Potential Involvement of MYC- and p53-Related Pathways in Tumorigenesis in Human Oral Squamous Cell Carcinoma Revealed by Proteomic Analysis Jadranka Koehn,†,‡,§ Kurt Krapfenbauer,†,§ Susanna Huber,§ Elisabeth Stein,‡ Walter Sutter,‡ Franz Watzinger,‡ Boban M. Erovic,| Dietmar Thurnher,| Thomas Schindler,⊥ Michael Fountoulakis,⊥ and Dritan Turhani*,‡ Department of Cranio-Maxillofacial and Oral Surgery, Medical University of Vienna, Vienna, Austria, Novartis Institutes for Biomedical Research, Novartis, Vienna, Austria, Department of Otorhinolaryngology, Medical University of Vienna, Vienna, Austria, and Roche Center for Medical Genomics, F. Hoffmann-La Roche, Basel, Switzerland Received January 30, 2008
Oral squamous cellular carcinoma is a malignant tumor with poor prognosis. Discovery of early markers to discriminate between malignant and normal cells is of high importance in clinical diagnosis. Subcellular fractions from 10 oral squamous cell carcinoma and corresponding control samples, enriched in mitochondrial and cytosolic proteins, as well as blood from the tumor were analyzed by proteomics, two-dimensional gel electrophoresis, followed by matrix-assisted laser desorption ionization time-of-flight mass spectrometry. Three-hundred and fifty different gene products were identified. Twenty proteins showed deranged levels in oral squamous cell carcinoma in comparison with the control samples and are potentially involved in tumor growth and metastasis. Of these, 16 proteins were upregulated. By applying pathway analysis, we found 8 of the upregulated gene products to be linked to three main locus genes, p53, MYC, and MYCN, and could be candidate biomarkers for OSCC. The findings of this pilot study show that OSCC gene ontology combined with proteomic analysis is a powerful tool in systems biology for the elucidation of the complexity of expression profiles in cellular processes. Application of such pathway analysis has the potential to generate new insights into complex molecular mechanisms underlying disease related processes and could therefore significantly contribute to the efficient performance of the entire discovery process. Keywords: oral squamous cell carcinoma (OSCC) • proteomics • MYC • p53 • protein pathway profiling • two-dimensional database • systems biology
1. Introduction Oral, head, and neck squamous cellular carcinoma (OSCC) is one of the most common forms of cancer,1 and the identification of markers to discriminate tumor from normal cells, as well as the different stages of the pathology, are of critical importance. Proteomics has been applied in the study of different kinds of cancers, that is, breast, lung, and colon cancer.2,3 Together with genomics, proteomics is well on the way to molecularly characterize the different tumor types and to detect diagnostic and novel therapeutic targets for disease treatment.4,5 In functional proteomics, methods have also been developed to study the intracellular signaling pathways that * To whom correspondence should be addressed. Dritan Turhani, Department of Cranio-Maxillofacial and Oral Surgery, Medical University of Vienna, Wa¨hringer Gu ¨ rtel 18-20, A-1090 Vienna, Austria. Phone: +43 1 40400 2891. Fax: +43 1 40400 4253. E-mail:
[email protected]. † These authors contributed equally to the present study. ‡ Department of Cranio-Maxillofacial and Oral Surgery, Medical University of Vienna. § Novartis Institutes for Biomedical Research. | Department of Otorhinolaryngology, Medical University of Vienna. ⊥ Roche Center for Medical Genomics.
3818 Journal of Proteome Research 2008, 7, 3818–3829 Published on Web 08/14/2008
underlie the development of cancers, facts which allow us to predict additional signaling proteins and gene products, which are involved in various pathways, like those of ERK/MAPK, p38 MAPK, or PI*K/AKT, and to decipher the complex signaling circuitry involved in tumor growth.6 The large scale proteomic analysis of human protein profiles has become a reality due to (i) the sequencing of the human genome, (ii) the development of protein purification and enrichment methods as well as of sensitive mass spectrometry (MS) based strategies for protein characterization, and (iii) the development of bioinformatics tools, allowing the compilation of searchable genomic and proteomic databases accessible via the Internet. As a consequence of the introduction of such a large scale analysis, various two-dimensional protein databases are now freely available. The databases are useful tools in the detection and quantification of differences in the protein levels between control and diseased states.7,8 2-DE analysis of a limited number of OSCC cell lines was performed earlier,9 but study of OSCC cells in culture will primarily allow for the selective identification of polypeptides regulated in the cancer cells only and which is only partly representative for the whole 10.1021/pr800077a CCC: $40.75
2008 American Chemical Society
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MYC- and p53-Related Pathways in OSCC Table 1. Proteins with Increased Levels in Oral Squamous Cell Carcinoma (OSCC) protein levels protein name
acc. number
control, SM
OSCC, TC
change
p value
Annexin II Cofilin C-reactive protein precursor Creatine kinase, M-chain Cytokeratin 6B Fatty acid binding protein Glutathione S-transferase HSP 27 Keratin type II Nucleoside diphosphate kinase A Phosphoglycerate mutase 1 Pyruvate kinase m1 Retinoblastoma like protein 2 Retinoic acid binding protein II SCCA-1 Stratifin
P07355 P23528 P02741 P06732 P04259 Q01469 P09211 P04792 P04259 P15531 P18669 P14618 Q08999 P29373 P29508 P31947
0.73 ( 0.22 0.94 ( 0.26 1.19 ( 0.32 3.85 ( 1.23 0.18 ( 0.14 0.32 ( 0.17 0.89 ( 0.15 0.63 ( 0.32 0.99 ( 0.35 0.78 ( 0.39 0.76 ( 0.18 0.54 ( 0.18 0.45 ( 0.14 0.97 ( 0.45 0.83 ( 0.65 5.27 ( 1.56
1.17 ( 0.13 2.07 ( 0.35 2.98 ( 0.93 16.2 ( 3.52 0.94 ( 0.02 2.84 ( 0.39 1.69 ( 0.34 1.53 ( 0.40 2.12 ( 0.83 5.07 ( 0.56 1.59 ( 0.21 1.37 ( 0.30 0.68 ( 0.21 3.78 ( 1.33 2.65 ( 0.89 7.91 ( 2.01
1.6 2.2 2.5 4.2 5.2 8.9 1.9 2.4 2.1 6.5 2.1 2.5 1.5 3.9 3.2 1.5
0.002 0.003 0.004 0.005 0.005 0.005 0.005 0.003 0.002 0.005 0.002 0.003 0.001 0.004 0.004 0.002
OSCC protein profile. To study the protein expression profiling of tumor tissue, we chose 10 pairs of primary OSCC tumor tissue and their matched adjacent healthy surrounding mucosa specimens. We analyzed the OSCC proteome and the differential protein expression in healthy and diseased states and assigned a number of the changed proteins to known pathways.
