Differential Protein Expressions in Renal Cell Carcinoma: New Biomarker Discovery by Mass Spectrometry K. W. Michael Siu,† Leroi V. DeSouza,† Andreas Scorilas,‡ Alexander D. Romaschin,§,| R. John Honey,⊥ Robert Stewart,⊥ Kenneth Pace,⊥ Youssef Youssef,§ Tsz-fung F. Chow,§ and George M. Yousef*,§,| Department of Chemistry and Centre for Research in Mass Spectrometry, York University, Toronto, Canada, Department of Biochemistry and Molecular Biology, Faculty of Biology, University of Athens, Panepistimiopolis, 15701 Athens, Greece, Department of Laboratory Medicine, and the Keenan Research Centre in the Li Ka Shing Knowledge Institute, St. Michael′s Hospital, Toronto, Canada, Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada, and Department of Urology, St. Michael′s Hospital, Toronto, Canada Received May 28, 2008
Renal cell carcinoma (RCC) is the most common neoplasm in the adult kidney. Unfortunately, there are currently no biomarkers for the diagnosis of RCC. In addition to early detection, biomarkers have a potential use for prognosis, for monitoring recurrence after treatment, and as predictive markers for treatment efficiency. In this study, we identified proteins that are dysregulated in RCC, utilizing a quantitative mass spectrometry analysis. We compared the protein expression of kidney cancer tissues to their normal counterparts from the same patient using LC-MS/MS. iTRAQ labeling permitted simultaneous quantitative analysis of four samples (cancer, normal, and two controls) by separately tagging the peptides in these samples with four cleavable mass-tags (114, 115, 116, and 117 Da). The samples were then pooled, and the tagged peptides resolved first by strong cation exchange chromatography and then by nanobore reverse phase chromatography coupled online to nanoelectrospray MS/MS. We identified a total of 937 proteins in two runs. There was a statistically significant positive correlation of the proteins identified in both runs (rp ) 0.695, p < 0.001). Using a cutoff value of 0.67 fold for underexpression and 1.5 fold for overexpression, we identified 168 underexpressed proteins and 156 proteins that were overexpressed in RCC compared to normal tissues. These dysregulated proteins in RCC were statistically significantly different from those of transitional cell carcinoma and end-stage glomerulonephritis. We performed an in silico validation of our results using different tools and databases including Serial Analysis of Gene Expression (SAGE), UniGene EST ProfileViewer, Cancer Genome Anatomy Project, and Gene Ontology consortium analysis. Keywords: renal cell carcinoma • kidney cancer • tumor markers • mass spectrometry • iTRAQ • proteomics • LC-MS/MS
Introduction Renal cell carcinoma (RCC) is the most common neoplasm in the adult kidney, making up 3% of all adult malignancies.1 The incidence of RCC has increased steadily over the past 20 years. Histopathologically, about 80% of RCC is clear-cell, 15% papillary, with the remaining 5% being other types. Early diagnosis of kidney-localized RCC is associated with a quite favorable prognosis with a five-year survival rate of about 89%. Unfortunately, patients often present with few signs, symptoms, * To whom correspondence should be addressed. George M. Yousef, MD PhD FRCPC (Path), Department of Laboratory Medicine, St. Michael′s Hospital, 30 Bond Street, Toronto, ON, M5B 1W8, Canada. Tel: 416-8646060Ext: 6129. Fax: 416-864-5648. E-Mail:
[email protected]. † York University. ‡ University of Athens. § Li Ka Shing Knowledge Institute, St. Michael′s Hospital. | University of Toronto. ⊥ Department of Urology, St. Michael′s Hospital. 10.1021/pr800389e CCC: $40.75
2009 American Chemical Society
or laboratory abnormalities, and are frequently (∼30%) diagnosed at the metastatic stage, when the prospects for cure are dismal (9% five-year survival rate).2 The clinical diagnosis of RCC is often confirmed by imaging studies, including X-ray and computed-tomography. The possible existence of non-neoplastic mass lesions can be a serious challenge to the diagnosis of RCC. There are currently no biomarkers available for the diagnosis of RCC. Molecular expression studies of cancer provide great opportunities to identify new cancer biomarkers to improve cancer management.3 In addition to early detection, biomarkers have potential use for prognosis, monitoring recurrence after treatment, and as predictive markers for optimal treatment modality. This last point is important as a means to avoid unnecessary treatments that may be ineffective or pose serious side effects.4 Finally, molecular-expression studies, especially those involving quantitative, differential-expression analyses, Journal of Proteome Research 2009, 8, 3797–3807 3797 Published on Web 07/17/2009
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Figure 1. Representative spectra showing the reporter ion region (top panel) and the full MS/MS spectrum (bottom panel) for a tryptic peptide of a protein that is differentially expressed in only the kidney cancer sample versus the normal control (Protein # 212: Signature ions for DNA binding protein B). The highlighted m/z values show b and y product ions that have been matched to the identified peptide sequence. Table 1. Distribution of Proteins and Their Expression Levels in RCC Compared to Their Normal Counterparts expression levels
First run Second run Proteins identified in both runs Proteins identified in either of the runs a
no of proteins
1.50
574 626 262
107 110 65a
351 424 163a
116 89 34a
937
168a
613a
156a
Average of the expression levels in both runs.
