organ colonisation. Clin Exp Metastasis. 2008;25:171–81.View ArticlePubMedGoogle Scholar
- Paoli P, Giannoni E, Chiarugi P. Anoikis molecular pathways and its role in cancer progression. Biochim Biophys Acta. 1833;2013:3481–98.Google Scholar
- Mori S, Chang JT, Andrechek ER, Matsumura N, Baba T, Yao G, et al. Anchorage-independent cell growth signature identifies tumors with metastatic potential. Oncogene. 2009;28:2796–805.View ArticlePubMed CentralPubMedGoogle Scholar
- Douma S, Van Laar T, Zevenhoven J, Meuwissen R, Van Garderen E, Peeper DS. Suppression of anoikis and induction of metastasis by the neurotrophic receptor TrkB. Nature. 2004;430:1034–9.View ArticlePubMedGoogle Scholar
- Chiarugi P, Giannoni E. Anoikis: a necessary death program for anchorage-dependent cells. Biochem Pharmacol. 2008;76:1352–64.View ArticlePubMedGoogle Scholar
- Simpson CD, Anyiwe K, Schimmer AD. Anoikis resistance and tumor metastasis. Cancer Lett. 2008;272:177–85.View ArticlePubMedGoogle Scholar
- Reddig PJ, Juliano RL. Clinging to life: cell to matrix adhesion and cell survival. Cancer Metastasis Rev. 2005;24:425–39.View ArticlePubMedGoogle Scholar
- Tang QL, Zhao ZQ, Li JC, Liang Y, Yin JQ, Zou CY, et al. Salinomycin inhibits osteosarcoma by targeting its tumor stem cells. Cancer Lett. 2011;311:113–21.View ArticlePubMedGoogle Scholar
- Martins-Neves SR, Lopes AO, do Carmo A, Paiva AA, Simoes PC, Abrunhosa AJ, et al. Therapeutic implications of an enriched cancer stem-like cell population in a human osteosarcoma cell line. BMC Cancer. 2012;12:139.View ArticlePubMed CentralPubMedGoogle Scholar
- Zhang H, Wu H, Zheng J, Yu P, Xu L, Jiang P, et al. Transforming growth factor beta1 signal is crucial for dedifferentiation of cancer cells to cancer stem cells in osteosarcoma. Stem Cells. 2013;31:433–46.View ArticlePubMedGoogle Scholar
- Fujii H, Honoki K, Tsujiuchi T, Kido A, Yoshitani K, Takakura Y. Sphere-forming stem-like cell populations with drug resistance in human sarcoma cell lines. Int J Oncol. 2009;34:1381–6.PubMedGoogle Scholar
- Gibbs CP, Kukekov VG, Reith JD, Tchigrinova O, Suslov ON, Scott EW, et al. Stem-like cells in bone sarcomas: implications for tumorigenesis. Neoplasia. 2005;7:967–76.View ArticlePubMed CentralPubMedGoogle Scholar
- Tsai LL, Hu FW, Lee SS, Yu CH, Yu CC, Chang YC. Oct4 mediates tumor initiating properties in oral squamous cell carcinomas through the regulation of epithelial-mesenchymal transition. PLoS One. 2014;9:e87207.View ArticlePubMed CentralPubMedGoogle Scholar
- Guo P, Gao A, Zhang G, Han H, Zhou Q. Decoding the knots of initiation of oncogenic epithelial-mesenchymal transition in tumor progression. Curr Cancer Drug Targets. 2013;13:996–1011.View ArticlePubMedGoogle Scholar
- Team RC. R: A Language and environment for statistical computing. R Foundation for Statistical Computing: Vienna, Austria; 2013.Google Scholar
- Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods. 2001;25:402–8.View ArticlePubMedGoogle Scholar
- Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Royal Stat Soc. 1995;57:289–300.Google Scholar
- Diaz-Montero CM, McIntyre BW. Acquisition of anoikis resistance in human osteosarcoma cells. Eur J Cancer. 2003;39:2395–402.View ArticlePubMedGoogle Scholar