2. Materials and Methods 2.1. Materials. Immobilized pH-gradient (IPG) strips and IPG buffers were purchased from GE Healthcare (Uppsala, Sweden). Acrylamide/piperazine-diacrylamide (PDA) solution (37.5:1, w/v) was purchased from Biosolve Ltd. (Valkenswaard, The Netherlands) and the other reagents for the polyacrylamide gel preparation were from Bio-Rad Laboratories (Hercules, CA). CHAPS was obtained from Roche Diagnostics (Mannheim, Germany), urea from AppliChem (Darmstadt, Germany), thiourea from Fluka (Buchs, Switzerland), 1,4-dithioerythritol (DTE) and EDTA from Merck (Darmstadt, Germany), and tributylphosphine (TBP) from Pierce Biotechnology (Rockford, IL). The reagents were kept at 4 °C. 2.2. Patient Material. Ten pairs of surgery specimens of primary OSCC and their matched adjacent normal surrounding mucosa specimens from five male and five female patients were obtained from archived material of the General Hospital of Vienna following routine pretreatment panendoscopy (Medical University of Vienna). The study was approved by the ethical committee of the General Hospital of Vienna (EK Nr: 075/2002 and 468/2007). However, for this preliminary study, only archival material was used. The staging of the OSCC samples was determined by the Tumor-Node-Metastases (TNM) system according to the American Joint Committee on Cancer (AJCC).10 The stage of the cancer was defined by the extent of the lesion and was determined by physical examination, radiological studies, and pathologic examination. Examination of tumor specimen in comparison to the surrounding mucosal tissue was further complemented by histological characterization and showed 66 ( 34% tumor cells in each tumor center. Tumor samples from seven OSCC patients were obtained from T3 cancers, three biopsies from T4 cancers, therefore all advanced malignancies. Upon histological examination, no tumor cells were detected at all in the surrounding mucosa samples. On the basis of the results of the histological characterization, the tissue selection was clearly defined due to
variations in cell composition and proliferation stage. Tissue samples were stored at -80 °C, and the freezing chain was not interrupted until analysis. Each sample was analyzed twice. The results of the individual analyses correlated well with each other without unexpected deviations. Statistical deviations are indicated in Table 1 and Supplementary Table S3 (Supporting Information). BCC samples were obtained from Astarand (Detroit, MI). 2.3. Sample Preparation. To remove excess blood from the tissues (which may interfere with the 2DE-PAGE analysis), whole tumors were washed 3 times in sucrose buffer consisting of 20 mM HEPES, pH 7.5, 320 mM sucrose, 1 mM EDTA, 5 mM DTE, and 1 mg/mL of a mixture of protease inhibitors (1 mM PMSF and 1 tablet complete(Roche Diagnostics) per 50 mL of wash buffer and phosphatase inhibitors (0.2 mM Na3VO3 and 1 mM NaF). The tissue was further homogenized with 10 strokes using a glass/Teflon homogenizer after Potter in a buffer consisting of 20 mM HEPES, pH 7.5, 280 mM sucrose, 1 mM EDTA, 5 mM DTE, and protease and phosphatase inhibitors as above. The suspension was centrifuged at 800 × g for 10 min at 4 °C to sediment the undissolved material. The supernatant was centrifuged at 10 000 × g for 15 min to obtain the enriched mitochondrial fraction. The supernatant of this step was centrifuged at 100 000 × g for 1 h to generate the supernatant representing the cytosolic fraction. Material from the blood, mitochondrial and cytosol fractions were suspended in 0.5 mL of IEF sample buffer consisting of 40 mM Tris, 7 M urea, 2 M thiourea, 4% CHAPS, 10 mM DTT, 1 mM EDTA, and 1 mM PMSF. The suspension was left at room temperature for 1 h and centrifuged at 14 000 × g for 60 min. Desalting was performed with Ultrafree-4 centrifugal filter unit (Millipore). The protein content in the supernatant was determined using the Coomassie blue method.11 2.4. Two-Dimensional Electrophoresis. Two-dimensional gel electrophoresis was performed essentially as reported.12,13 Samples of 1 mg protein were applied on immobilized pH 3-10 nonlinear gradient strips in sample cups at their basic and acidic ends. Focusing started at 200 V and the voltage was gradually increased to 8000 at 4 V/min and kept constant for a further 3 h (approximately 150 000 Vh totally). The second dimensional separation was performed in 12% SDS-polyacrylamide gels. The gels (180 × 200 × 1.5 mm3) were run at 40 mA/gel. After protein fixation in 50% methanol, containing 5% Journal of Proteome Research • Vol. 7, No. 9, 2008 3819
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Figure 1. Two-dimensional map of blood from human oral squamous cell carcinoma proteins. The proteins were separated on a pH 3-10 nonlinear IPG strip, followed by a 12% SDS-polyacrylamide gel, as stated under Materials and Methods. The gel was stained with Coomassie blue. The spots were analyzed by MALDI-MS. The proteins identified are designated with their accession numbers. The names of the proteins are listed in Table S1 (Supporting Information).
