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| Year : 2009 | Volume
: 20
| Issue : 1 | Page : 52-59 |
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| A comparison between genetic portraits of normal osteoblasts and osteosarcoma cell lines |
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Annalisa Palmieri1, Furio Pezzetti1, Giorgio Brunelli2, Zollino Ilaria2, Francesco Carinci2
1 Institute of Histology, University of Bologna, Bologna, Italy
2 Maxillofacial Surgery, University of Ferrara, Ferrara, Italy
Click here for correspondence address and email
| Date of Submission | 18-Feb-2008 |
| Date of Decision | 13-Mar-2008 |
| Date of Acceptance | 28-Apr-2008 |
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 Abstract | | |
Background: Osteosarcoma (OS) is the most frequent malignant bone tumor occurring in young patients in the first two decades of life. Metastases are the cause of 90% of cancer deaths for patients with OS. OS of the jaw is rare and aggressive malignancy constitutes approximately 5-13% of all cases of skeletal OS. Chemotherapy plus surgery are the first choice for treatment.
Aims : Because OS cell lines (OCLs) should share a common pathway with primary OS and new drugs are screened in in vitro systems, new insight about the genetic profiling of OCLs is of paramount importance to a better understanding of the molecular mechanism of this rare tumor and detecting a potential target for specific therapy.
Materials and Methods : The SAOS2 and TE85 cell lines were analysed using DNA microarrays containing 19,000 genes. Several genes in which expression was significantly differentially expressed in OCLs vs. normal osteoblast (NO) were detected.
Results : The differentially expressed genes cover a broad range of functional activities: (a) cell cycle regulation, (b) cell differentiation, (c) apoptosis, and (d) immunity.
Conclusion: The reported data can be relevant to a better understanding of the biology of OS and as a model for comparing the effect of drugs used in OS treatment. Keywords: Bone, differentiation, DNA microarray, gene expression, gene profiling, osteoblast, osteoblast-like cell, osteosarcoma
How to cite this article:
Palmieri A, Pezzetti F, Brunelli G, Ilaria Z, Carinci F. A comparison between genetic portraits of normal osteoblasts and osteosarcoma cell lines. Indian J Dent Res 2009;20:52-9 |
How to cite this URL:
Palmieri A, Pezzetti F, Brunelli G, Ilaria Z, Carinci F. A comparison between genetic portraits of normal osteoblasts and osteosarcoma cell lines. Indian J Dent Res [serial online] 2009 [cited 2023 Mar 20];20:52-9. Available from: https://www.ijdr.in/text.asp?2009/20/1/52/49069 |
Osteosarcoma (OS) is a malignant mesenchymal tumor whose cancerous cells produce osteoid matrix. OS is one of the most common primary tumors of the bone that predominantly affects children; [1],[2],[3] only 5% of OS occurs in the jaw.
Metastasis, which generally occurs within 1 to 2 years, remains the primary cause of poor survival for patients and preoperative chemotherapy followed by surgery remains the only method of treatment for patients with this disease. [4],[5],[6]
Several biomarkers have been proposed to predict the evolution of OS, including Survivin, [5] ErbB2, [6] Ki67, [7] alphaV integrins, [8] and TEM7. [9] Despite all of these efforts, a reliable prognostic marker has not yet been found and the classification of the tumors is limited to clinical parameters. Molecular markers may serve to (i) stage a tumor, (ii) plan surgical treatment, and (iii) be used as an adequate chemotherapeutic regimen, possibly specific for the genetic profiling of the patient.
Because OS cell lines (OCLs) share a common pathway with primary OS and new drugs are screened in in vitro systems, new insight about the genetic profiling of OCLs is of paramount importance to a better understanding of the molecular mechanism of this rare tumor and in detecting a potential target for specific therapy.