- Cho SH, Ahn AK, Bhargava P, Lee CH, Eischen CM, McGuinness O, et al. Glycolytic rate and lymphomagenesis depend on PARP14, an ADP ribosyltransferase of the B aggressive lymphoma (BAL) family. Proc Natl Acad Sci U S A. 2011;108:15972–7.View ArticlePubMed CentralPubMedGoogle Scholar
- Nunes-Xavier CE, Elson A, Pulido R. Epidermal growth factor receptor (EGFR)-mediated positive feedback of protein-tyrosine phosphatase epsilon (PTPepsilon) on ERK1/2 and AKT protein pathways is required for survival of human breast cancer cells. J Biol Chem. 2012;287:3433–44.View ArticlePubMed CentralPubMedGoogle Scholar
- Pei XH, Lv XQ, Li HX. Sox5 induces epithelial to mesenchymal transition by transactivation of Twist1. Biochem Biophys Res Commun. 2014;446:322–7.View ArticlePubMedGoogle Scholar
- Ozaki T, Sugimoto H, Nakamura M, Hiraoka K, Yoda H, Sang M, et al. Runt-related transcription factor 2 attenuates the transcriptional activity as well as DNA damage-mediated induction of pro-apoptotic TAp73 to regulate chemosensitivity. FEBS J. 2015;282:114–28.View ArticlePubMed CentralPubMedGoogle Scholar
- Barton CA, Gloss BS, Qu W, Statham AL, Hacker NF, Sutherland RL, et al. Collagen and calcium-binding EGF domains 1 is frequently inactivated in ovarian cancer by aberrant promoter hypermethylation and modulates cell migration and survival. Br J Cancer. 2010;102:87–96.View ArticlePubMed CentralPubMedGoogle Scholar
- Jiang CG, Lv L, Liu FR, Wang ZN, Liu FN, Li YS, et al. Downregulation of connective tissue growth factor inhibits the growth and invasion of gastric cancer cells and attenuates peritoneal dissemination. Mol Cancer. 2011;10:122.View ArticlePubMed CentralPubMedGoogle Scholar
- Marshall GM, Gherardi S, Xu N, Neiron Z, Trahair T, Scarlett CJ, et al. Transcriptional upregulation of histone deacetylase 2 promotes Myc-induced oncogenic effects. Oncogene. 2010;29:5957–68.View ArticlePubMedGoogle Scholar
- Kurland JF, Tansey WP. Myc-mediated transcriptional repression by recruitment of histone deacetylase. Cancer Res. 2008;68:3624–9.View ArticlePubMedGoogle Scholar
- Zappasodi R, Cavane A, Iorio MV, Tortoreto M, Guarnotta C, Ruggiero G, et al. Pleiotropic antitumor effects of the pan-HDAC inhibitor ITF2357 against c-Myc-overexpressing human B-cell non-Hodgkin lymphomas. Int J Cancer. 2014;135:2034–45.View ArticlePubMedGoogle Scholar
- Song D, Ni J, Xie H, Ding M, Wang J. DNA demethylation in the PTEN gene promoter induced by 5-azacytidine activates PTEN expression in the MG-63 human osteosarcoma cell line. Exp Ther Med. 2014;7:1071–6.PubMed CentralPubMedGoogle Scholar
- Zhang H, Zhou WC, Li X, Meng WB, Zhang L, Zhu XL, et al. 5-Azacytidine suppresses the proliferation of pancreatic cancer cells by inhibiting the Wnt/beta-catenin signaling pathway. Genet Mol Res. 2014;13:5064–72.View ArticlePubMedGoogle Scholar
- Thayanithy V, Park C, Sarver AL, Kartha RV, Korpela DM, Graef AJ, et al. Combinatorial treatment of DNA and chromatin-modifying drugs cause cell death in human and canine osteosarcoma cell lines. PLoS One. 2012;7:e43720.View ArticlePubMed CentralPubMedGoogle Scholar
- Oronsky B, Oronsky N, Scicinski J, Fanger G, Lybeck M, Reid T. Rewriting the epigenetic code for tumor resensitization: a review. Transl Oncol. 2014;7:626–31.View ArticlePubMed CentralPubMedGoogle Scholar