phosphoric acid for 2 h, the gels were stained with colloidal Coomassie blue (Novex, San Diego, CA) for 12 h. Molecular masses were determined by running standard protein markers (Bio-Rad), covering the range 10-250 kDa. pI values were used as given by the supplier of the immobilized pH gradient strips. Excess of dye was washed out from the gels with distilled water and the gels were scanned with the ImageScanner (GE Healthcare). Electronic images of the gels were recorded using Photoshop (Adobe) and PowerPoint (Microsoft) software. 3820
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Protein spots were outlined (first automatically and then manually) and quantified using the ImageMaster 2D Elite software (GE Healthcare). The percentage of the volume of the spots representing a certain protein was determined in comparison with the total proteins present in the 2-D gel. 2.5. Matrix-Assisted Laser Desorption Ionization Time-ofFlight Mass Spectrometry (MALDI-TOF-MS). MALDI-TOF-MS analysis was performed as described8,13-15 with minor modifications. Briefly, spots were excised, destained with 30% (v/v)
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Figure 2. Two-dimensional map of the OSCC fraction enriched in cytosolic proteins. The proteins were analyzed as stated under legend to Figure 1. The names of the proteins are listed in Table S1 (Supporting Information).
acetonitrile in 0.1 M ammonium bicarbonate and dried in a Speedvac evaporator. The dried gel pieces were rehydrated with 5 µL of 5 mM ammonium bicarbonate (pH 8.8), containing 50 ng trypsin (Promega, Madison, WI), centrifuged for 1 min and left at room temperature for about 12 h. After digestion, 5 µL of water was added, followed 10 min later by 10 µL of 75% acetonitrile, containing 0.3% trifluoroacetic acid, centrifuged for 1 min, and the content was vortexed for 2 min; 1.5 µL of the separated liquid was mixed with 1 µL of saturated alphacyano-4-hydrocinnamic acid in 50% acetonitrile, 0.1% TFA in
water and applied to the sample target. The samples were analyzed in a time-of-flight mass spectrometer (Ultraflex, Bruker Daltonics) equipped with a reflector and delayed extraction. An accelerating voltage of 20 kV was used. Calibration was internal to the samples. Des-Arg-1 Bradykinin (Sigma) and ACTH (18-38) (Sigma) were used as standard peptides. The peptide masses were matched with the theoretical peptide masses of all proteins from all species of the Swiss-Prot database. For protein search, monoisotopic masses were used and a mass tolerance of 0.0025% was allowed. The protein Journal of Proteome Research • Vol. 7, No. 9, 2008 3821
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Figure 3. Two-dimensional map of the OSCC fraction enriched in mitochondrial proteins. The proteins were analyzed as stated under legend to Figure 1. The names of the proteins are listed in Table S1 (Supporting Information).
search was performed with in-house developed software,16,17 which is similar to the Peptident software on the ExPASy server (http://expasy.hcuge.ch/sprot/peptident.html). The algorithm used to determine the probability of a false positive match with a given MS-spectrum was published16 and it can be described as follows. Baseline correction: The baseline of the MALDI-MS spectrum was found by splitting the spectrum into sequential mass segments of a size of 0.05 mass range. For each of these segments we calculated a robust linear fit17 and derived the slope and offset and their respective errors. The baseline was then approximated by cubic spline interpolation in-between 3822
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the midpoints of the segments, and the baseline was subtracted from the spectrum. Peak detection and isotope distribution fit: After baseline correction, the maximum y-coordinate was taken as the starting point for peak fitting. A standard implementation of the Levenberg-Marquardt algorithm17 was used to fit the isotope distribution of an average peptide at a given mass (calculated using the algorithm of Rockwood et al.16,17), which is parametrizized by the monoisotopic mass position, the instrument resolution, and the peak height. The fit was characterized by the usual fit quality estimates (chi-square statistics).
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MYC- and p53-Related Pathways in OSCC
Figure 4. Partial two-dimensional images showing proteins with different levels in OSCC (A, C, E, G) and surrounding mucosa a as control (B, D, F, H) tissue. Proteins were separated on 3-10 nonlinear IPG strips, followed by 12% SDS-gels. The gels were stained with colloidal Coomassie blue and the proteins were identified by MALDI-TOF-MS, as described in Materials and Methods.