are useful in providing insight into the changes in functional pathways and thus the mechanisms of cancer development at the molecular level.5 This approach may constitute the basis for identifying new molecular drug targets for therapy. Proteomics has distinct advantages over genomic expression studies because it is the proteins that are ultimately responsible for the malignant phenotype. Mass spectrometry (MS)-based protein expression examinations have been successfully used to identify and evaluate new biomarkers for cancer, including prostate,6 endometrial,7-9 breast,10 pancreatic,11 and head-and-neck12,13 cancers, among many others.14 Proteomics combined with MS offers great promise for unveiling the complex molecular events of tumorigenesis and identifying cancer biomarkers. More recently, advanced MS techniques have been developed that enable proteins from different samples to be compared via labeled tags differing in isotopic composition. Samples are combined and processed in a single batch, allowing comparative quantification to be performed15 Effective labeling strategies include isotope-coded affinity tag 3798
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Figure 2. Correlation between normalized protein expression levels in the RCC specimen between the two runs. Protein expression levels were normalized by the expression levels in normal kidney tissue. A statistically significant positive correlation was observed. Values were compared using the Pearson correlation coefficient.
(ICAT),16 or the more recent variation that uses isobaric tagging reagent, iTRAQ,17 followed by multidimensional LC-MS/MS analysis. We have recently applied these approaches successfully to identify new tumor markers for endometrial cancer.18-20 In this study, we identified proteins that are dysregulated in RCC, utilizing iTRAQ-labeling LC-MS/MS analysis. We also provide bioinformatics validation of the results.
Proteomics in Kidney Cancer
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Figure 3. Subcellular localization of proteins identified in RCC, as analyzed through the Gene Ontology consortium databases. This included analysis of (A) the total numbers of proteins extracted and (B) upregulated proteins in RCC compared to their normal counterparts.
Materials and Methods Tissue Collection and Storage. Tumor tissue from patients diagnosed with RCC and their adjacent normal counterparts were obtained from nephrectomy specimens at St. Michael′s hospital after obtaining an informed consent, or from the Ontario Tumor Bank. As RCC is known to arise from the proximal tubules,21 the kidney cortex was used as a normal control.22,23 All specimens were histologically confirmed. Specimens were treated with protease inhibitors as described24 and stored in liquid nitrogen. The study was approved by the Research Ethics Board of St. Michael′s Hospital and the Ontario Cancer Institute. iTRAQ Sample Preparation Procedure. We utilized a tissue preparation protocol that was previously used for biomarker studies in endometrial cancer.25,26 In summary, cell debris from the tissue homogenates was removed by centrifugation in a microfuge at 4 °C for 30 min at 14 000 rpm. The clarified supernatant was transferred to fresh microfuge tubes and the total protein content determined using a commercial Bradford assay reagent (Bio-Rad, Mississauga, ON, Canada). A standard
curve for the Bradford assay was made using γ-globulin as a control. One-hundred micrograms of each sample was then denatured and the cysteines blocked as described in the iTRAQ protocol (Applied Biosystems, Foster City, CA). Each sample was then digested with trypsin as recommended in the iTRAQ protocol and labeled with the iTRAQ tags as follows: noncancer, diseased kidney, iTRAQ114; normal kidney, iTRAQ115; and the two kidney cancer samples, iTRAQ116 and iTRAQ117. The labeled samples were then pooled and mixed with Eluent A (10 mM KH2PO4 solution in 25% acetonitrile and 75% deionized water acidified to a pH of 3.0 with phosphoric acid) to a total volume of 1.0 mL for strong cation exchange (SCX) chromatography. This diluted sample was further acidified using 5 µL of concentrated phosphoric acid, after which the contents were manually injected onto a 200 µL bed volume strong cation exchange (SCX) cartridge (Applied Biosystems Inc., Foster City, CA). Separation was effected by first a wash with 1.0 mL of Eluent A and then ten step-elutions using 0.5 mL each of Eluent A with increasing concentrations of KCl. The 10 salt concentrations used were 10 mM, 50 mM, 100 mM, 150 mM, 200 mM, Journal of Proteome Research • Vol. 8, No. 8, 2009 3799
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Table 2. Partial List of Differentially Expressed Proteins in Kidney Cancer Tissue Compared to Normal Counterparts from the Same Patient no.