- Ma Y, Ren Y, Han EQ, Li H, Chen D, Jacobs JJ, et al. Inhibition of the Wnt-beta-catenin and Notch signaling pathways sensitizes osteosarcoma cells to chemotherapy. Biochem Biophys Res Commun. 2013;431:274–9.View ArticlePubMed CentralPubMedGoogle Scholar
- Scholten 2nd DJ, Timmer CM, Peacock JD, Pelle DW, Williams BO, Steensma MR. Down regulation of Wnt signaling mitigates hypoxia-induced chemoresistance in human osteosarcoma cells. PLoS One. 2014;9:e111431.View ArticlePubMed CentralPubMedGoogle Scholar
- Gan L, Liu P, Lu H, Chen S, Yang J, McCarthy JB, et al. Cyclin D1 promotes anchorage-independent cell survival by inhibiting FOXO-mediated anoikis. Cell Death Differ. 2009;16:1408–17.View ArticlePubMed CentralPubMedGoogle Scholar
- Carduner L, Picot CR, Leroy-Dudal J, Blay L, Kellouche S, Carreiras F. Cell cycle arrest or survival signaling through alphav integrins, activation of PKC and ERK1/2 lead to anoikis resistance of ovarian cancer spheroids. Exp Cell Res. 2014;320:329–42.View ArticlePubMedGoogle Scholar
- Basu-Roy U, Seo E, Ramanathapuram L, Rapp TB, Perry JA, Orkin SH, et al. Sox2 maintains self renewal of tumor-initiating cells in osteosarcomas. Oncogene. 2012;31:2270–82.View ArticlePubMed CentralPubMedGoogle Scholar
- Lee SH, Oh SY, Do SI, Lee HJ, Kang HJ, Rho YS, et al. SOX2 regulates self-renewal and tumorigenicity of stem-like cells of head and neck squamous cell carcinoma. Br J Cancer. 2014;111:2122–30.View ArticlePubMedGoogle Scholar
- Rubin EM, Guo Y, Tu K, Xie J, Zi X, Hoang BH. Wnt inhibitory factor 1 decreases tumorigenesis and metastasis in osteosarcoma. Mol Cancer Ther. 2010;9:731–41.View ArticlePubMed CentralPubMedGoogle Scholar
- Han G, Wang Y, Bi W. C-Myc overexpression promotes osteosarcoma cell invasion via activation of MEK-ERK pathway. Oncol Res. 2012;20:149–56.View ArticlePubMedGoogle Scholar
- Shimizu T, Ishikawa T, Sugihara E, Kuninaka S, Miyamoto T, Mabuchi Y, et al. c-MYC overexpression with loss of Ink4a/Arf transforms bone marrow stromal cells into osteosarcoma accompanied by loss of adipogenesis. Oncogene. 2010;29:5687–99.View ArticlePubMedGoogle Scholar
- Pyndiah S, Tanida S, Ahmed KM, Cassimere EK, Choe C, Sakamuro D. c-MYC suppresses BIN1 to release poly(ADP-ribose) polymerase 1: a mechanism by which cancer cells acquire cisplatin resistance. Sci Signal. 2011;4:ra19.View ArticlePubMedGoogle Scholar
- Ozaki T, Wu D, Sugimoto H, Nagase H, Nakagawara A. Runt-related transcription factor 2 (RUNX2) inhibits p53-dependent apoptosis through the collaboration with HDAC6 in response to DNA damage. Cell Death Dis. 2013;4:e610.View ArticlePubMed CentralPubMedGoogle Scholar
- Lucero CM, Vega OA, Osorio MM, Tapia JC, Antonelli M, Stein GS, et al. The cancer-related transcription factor Runx2 modulates cell proliferation in human osteosarcoma cell lines. J Cell Physiol. 2013;228:714–23.View ArticlePubMedGoogle Scholar
- Ozaki T, Sugimoto H, Nakamura M, Hiraoka K, Yoda H, Sang M, et al. Runt-related transcription factor 2 attenuates the transcriptional activity as well as DNA damage-mediated induction of pro-apoptotic TAp73 to regulate chemosensitivity. FEBS J. 2015;282:114–28.View ArticlePubMed CentralPubMedGoogle Scholar
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