Subtraction of fit: From the fitted isotope distribution parameters we calculate the fit isotope distribution multiplied by a safety margin of 1.2 and subtracted the fit from the spectrum. The procedure was restarted and looped until the desired number of monoisotopic masses has been founded. The algorithm was implemented on a standard personal computer. 2.6. Network and Gene Product Ontology Analysis. Differentially expressed proteins, identified in the present study, were used for pathway analysis. For this purpose, the Swiss-Prot accession numbers were inserted into the Ingenuity Pathway Analysis (IPA) software (Ingenuity Systems, Mountain View, CA). The software categorizes gene products based on the location of the protein within cellular components and suggests possible biochemical, biological, and molecular functions. The identified proteins also were mapped to genetic networks available in the Ingenuity database and then ranked by score. The genetic networks described functional relationships between gene products based on known interactions in the literature. The IPA tool then associated these networks with known biological pathways. 2.7. Quantitative Reverse Transcriptase PCR. For gene expression analysis, tissue samples were homogenized by using a bead-based homogenizer (Bertin Technologies, Precellys24, France), which allows the extraction of several samples simultaneously. Each tissue occupied a separate sterile plastic tube to avoid sample carryover and cross contamination. Homogenization was completed within 30 s for 5600 rpm in precooled tubes.
Total RNA was extracted using the Trizol method (Invitrogen) with some modification as described in the classic Trizol/ 1-Brome-3-chloropropan/water extraction protocol18 and additional DNase treatment (Qiagen). Concentration and purity were determined with a spectrophotometer (Biospectrometer, Eppendorf) by calculating the ratio of optical density at 260 and 280 nm. cDNA synthesis was carried out using iScript cDNA Synthesis Kit from BioRad, according to the manufacturer’s instructions. Three-hundred nanograms of RNA were used and synthesized in a total volume of 30 µL. For the PCR reaction, 1 µL of cDNA from each synthesis was added to a RealMasterMix Sybr ROX solution containing 20× SYBR Green (5PRIME) in a total volume of 20 µL. A final concentration of 100nM of the specific flanking primers was added. For qPCR amplification, a Mastercycler epgradientS from Eppendorf was used. The mixtures were analyzed in technical duplicates in a 96-well plate (Eppendorf). The initial denaturation started at 94 °C for 2min, followed by 45 cycles of amplification with denaturation at 94 °C for 15 s, annealing and amplification at 60 °C for 1 min, and finalized by a melting curve analysis. Reverse transcription quantitative polymerase chain reaction (RT-qPCR) was performed for C-MYC (Genbank ID NM_002467), N-MYC (Genbank ID NM_005378), GAPDH (Genbank ID NM_002046), and TP53 (Genbank ID NM_000546). Signals of these genes were derived from the same cDNA and were compared to GAPDH with the ∆∆CT method.19 Comparison to β-Actin (Genbank ID NM_001101) gave the same relative amounts (data not shown). Primer sequences were retrieved from Ensembl and designed with primer ExpressTM software from ABI. To avoid genomic DNA amplification primers were located in exons and exon spanning regions. Sequence of primers is listed in Table 2. 2.8. Immunohistochemistry. Dewaxed and microwaved slides were incubated with mouse monoclonal antistratifin (Research Diagnostics Inc., USA) antibodies overnight at room temperature (RT). As control, slides were exposed to IgG1 (Ancell, USA) antibody. After washing with TBS three times, slides were incubated with a multilink antibody (Dako, Denmark) for 1 h at RT, washed, and again exposed to alkaline phosphatase conjugated Streptavidin-AP/10% human-serum (Dako) for 1 h at RT. Visualization was performed with Fast Red TR, 4-chloro-2-methylbenzenediazonium-salt (SigmaAldrich), counterstained with hemalaun, dehydrated, and mounted. 2.9. Statistical Analysis. All tumor samples were processed twice by 2-D gels or qRT-PCR. The results of the individual analyses correlated well with each other without unexpected deviations. Statistical evaluation of differential protein expression in tumor samples and the control samples from the same patient was performed using the SPSS software (version 8.0). Statistical significance was calculated using the two-side paired Student’s t test. The comparison of OSCC and BCC samples was done by unpaired Student’s t test. Significance was set a p < 0.05. Statistical deviations are indicated in Table 1 and the supplementary Table S3 (Supporting Information) for the proteomics data and in Figures 6 and 7 for the qRT-PCR data.
3. Results 3.1. Two-dimensional Electrophoresis Analysis. Total protein extracts were separated on 2-D gels and in each gel, approximately 1500 spots were detected. Two-dimensional protein databases for OSCC blood, mitochondria and cytosol Journal of Proteome Research • Vol. 7, No. 9, 2008 3823
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Figure 5. Expression Profiling Diagram: The diagram was constructed with the use of the Ingenuity Pathway Analysis software as described in Materials and Methods and in Results. ANXA2, annexin A2; BRCA1, breast cancer 1; CKM, creatin kinase M-chain; CRP, c-reactive protein; FN1, fibronectin 1;GSTP1, glutathione S-transferase pi; HSPB1, heat shock protein 27; NME1, nucleoside diphosphate kinase A; RB1, retinoblastoma; RBL2, retinoblastoma like protein 2; SFN, 14-3-3sigma, stratifin. The numbers in brackets indicate references.