protein name
gene symbol
Swiss-Prot ID
fold changea
regulationa
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77
KIAA1865 protein tyrosine 3/tryptophan 5-monooxygenase activation protein CAPNS1 protein Complement component 1 inhibitor UDP-glucose 6-dehydrogenase L-lactate dehydrogenase A chain Nicotinamide N-methyltransferase Hypothetical protein DKFZp686H13163 Poly(RC)-binding protein 2, isoform b ADP-ribosylation factor 3 Calnexin precursor 3′(2′),5′-bisphosphate nucleotidase 1 Hypothetical protein DKFZp547J2313 Catechol O-methyltransferase, membrane-bound form Glyceraldehyde-3-phosphate dehydrogenase, testis-specific Hypothetical protein Hypothetical protein DKFZp686I04222 Echinoderm microtubule-associated protein-like 4 vimentin-human Cytoplasmic dynein intermediate chain 2C Calpain small subunit 1 Glutathione S-transferase Alpha Crystallin β ALDOC protein Rab GDP dissociation inhibitor alpha PRKAR2A protein Chloride intracellular channel protein 1 Pre-B-cell colony enhancing factor 1, isoform b Annexin A5 glyceraldehyde-3-phosphate dehydrogenase Endothelial cell growth factor 1 (platelet-derived) Major vault protein Adipose differentiation-related protein 60 kDa heat shock protein ATP synthase delta chain Coronin 1A GCSH protein TAGLN protein Ksp-cadherin splicing factor, arginine/serine-rich 2 Zinc finger protein 207 40S ribosomal protein S17 Secreted cement gland protein XAG-2 homologue Thymosin beta-4 CKB protein Calmodulin Hypothetical protein FLJ46684 Elongation factor Tu Tumor protein p53 inducible protein 3 Calmodulin Hypothetical protein DKFZp586K2222 creatine kinase-B ATP synthase beta chain Hemoglobin beta Histone H2A AP endonuclease 1 Acyl-CoA dehydrogenase, medium-chain specific Nonhistone chromosomal protein HMG-17 HES1 protein Eukaryotic translation initiation factor 3 subunit 3 Calreticulin Programmed cell death protein 5 Chromosome 10 open reading frame 65 adenylate kinase 3 alpha LOC112817 protein Ubiquinol-cytochrome-c reductase complex core protein I Plastin 3 Membrane associated protein SLP-2 MHC class II antigen Reticulocalbin 1 precursor DNA-binding protein B Cytochrome c NADH-ubiquinone oxidoreductase 13 kDa-A subunit Lupus La protein Pyruvate dehydrogenase E1 component beta subunit FERM, RhoGEF, and pleckstrin domain protein 1, isoform 1 Mitochondrial aldehyde dehydrogenase 2