In this study, cDNA microarray technology was employed to identify gene expression alterations in OCLs. By using 19K slides, the genetic profiling of two OCLs (i.e. TE85 and SAOS2) was compared with human normal osteoblasts (NO) derived from alveolar bone fragments and different expressed genes were detected.
| Materials and Methods | |  |
Normal Osteoblasts Cell Cultures
Three cultures of NOs have been obtained from alveolar bone fragments extracted from healthy subjects. Informed consent for a protocol reviewed and approved by the local Institutional Ethical Committee was obtained from patients to use their tissue specimens for research purposes. Bone fragments were immersed in a digestive solution: 100 U/ml penicillin, 100 µg/ml streptomycin, and 500 µg/ml claritromycin in 4 ml PBS with 3 mg/ml Type I collagenase and 4 mg/ml dispase for 1 h at 37°C.
After filtration, bone fragments were immersed in α-MEM culture medium with 22.5% FBS, 100 µM 2P-ascorbic acid, 2 mM L-glutamine, 100 U/ml penicillin, and 100 µg/ml streptomycin (all purchased from Invitrogen Celbio Italy, San Giuliano Milanese, Milan, Italy) and placed in 25 ml flasks. Flasks were incubated at 37°C in a 5% CO2 and the medium was changed twice a week.
SAOS2 and TE85 Cell Cultures
OCLs (SAOS2 and TE85) were cultured in sterile Falcon wells (Becton Dickinson, New Jersey, U.S.A.) containing Eagle's minimum essential medium (MEM) supplemented with 10% fetal calf serum (FCS) (Sigma, Chemical Co., St Louis, Mo, U.S.A.) and antibiotics (penicillin 100 U/ml and streptomycin 100 micrograms/ml - Sigma, Chemical Co., St Louis, Mo, U.S.A.). Cultures were maintained in a 5% CO2 humidified atmosphere at 37°C.
SAOS2 and TE85 cells were collected and seeded at a density of 1x10 5 cells/ml into 9 cm 2 (3 ml) wells by using 0.1% trypsin, 0.02%EDTA in Ca ++ -, and Mg - free Eagle's buffer for cell release. After 24 hours, when cultures were sub-confluent, cells were processed for RNA extraction.
DNA Microarrays Screening and Analysis
The protocol was the same as a previous experiment. [10],[11],[12] Briefly, RNA was extracted from the cells using RNAzol. RNA extracted from every NO culture, at the third passage, was pooled and used as a control. Ten micrograms of total RNA were used for each sample. cDNA was synthesized using Superscript II (Life Technologies, Invitrogen, Milano, Italy) and amino-allyl dUTP (Sigma, St. Louis, MO, U.S.A.). Mono-reactive Cy3 and Cy5 esters (Amersham Pharmacia, Little Chalfont, UK) were used for indirect cDNA labelling. RNA extracted from NO was labelled with Cy3 and used as the control against the Cy5 labelled OLCs cDNA. Human 19.2 K DNA microarrays were used (Ontario Cancer Institute, Toronto, Canada). For 19.2 K, slides hundred µl of the sample and control cDNAs in DIG Easy hybridisation solution (Roche, Basel, Switzerland) were used in a sandwich hybridisation of the two slides constituting of 19.2 K set at 37°C overnight. Washing was performed three times for 10 minutes with 1 x saline sodium citrate (SSC) and 0.1% sodium dodecyl sulfate (SDS) at 42°C three times for 5 minutes with 0.1 x SSC at room temperature. Slides were dried by centrifugation for 2 minutes at 2000 rpm. The experiment was repeated twice and the dyes were switched. A GenePix 4000a DNA microarrays scanner (Axon, Union City, CA, U.S.A.) was used to scan the slides and the data extracted with GenePix Pro. Genes with expression levels of less than 1000, after removing local background, were not included in the analysis, since ratios are not reliable at that detection level.
After scanning the two slides containing the 19.200 human genes in duplicate, the local background was calculated for each target location. GP3 software was used for global normalization. A normalization factor was estimated from ratios of the median. Normalization was performed by adding the log 2 of the normalization factor to the log 2 of the ratio of medians. The log 2 ratios for all the targets on the array were then calibrated using the normalization factor, and log 2 ratios outside the 99.7% confidence interval (the median ±3 times the SD = 0.52) were determined as significantly changed in the treated cells. Thus, genes are significantly modulated in expression when the absolute value of their log 2 expression level is higher than 1.56, or else there is a 3-fold difference in expression between treated cells and the reference. GenePix Pro software was used to report genes above the threshold and with less than 10% difference in three different statistical evaluations of the intensity ratio, thus, effectively enabling an automated quality control check of the hybridised spots. Furthermore, all the positively passed spots were visually inspected.