were constructed. In total, 350 different gene products were identified in the OSCC and control samples (Table S1, Supporting Information). Figures 1, 2, and 3 show representative 2-D gels of the OSCC blood, mitochondrial, and cytosolic fractions with the identified proteins indicated. 3.2. Link of the Upregulated Proteins to Biological Pathways. We studied the differences in the protein expression levels in cancer and healthy tissues (tumor center and surrounding mucosa, respectively). The protein levels were quantified using the ImageMaster II software. A minimal 1.5-fold change in expression between tumor and control tissue was considered as this value allows a reproducible detection of differences by the applied technologies. Examples of changed protein levels are shown in Figure 4. Twenty proteins with deranged levels were detected of which 16 were upregulated. The proteins with increased levels in OSCC and the standard deviations for each value are listed in Table 1. The statistical significance was calculated using the two-sided paired Student’s t test. The pathways of the proteins with increased levels in OSCC were studied using the IPA software. Pathway analysis combines extensive structure knowledge, based on literature data, with experimental data sets (genomics, proteomics) and generates protein-protein interaction networks, relevant to the underlying experimental paradigm. The proteomic and pathway analyses indicated that eight of the significantly upregulated proteins, creatine kinase m chain (P ) 0.005), glutathione S-transferase pi (P ) 0.046), stratifin (P ) 0.001), nucleosidediphosphate kinase A (P ) 0.005), heat shock protein 27 kDa (P ) 0.002), C-reactive protein (P ) 0.0041), annexin II (P ) 0.0026), and retinoblastoma-like protein 2 (P ) 0.013), are directly linked to three locus gene products, p53, MYC, and MYCN (Figure 5). Annexin II and C-reactive protein were overexpressed in 9 of the 10 patients, stratifin and retinoblastoma-like protein 2 were overexpressed in 8 and 7 out of 10 3824
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patients, respectively, whereas all others were upregulated in all patients examined. The expression changes of the 8 proteins in tumor over control tissue of the individual patients are shown in the supplementary Table S2 (Supporting Information). The links of each protein to the three pathways are outlined in Discussion. 3.3. Validation by Quantitative RT-PCR and Immunhistochemistry. Applying pathway analysis revealed that many of the proteins upregulated in tumor samples map to a pathway linking TP53, c-Myc, and N-Myc. The latter proteins were not identified by the proteomics approach. We therefore analyzed their mRNA expression level (Figure 6). The mRNA for TP53 was reproducibly induced in all OSCC tumor samples analyzed when compared to the corresponding surrounding mucosa. For c-Myc, a similar tumor specific induction could be observed, except for patient 10. In this case, p53 induction had also been weaker and no induction of N-Myc could be observed. On the whole N-Myc induction showed the greatest variability between the three markers analyzed. In two patients, a dramatic increase could be observed (10- to 15-fold in patients 3 and 4) whereas others showed none (patient 10) or only marginal (patients 6 and 9) induction. To ascertain specificity of the observed induction of the TP53 and Myc pathway, we also analyzed mRNA expression in tumor samples form basal cell carcinoma (BCC, Figure 7). Although the expression of p53, c-Myc, and N-Myc mRNA was higher in OSCC tumor samples than in control tissue, these mRNAs were expressed in BCC tumors at levels comparable to the control tissues, indicating that the activation of the p53 and Myc pathways is specific for OSCC. In our proteomics approach, we identified stratifin (14-3-3 sigma) as one of the proteins upregulated in OSCC tissues in comparison to healthy mucosa. To verify the proteomics data, we stained OSCC tumor and corresponding control mucosa for
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Figure 7. Specificity of p53, c-Myc, and N-Myc mRNA upregulation. mRNA expression of p53, c-Myc, and N-Myc was analyzed in five tumor samples form basal cell carcinoma (BCC), 10 oral squamous cell carcinoma (OSCC) samples, and their 10 control tissues and normalized to GAPDH mRNA expression.
Figure 6. Verification of pathway activation by quantitative RTPCR. Expression of (A) p53, (B) c-Myc, and (C) N-Myc mRNA was determined by quantitative RT-PCR in tumor samples (TC) and control tissues (SM) from 10 patients diagnosed with OSCC and normalized to GAPDH mRNA. Fold overexpression was calculated for each patient and is indicated in each panel. Mean expression values and fold induction were also calculated.
stratifin and could demonstrate a significant overexpression of stratifin in the tumor samples. Figure 8 shows a typical matched pair. Isotpye control did not stain either tissue (data not shown).
4. Discussion We performed a proteomic analysis of the blood and of fractions enriched in cytosolic and mitochondrial proteins of OSCC and identified 350 different gene products. We further studied the pathway profiling in OSCC, considering that such pathway analyses generate new insights into systems biology. There exist a limited number of reports and studies on proteomic analysis of OSCC cell lines.9 Protein identity and quantification require the detection and quantification of the
Figure 8. Stratifin expression in tumor tissue. In panel (A), low stratifin levels are observed in the epithelium of healthy oral mucosa, whereas in clear contrast, in panel (B), stratifin is significantly overexpressed in the cell cytoplasm of tumor cells.