C14orf4 YWHAH CAPNS1 SERPING1 UGDH LDHA NNMT GSTO1 PCBP2 ARF3 CANX BPNT1 FABP7 COMT GAPDHS ANXA4 SERPINB6 EML4 VIM DYNC1I2 CAPNS1 GSTA2 CRYAB ALDOC GDI1 PRKAR2A CLIC1 PBEF1 ANXA5 GAPDH ECGF1 MVP ADFP HSPD1 ATP5D CORO1A GCSH TAGLN CDH16 SFRS2IP ZNF207 RPS17 AGR2 TMSB4X CKB CALM1 C9orf58 TUFM TP53\ill\3 CALM1 TPM1 CKB ATP5B HBB HIST3H2A APEX1 ACADM HMGN2 C21orf33 EIF3H CALB2 PDCD5 C10orf65 AK3 C10orf65 UQCRC1 PLS3 STOML2 HLA-DRB1 RCN1 YBX1 CYCS NDUFS6 SSB PDHB FARP1 ALDH2
Q9H1B7 Q04917 P04632 Q96FE0 O60701 P00338 P40261 Q7Z3T2 Q6PKG5 P61204 P27824 O95861 Q9H047 P21964 O14556 Q6P452 Q7Z2Y7 Q9HC35 P08670 Q7Z4X1 P04632 P09210 P02511 Q6P0L5 P31150 Q9BUB1 O00299 Q8WW95 P08758 P04406 P19971 Q14764 Q99541 P10809 P30049 P31146 Q6IAT2 Q6FI52 Q6UW93 Q99590 O43670 P08708 O95994 P62328 Q6FG40 Q13942 Q6ZR40 P49411 Q9BWB8 P62158 Q9Y427 P12277 P06576 Q6R7N2 Q7L7L0 P27695 P11310 P05204 P30042 O15372 Q96BK4 O14737 Q86XE5 Q9UIJ7 Q96EV5 P31930 Q86YI6 Q9UJZ1 Q9MYD9 Q15293 P67809 Q6NUR2 O75380 P05455 P11177 Q9Y4F1 Q6IV71
4.8624 4.2912 3.9777 3.66505 3.3497 3.32095 3.2572 3.1252 3.0866 3.0753 2.8414 2.7755 2.7722 2.7512 2.6716 2.6156 2.5934 2.575 2.5615 2.5234 2.5198 2.49785 2.48945 2.4321 2.4198 2.4146 2.4112 2.4046 2.3679 2.3475 1.834 1.6983 1.6629 0.4896 0.4879 0.4878 0.4873 0.4862 0.4818 0.4802 0.4726 0.4702 0.46395 0.4631 0.4589 0.4562 0.4479 0.4476 0.4383 0.4245 0.4187 0.4162 0.41455 0.4127 0.4112 0.4085 0.3938 0.39295 0.3925 0.3894 0.3772 0.3623 0.3616 0.3613 0.3554 0.3531 0.3394 0.3341 0.3294 0.312 0.31 0.2901 0.2759 0.2709 0.2662 0.2628 0.2603
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Proteomics in Kidney Cancer Table 2 Continued no.
protein name
gene symbol
Swiss-Prot ID
fold changea
regulationa
78 79 80 81 82 83 84
2,4-dienoyl-CoA reductase OXCT protein Mitochondrial glycine cleavage system H-protein Prothymosin alpha Hypothetical protein DKFZp564K0164 Hemoglobin beta Hypothetical protein DKFZp761J19
DECR1 OXCT1 GCSH PTMA PDHB HBB C9orf58
Q16698 Q6IAV5 Q6QN92 Q15202 Q9UFK3 Q6J1Z7 Q9BQI0
0.21255 0.1973 0.164 0.1322 0.1253 0.0835 0.0412
DOWN DOWN DOWN DOWN DOWN DOWN DOWN
a
Fold change and regulation is calculated as the ratio of cancer: normal.
Table 3. Protein Expressiona in RCC Sample Compared to Non-Cancerous Renal Failure and Transitional Cell Carcinoma (TCC) no of proteins (%) with expression status mean ( SE
RCC: renal 1.088 ( 0.016 failure RCC: TCC 1.156 ( 0.018
1.50
151 (16.1)
637 (68.0)
149 (15.9)
148 (15.8)
614 (65.5)
175 (18.7)
a
Protein expression levels were normalized by normal kidney tissue expression and expressed as fold changes from normal.