To select the cDNAs that distinguish between two sample groups (e.g., SAOS2 vs. NO and TE85 vs. NO), we used the Significance Analysis of Microarray (SAM) software. Specifically designed for usage with microarray data, SAM is a Microsoft Excel® plug-in that reports the most statistically significant differentially expressed genes between two groups of samples. [10],[11],[12]
| Results | | |
Hybridisation of cDNA (derived form NOs and OCLs) to cDNA microarrays allowed us to perform systemic analyses of expression profiles for thousands of genes simultaneously and to provide primary information on transcriptional changes. We identified 80 up-regulated genes and 261 down-regulated genes in OCLs.
The genes differentially expressed are reported in [Table 1] and [Table 2], and the SAM plot is shown in [Figure 1].
Up-Regulated Genes in OCLs vs. NO
Interesting genes with elevated expression in OCLs are involved in cell cycle regulation (ANAPC1, TUBG1, NUP214) and cell proliferation (IGFBP4, FTH1). ANAPC1 regulates the metaphase-to-anaphase transition, TUBG1 is a gamma-tubulin component of microtubule organizing centers, and NUP214 encodes a protein localized to the cytoplasmic face of the nuclear pore complex required for proper cell cycle progression and nucleo-cytoplasmic transport. IGFBP4 is a member of the insulin-like growth factor binding protein (IGFBP) family, while FTH1 encodes the heavy subunit of ferritin.
Additional up-regulated genes are involved in cell death, like API5 (apoptosis inhibitor 5), CTNNAL1 (an alpha-catenin-related protein), and NUP62 (a nucleoporine responsible for importing the proteins containing nuclear localization signals).
Other up-regulated genes are involved in cell differentiation: SNTA1 is a peripheral membrane protein associated with dystrophin and EIF2B1 is a GTP exchange factor essential for protein synthesis.
Among the up-regulated genes, some participate in cell transduction like KIAA0319L (that modulate intracellular calcium homoeostasis). Other genes are involved in cell adhesion like collagens COL6A1, COL11A1, and VCAM1 (a cell surface sialoglycoprotein expressed by cytokine-activated endothelium).
Other up-regulated genes participate in the immune system process: CXCL14 is a cytokine involved in immunoregulatory and inflammatory processes whereas C4A is the acidic form of complement factor 4. Deficiency of this protein is associated with systemic lupus erythematosus and Type I diabetes mellitus.
Down-Regulated Genes in OCLs vs. NO
Many down-regulated genes in OCLs are involved in cell cycle regulation like the tyrosine kinase WEE1 and the tumor suppressor gene APC. Other genes are PLCB1 (that is involved in the intracellular transduction of many extracellular signals), histone deacetylase 5 (HDAC5), and DUSP1 that may play an important role in the human cellular response to environmental stress as well as in the negative regulation of cellular proliferation.
Interestingly, down-regulated genes participate in cell proliferation like ILK (a serine/threonine protein kinase), MTCP1 associated with mature T-cell proliferations, and NF1, a negative regulator of the ras signal transduction pathway.
Also, cell death genes are regulated like CDH1 (a calcium-dependent cell-cell adhesion glycoprotein), TIA1 (involved in the induction of apoptosis), LTBR (a member of the tumor necrosis factor [TNF] family of receptors), and PRKCE (a member of the PKC family that serves as a major receptor for phorbol esters, a class of tumor promoters).
Among down-regulated genes, many are involved in cell adhesion like GNE (an enzyme that regulates biosynthesis of a precursor of sialic acids), collagenase COL6A3 and COL15A1, ABL2 (a cytoplasmic tyrosine kinaseand), and TNXB (that encodes a member of extracellular matrix proteins with anti-adhesive effects).