proteins which constitute the system as well as analysis of the differentially processed forms. Inherent difficulties in protein analysis complicate these tasks as proteins cannot be amplified. It is possible to produce large amounts of a particular protein by overexpression in specific cell systems, for example in cells of epithelial origin; however, OSCC tumors are not exclusively Journal of Proteome Research • Vol. 7, No. 9, 2008 3825
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Table 2. Primer Sequences and PCR Conditions name
primer
sequence
myc_Exon2_3_human myc_Exon2_3_human GAPDH_Exon5_6_human GAPDH_Exon5_6_human Nmyc_Exon2_3human Nmyc_Exon2_3human TP53_Exon4_5_human TP53_Exon4_5_human
forward reverse forward reverse forward reverse forward reverse
CAGCGACTCTGAGGAGGAACA GCCTCCAGCAGAAGGTGATC GCGAGATCCCTCCAAAATCA GCAAATGAGCCCCAGCCT CACCCTGAGCGATTCAGATGA CCTTGGTGTTGGAGGAGGAA CCAAGTCTGTGACTTGCACGTACT GCTGCACAGGGCAGGTCTT
composed of these cells. They contain normal cells, endothelial cells, and fibroblasts as well. Each species has its own characteristic proteomic profile and therefore has to be studied both individually and within the system. Study of OSCC cells in culture will primarily allow for the selective identification of polypeptides regulated in cancer cells only, which is partially representative of the whole OSCC protein expression profile. The question is to what level tumors should produce differentially expressed proteins for the proteins to be useful as pathway candidates as most of the differentially expressed proteins identified in tumor tissues have basal expression levels in surrounding mucosa as well. Therefore, in the present proteomics study, we chose OSCC tumor tissues and studied the protein expression profile compared to the surrounding mucosa as control in each patient. Sixteen proteins showed increased levels in OSCC (Table 1). In a previous study,20 we identified 14 upregulated proteins. Network analysis discussed below revealed an interrelation of eight of the upregulated gene products. A protein with increased levels in OSCC is creatine kinase M (CKM) and was identified as upregulated in all ten tumors examined (Figure 4, Table 1 and Supplementary Tables S2, S3, and Supplementary Figure S1, Supporting Information). CKM is a cytoplasmic enzyme and an important serum marker for myocardial infarction. It plays important roles in morphology, aggregation and permeabilization of cells. The protein is a member of the ATP guanido phosphotransferase protein family and reversibly catalyzes the transfer of phosphate between ATP and various phosphogens such as creatine phosphate. Transcription of the muscle creatine kinase gene is controlled by the tumor protein 53 (TP53), which is activated during muscle differentiation and together with MyoD participates in the transcription of the muscle creatine kinase gene21 (Figure 5). TP53 is a DNA-binding protein, comprising DNA-binding, oligomerization and transcription activation domains and plays an important role in the regulation of the cell cycle, specifically in the transition from G0 to G1. It is found at very low levels in normal cells, however, in a variety of transformed cell lines, it is expressed at high amounts, and it is believed to contribute to transformation and malignancy. It is postulated to bind as a tetramer and to activate expression of genes that inhibit growth and/or invasion, and to function as a tumor suppressor. The p53 gene is known to be the most commonly mutated gene in human cancers and mutations in p53 can be found in approximately half of the oral squamous cell carcinoma.22-25 Mutants of p53 that frequently occur in a number of different human cancers fail to bind the consensus DNA binding site, and hence cause the loss of tumor suppressor activity. Mutations in p53 frequently but not always correlate with TP53 overexression.26 Another upregulated protein in all ten OSCC samples is glutathione-S transferase pi (GSTP1) (Figure 4, Table 1 and 3826
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annealing temp
amplicon length
59 °C
112bp
60 °C
101bp
60 °C
91bp
60 °C
78bp
supplementary Tables S2, S3, and supplementary Figure S1) also controlled by p53 (Figure 5). Glutathione S-transferases are a family of enzymes that play important roles in detoxification by catalyzing the conjugation of many hydrophobic and electrophilic compounds with reduced glutathione. On the basis of their biochemical, immunological, and structural properties, the soluble GSTs are categorized into four main classes, alpha, mu, pi, and theta. The GSTP1 gene is a polymorphic gene encoding active, functionally different GSTP1 variant proteins that are thought to function in xenobiotic metabolism and play a role in susceptibility to cancer, and other diseases.27 One of the functions of the p53 gene is to regulate a protein known as stratifin (SFN) that causes cells to arrest at the G2 phase of the cell cycle.28 Stratifin (14-3-3) protein, a member of the 14-3-3 family, was found to be upregulated in eight OSCC samples (Table 1 and Supplementary Tables S2, S3, and Supplementary Figure S1, Supporting Information). The 143-3 proteins have diverse functions, including critical roles in signal transduction pathways and cell cycle regulation.29-31 Stratifin has also been identified as a p53-inducible gene product involved in the cell cycle checkpoint control after DNA damage. The detailed mechanism of the cell cycle regulation is not well understood. We found stratifin overexpression which agrees with published data demonstrating that stratifin is a biomarker for OSCC.32-35 Here, we propose a link between stratifin activities and TP53 regulation. Expression of stratifin strongly correlates with overexpression of GSTP1 and CKM which interact with TP53 (Figure 5). Yang et al. described that stratifin expression leads to stabilized expression of TP53.32 In studying the molecular mechanism of increased stabilization of TP53, they found that stratifin antagonized the biological functions of TP53 and enhanced p53 transcriptional activity. As a target gene