Figure 4. Comparison between normalized (expressed as fold changes of normal kidney tissue expression levels) protein average expression levels in RCC to noncancerous renal failure (A) and transitional cell carcinoma (TCC) (B) differences were statistically significant in both cases. Analysis was performed by the Wilcoxon Signed Ranks Test.
250 mM, 300 mM, 350 mM, 500 mM, and 1 M KCl. Following fractionation, the samples were dried by speed-vacuuming and stored at -20 °C. Prior to reverse phase nanobore liquid chromatography-tandem mass spectrometric (nanoLC-MS/ MS) analysis, these fractions were redissolved in 10 µL of an aqueous solution of 1.0% formic acid. Nanobore LC-MS/MS. We used a nanobore LC system from LC Packings (Amsterdam, The Netherlands) consisting of a Famos autosampler and an Ultimate Nano LC system. The LC system was interfaced to an API QSTAR Pulsar-i hybrid quadrupole/time-of-flight (QqTOF) tandem mass spectrometer (Applied Biosystems/MDS Sciex, Foster City, CA) equipped with a Protana NanoES ion source (Protana Engineering A/S, Odense, Denmark). The spray capillary was a PicoTip SilicaTip emitter with a 10-µm ID tip (New Objective, Woburn, MA). The
nanobore LC column was 75-µm ID × 150-mm length reversephase nano capillary column packed in-house with 3 µm C18 beads with 100 Å pores (Kromasil). One µL of sample was injected via the “µL-pick-up” mode. Separation was performed using a binary mobile-phase gradient at a total flow rate of 200 nL/min. For nanospray analysis, the following source conditions were used: a curtain-gas setting of 20 and an ionspray voltage of 1800-3000 V, Q0 declustering potential of 65 V, and a focusing potential 265 V. Nitrogen was used as the collision gas (CAD setting ) 5) for both TOF MS and MS/MS scans. All nanoLC MS/MS data were acquired in information-dependent acquisition (IDA) mode using Analyst QS 1.1 (Applied Biosystems/MDS SCIEX). We performed two sets of analysis for each fraction. MS cycles comprised a TOF MS survey scan with an m/z range of 400-1500 Th for 1 s, followed by five product ion scans with an m/z range of 80-2000 Th for 2 s each. Collision energy (CE) was automatically controlled by the IDA CE Parameters script. Switching criteria were set to ions greater than 400 Th and smaller than 1500 Th with a charge state of 2 to 5 and an abundance of g10counts/s. Former target ions were excluded for 30s and ions within a 4-Th window were ignored. In addition, the IDA Extensions II script was set to no repetitions before dynamic exclusion. In all experiments, the script was set to select a precursor ion nearest to a threshold of15 count/s every 4 cycles. These settings ensured examination of not only high abundance ions, but low abundance ones as well. Figure 1 shows a representative scan.
Data Analysis Data analysis for the iTRAQ experiments were performed with ProteinPilot version 2.0.1 (Applied Biosystems) using a human Celera protein sequence database (human KBMS 20041109) provided by Applied Biosystems that contained a total of 178,239 protein sequences and comprised sequences from NCBI′s nr, refseq, Swiss-Prot, TrEMBL and Celera databases. ProteinPilot utilizes the Paragon algorithm for assigning sequence identity.27 Redundancy between proteins identified is minimized using a grouping function, which assigns an “unused score” to the peptides that are unique to a protein or group of redundant proteins. The cutoff unused score used for assessing detection was 1.3, which corresponds to a confidence Journal of Proteome Research • Vol. 8, No. 8, 2009 3801
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Table 4. Dysregulated Proteins in Our Analysis That Were Found to Be Differentially Expressed in Kidney Cancer by Other Independent Reports
Table 5. Partial List of Proteins from Our Analysis with Documented Kidney Expression in the Swiss-Prot Database Swiss Prot
Gene symbol