Down-regulated genes participate in the immunity system like IRF2 (interferon regulatory factor 2), VAV1 (a proto-oncogene that plays a role in T-cell and B-cell development and activation), SPG21 (involved in the repression of T cell activation and in the modulating of the stimulatory activity of CD4), and HLA-DPA1 (plays a central role in the immune system by presenting peptides derived from extracellular proteins).
| Discussion | | |
OS is the most frequent malignant bone tumor with a peak incidence in the second and third decade of life. Metastasis remains the primary cause of poor survival of patients with this tumor.
Evaluation of the prognosis of patients affected by OS is limited to clinical parameters whereas molecular markers of tumor aggression have not yet been identified. A biomarker that can predict poor prognosis not only has implications for treatment, but may also serve as a novel target for the development of therapy against the most aggressive form of OS.
To identify novel genes that characterize OCLs, we compared NOs and OCLs (i.e., SAOS2 and TE85) using 19.2 K cDNA microarray slides. The osteosarcoma cell lines may have different characteristics from those of the primary tumor due to the clonal variability intrinsic of cellular lines and the effects of ex vivo culture conditions. To minimize this problem, we compared NOs to 2 different cell lines (SAOS2 and TE85) and considered only differently expressed genes that are common to both lines. In this way, the genes identified are more likely representatives of the osteosarcoma transformation and less likely intrinsic characteristics of each cell line. However, we recognize that the limitations of sample size and differences between primary cultures and clonal cell lines necessitate caution in the interpretation of these studies as relevant to in vivo tumor progression.
Several genes whose expression was definitely up- or down-regulated were identified.
The antiapoptotic protein API5 was shown to be up-regulated in the investigated OCLs. A high expression of this gene was also found in lung cancer, in cervical cancer where it is a metastasis-related factor, [14] and was associated with poor survival of the patients. [13]
Another interesting up-regulated gene in OCLs is the nucleoporine NUP62. This gene imported to the nucleus the MUC1-C oncoprotein that is overexpressed by most human carcinomas. [15]
CXCL14 is a chemokines that belongs to the family of cytokines that play different roles in cancer progression including angiogenesis, inflammation, cell recruitment, and migration. Schwarze, et al. [16] found that CXCL14 mRNA up-regulation is a common feature in prostate cancer and that CXCL14 expression inhibits tumor growth suggesting tumor suppressive functions for this gene. [16] Frederik, et al. [17] demonstrates the up-regulation of CXCL14 mRNA by inflammatory cells in the tumor microenvironment and lost expression from certain cancers in vivo. The data suggest that CXCL14 may have a role in host-tumor interactions. [17]
Among down-regulated genes in OCLs, DUSP1 is relevant as this gene is down-regulated in the early event of prostate carcinomas suggesting an important role in the induction of the tumorigenesis of prostate cancer. [18]
Two other important down-regulated genes are APC and caderin CDH1. APC regulates chondrocytes, osteoblasts, and osteoclasts whereas catenin-cadherin interactions are important in regulation of bone cell activity. Abnormalities of expression of these molecules could be important in bone tumor formation and in their clinical behavior. [19] Moreover, instability of the APC gene was frequently detected in pediatric osteosarcoma. [20],[21]
In conclusion, the cDNA microarray analyses indicate a variety of changes in gene expression of OCLs. The gene identified may be useful as a prognostic indicator in the pathogenesis of OS and as a potential target for drug therapies.
| Acknowledgments | | |
This work was supported by grants from the University of Ferrara, Italy (F.C.), Fondazione CARIFE (F.C.), Fondazione CARISBO (F.P.), MIUR (project of relevant interest "Detection of neoplastic potential of stem cells derived osteoblasts" 2005 to G.P. and F.C.), 2nd University of Naples (G.P.) and from the Associazione Tumori Toscana.
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Correspondence Address:
Francesco Carinci
Maxillofacial Surgery, University of Ferrara, Ferrara
Italy
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/0970-9290.49069
[Figure 1]
[Table 1], [Table 2] |
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