of p53, stratifin appears to have a positive feedback effect on p53 activity. Overexpression of stratifin inhibited oncogene-induced tumorigenicity in a tetracyclineregulated system. The results suggest that stratifin defines an important TP53 regulatory loop and its expression can be considered for therapeutic intervention and diagnosis in cancers. Nucleoside-diphosphate kinase A (NME1), for which increased levels were found in all OSCC samples (Figure 4, Table 1, and Supplementary Tables S2, S3 and Supplementary Figure S1, Supporting Information), exerts important functions in cell movement, invasiveness, quantity disease and tumorogenesis. NME1 gene was identified because of its reduced mRNA transcript levels in highly metastatic cells. Nucleoside disphophate kinase (NDK) exists as a hexamer composed of the “A” (NME1) and “B” (encoded by NME2) isoforms. Mutations in NME1 have been identified in aggressive neuroblastomas.36 Two transcript variants encoding different isoforms have been found for this gene. Furthermore, the protein is involved in
MYC- and p53-Related Pathways in OSCC GTP, UTP, CTP biosynthesis, and the negative regulation of cell proliferation and cell cycle. NME1 is regulated by MYC and MYCN.36-38 Human small heat shock proteins (HSHP) are a group of stress related gene products, including HSP60, HSP27, HSP20, and alpha B-Crystallin. HSP20 was first described and characterized as a byproduct of the purification of human alpha B-Crystallin and HSP27.39 HSP27 is a molecular chaperone involved in cellular defense mechanisms and its expression is considerably altered in tumor tissues and under the effect of toxic agents.40 Investigation of the biochemical properties of HSP27 lag behind. Rat HSP27 has only been briefly characterized in the reports of Kato et al.39 and van de Klundert et al.,41 whereas the corresponding properties of human HSP27 remain practically uncharacterized. It seems that HSP27 and HSP20 are independently modulated in response to stress.42 Our observation that expression of HSP27 increased in OSCC, whereas that of HSP20 decreased (Figure 4) are consistent with the observed results from other studies.20,43 These findings suggest that HSP27 may play a distinct role in the progress of tumorigenesis although it shares structural and functional similarities with HSP20. HSP27 was induced in all 10 tumor samples examined (Table 1 and Supplementary Tables S2, S3 and Supplementary Figure S1, Supporting Information). C-reactive protein (CRP) also shows higher levels in nine of ten OSCC samples (Table 1 and Supplementary Tables S2, S3 and Supplementary Figure S1, Supporting Information). The protein was initially identified as a substance reacting with the C polysaccharide of Streptococcus pneumoniae in the serum of a patient with pneumonia and it is well-known to be synthesized in hepatocytes in response to inflammatory changes. Altered immune, inflammatory and angiogenesis responses are observed in patients with head and neck squamous cell carcinoma (HNSCC) and many of these responses have been linked to aggressive malignant behavior and poor prognosis.44 It seems that HNSCC cells produce cytokines that regulate immune, inflammatory, and angiogenesis responses and CRP acts as an acute phase protein, which increases in the serum in response to inflammatory diseases.44 Its expression is induced by the pro-inflammatory cytokine, interleukin-6, which acts as a growth factor in malignant tumors.45 Increase of serum CRP is useful as an indicator of the malignant potential of the tumor. CRP is controlled by MYC via binding to fibronectin 1 (FN1).46 FN1 is a glycoprotein present in a soluble dimeric form in plasma, and in a dimeric or multimeric form at the cell surface and in extracellular matrix. Fibronectin is involved in cell adhesion and migration processes including embryogenesis, wound healing, blood coagulation, host defense, and metastasis. The gene has three regions subjected to alternative splicing, with the potential to produce 20 different transcript variants. However, the full-length nature of some variants has not been determined. Annexins are structural, calcium- and phospholipid-binding proteins that have been implicated in a broad range of molecular and cellular processes, including tissue growth and differentiation, modulation of phospholipase A2 and kinase activities in signal transduction, inflammation, and blood coagulation. Human annexins and their cognate orthologues are represented by 12 different members, classified A1 through A11 and A13. Annexins are normally well expressed in a wide range of different organs and tissues and several studies found dysregulation in certain cancer forms, including HNSCC.47-50
research articles We found a significant downregulation of ANXA1, whereas the ANXA2 levels were significantly increased in the tumor tissue of nine patients compared to the levels in the surrounding mucosa (Table 1 and Supplementary Tables S2, S3 and Supplementary Figure S1, Supporting Information). The reason for the reduced ANXA1 expression in OSCC is not known. Possible mechanisms include genomic deletion, mutations of the ANXA1 gene, hypermethylation of the promoter with subsequent loss of transcription, alteration of one or more proteins that regulate ANXA1 transcription such as IL-6,42 or alterations in post-translational processing of the protein by proteolysis or phosphorylation. Alterations in the ANXA1 expression were found in breast cancer, hepatocellular carcinoma, esophageal carcinoma, including esophageal squamous cell carcinomas, prostate, and gastric carcinomas. ANXA2 was found to be overexpressed in brain glial tumors and pancreatic carcinoma but markedly down-regulated in prostate cancer.28,51-54 ANXA2 is controlled by MYC via binding to CD44 antigen and FN1.55 CD44 is a cell-surface glycoprotein involved in cell-cell interactions, cell adhesion, and migration. It participates in a wide variety of cellular functions including lymphocyte activation, recirculation and homing, hematopoiesis, and tumor metastasis. Transcripts for this gene undergo complex alternative splicing that results in many functionally distinct isoforms; however, the full length nature of some of these variants has not been determined. Alternative splicing is the basis for the structural and functional diversity of this protein, and may be related to tumor metastasis. Retinoblastoma like protein 2 (RBL2) which shows increased levels in 7 of the 10 OSCC samples (Figure 4, Table 1, and Supplementary Tables S2, S3 and Supplementary Figure S1, Supporting Information) is controlled by MYC and by binding to retinoblastoma protein 1 (RB1). RB1 regulates the exit of cells from the G1 phase of the cell cycle into the S-phase. For that, RB1 has to be phosphorylated by so-called cyclin depended kinases (CDKs).56,57 Once RB1 is phosphorylated, it becomes inactive enabling cells to enter the S phase. A complex consisting of three different proteins, cyclin D, CDK4 and CDK6, phosphorylates RB1. Inhibitors of these CDKs, so-called cyclin dependent kinase inhibitors (CKI), stop their association with D-type cyclins and accordingly inhibit phosphorylation of RB1. Although we were able to identify several proteins that can be mapped to the TP53 or Myc pathways, not all of these were significantly upregulated. Of the 350 proteins identified, ∼85% do not have any known association to these pathways whereas 50% of the upregulated proteins are associated with either the TP53 or the Myc pathway. TP53 and Myc themselves were not identified in the proteomics screen, which can be explained by their low abundance. However, we were able to demonstrate by quantitative RT-PCR for TP53, c-Myc, and N-Myc that the mRNA for these proteins is indeed upregulated in OSCC tumor samples, a finding that is specific for OSCC, as BCC tumors showed no significant induction of either mRNA species. BCC and OSCC are both nonmelanoma skin carcinomas and they can be expected to have comparable basic protein expression. For both carcinomas, involvement of the TP53 and the hedgehog pathway have been described.58-63 Although a deregulated activity of the sonic hedgehog signaling pathway is considered as the main molecular pathologic mechanism in BCC development, OSCC still seems to be mainly a TP53 driven carcinoma.64 Journal of Proteome Research • Vol. 7, No. 9, 2008 3827
research articles 5. Conclusion We applied proteomics technologies to analyze the OSCC proteome of 10 patients, and we identified 350 different gene products. We further detected 20 proteins with deranged levels in OSCC in comparison with control tissue, of which 16 were upregulated. We studied what is known about their functions and their possible interactions to other proteins and in particular to the proteins involved in the pathways of three well characterized proto-oncogenes, p53, MYC, and MYCN. Eight of the upregulated proteins are associated with gene products involved in the three pathways, which are transcription factors, nuclear receptors, enzymes, or have other functions.36,65-67 Their interactions to the other gene products affect activation or deactivation, binding expression and transcription and it is the first time that these networks are correlated with OSCC. The constructed network is shown in Figure 5. This is an example of a systems biology study, in which functional proteomics helped to elucidate mechanistic aspects and potential involvement of proteins of interest in biological pathways that are biomarker candidates.
Supporting Information Available: Table S1 lists all identified proteins from this study. Table S2 lists fold overexpression of the candidate genes of the OSCC network in the individual patients, and their raw expression is listed in Table S3 and depicted in Figure S1 (Expression of p53, c-Myc, and N-Myc pathway associated proteins in individual patients. The mean expression of the proteins in each tumor sample (TC) or control tissue (SM) was calculated from both individual experiments and is shown for each patient.). This material is available free of charge via the Internet at http://pubs.acs.org. References (1) Fearlay, J.; Bray, F.; Pisani, P.; Parkin, D. M. Cancer inidence, mortality and prevalence worldwide, version 1.0; IARC Cancer Base No. 5; IARC Press: Lyon, 2001. (2) Wulfkuhle, J. D.; McLean, K. C.; Paweletz, C. P.; Sgroi, D. C.; Trock, B. J.; Steeg, P. S.; Petricoin, E. F., 3rd. New approaches to proteomic analysis of breast cancer. Proteomics 2001, 1 (10), 1205–15. (3) Xiao, X.; Liu, D.; Tang, Y.; Guo, F.; Xia, L.; Liu, J.; He, D. Development of proteomic patterns for detecting lung cancer. Dis. Markers 2003, 19 (1), 33–9. (4) Peyrl, A.; Krapfenbauer, K.; Slavc, I.; Yang, J. W.; Strobel, T.; Lubec, G. Protein profiles of medulloblastoma cell lines DAOY and D283: identification of tumor-related proteins and principles. Proteomics 2003, 3 (9), 1781–800. (5) Trog, D.; Fountoulakis, M.; Friedlein, A.; Golubnitschaja, O. Is current therapy of malignant gliomas beneficial for patients? Proteomics evidence of shifts in glioma cells expression patterns under clinically relevant treatment conditions. Proteomics 2006, 6 (9), 2924–30. (6) Bichsel, V. E.; Liotta, L. A.; Petricoin, E. F. 3rd, Cancer proteomics: from biomarker discovery to signal pathway profiling. Cancer J. 2001, 7 (1), 69–78. (7) Fountoulakis, M. Proteomics: current technologies and applications in neurological disorders and toxicology. Amino Acids 2001, 21 (4), 363–81. (8) Fountoulakis, M. Application of proteomics technologies in the investigation of the brain. Mass Spectrom. Rev. 2004, 23 (4), 231– 58. (9) Koike, H.; Uzawa, K.; Nakashima, D.; Shimada, K.; Kato, Y.; Higo, M.; Kouzu, Y.; Endo, Y.; Kasamatsu, A.; Tanzawa, H. Identification of differentially expressed proteins in oral squamous cell carcinoma using a global proteomic approach. Int. J. Oncol. 2005, 27 (1), 59–67. (10) Brennan, J. A.; Sidransky, D. Molecular staging of head and neck squamous carcinoma. Cancer Metastasis Rev. 1996, 15 (1), 3–10. (11) Bradford, M. M. A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding. Anal. Biochem. 1976, 72, 248–54.
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