Q03154 P30038 P49189 P05090 Q93088 P46940 O00151 Q01082 P27348 Q9UKL9 Q6DN90 Q8IZK5 Q6IPK9 Q6I9V1 O75531 Q16698 Q96H54 Q96CU2 Q9Y4F1 P35558 Q9Y371 O95994 P38117
ACY1 ALDH4A1 ALDH9A1 APOD BHMT IQGAP1 PDLIM1 SPTBN1 YWHAQ AKR1C3 IQSEC1 LDHD CES2 DPEP1 BANF1 DECR1 ECHS1 EEF1G FARP1 PCK1 SH3GLB1 AGR2 ETFB
of 95%. Relative quantification of proteins in the case of iTRAQ is performed on the MS/MS scans and is the ratio of the areas under the peaks at 114, 115, 116, and 117 Da which are the masses of the tags that correspond to the iTRAQ reagents. The protein ratios are calculated using the individual ratios of the peptides with a weighting factor incorporated based on the confidence of the matching peptide. Normalization of the ratio 3802
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Table 6. Comparison between Protein and SAGE mRNA Expression Data for Upregulated Proteins That Were Detected in Both Runs SAGEb a
gene symbol
protein
SERPING1 LDHA FABP7 GAPDHS CRYAB CLIC1 ANXA5 GAPDH HPX PCBP1 KRT18 ENO1 PRDX6 ACTB CNDP2 AKR1A1 PTBP1 NDRG1
3.6651 3.3210 2.7722 2.6716 2.4895 2.4112 2.3679 2.2959 2.2292 2.1984 2.1134 2.0265 1.9705 1.8686 1.8460 1.8214 1.8200 1.8022
normal
cancer
9 29 1 1 136 48 4 87 1 24 39 1 4 331 53 19 1 4
31 71 6 3 5 57 25 1326 4 15 239 15 23 285 167 25 15 9
a Protein expression is expressed as the fold change of expression of cancer: normal. b SAGE expression data are presented as relative expression value (tag/200,000 tags).
is performed by the ProteinPilot using the median ratio obtained across all the proteins identified in a run. This normalization is based on the assumption that most proteins will not show significant differential expression between the samples. The normalization factor is termed the “Applied bias” and is calculated for each pair of samples.
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Proteomics in Kidney Cancer
Table 7. Partial List of Consistently Overexpressed Proteins (in Both Runs) in RCC Compared to Two Different in silico EST Databases EST ProfileViewera
a
digital gene expression displayerb
gene symbol
normal
cancer
normal
cancer
overall value
ACTB ACTR3 AKR1A1 ALB ANXA5 CLIC1 CNDP2 CTSD DCTN2 DYNC1I2 EEF2 FABP7 FAM49B GAPDH GAPDHS GSTA2 KRT18 LDHA MTPN MVP PCBP1 PRDX6 PTBP1
3090 291 136 1180 112 211 508 329 230 235 197 32 51 2309 9 42 310 870 23 399 89 225 169
3631 86 144 14 187 446 489 879 230 230 288 14 57 6066 14 14 547 835 28 244 115 259 288
111 0 N/A 155 7 2 57 2 7 28 4 0 4 11 1 5 6 47 4 65 0 22 14
185 1 N/A 0 11 34 26 0 15 3 15 7 3 260 0 3 38 52 7 7 7 15 16
Increased in both Increased in one Increased in one Decreased in both Increased in both Increased in both Decreased in both Increased in one Increased in one Decreased in both Increased in both Increased in one Increased in one Increased in both Increased in one Decreased in both Increased in both Increased in one Increased in both Decreased in both Increased in both Increased in one Increased in both
EST ProfileViewer values are calculated as transcripts/million.
b
Digital gene expression displayer values are calculated as sequence odds ratio.
Bioinformatics Analysis. Proteins were classified into groups according to subcellular compartmentalization (e.g., cytoplasm, nucleus, membranes, etc.), biological process (e.g., cell cycle proteins), and molecular function (e.g., receptor binding). These analyses were performed through the Gene Ontology (GO) Consortium databases (http://www.geneontology.org/) and their related analytical tools. Bioinformatic validation of gene expression in normal and cancerous tissue was performed via the Swiss-Prot Protein knowledgebase and the TrEMBL annotated databases (http:// expasy.org/sprot/), GeneCards (http://www.genecards.org/), and NCBI (http://www.ncbi.nlm.nih.gov/). In silico analysis and validation of differential gene expression in cancer were carried out via two independent databases: the SAGE database of the Cancer Genome Anatomy Project (CGAP) (http://cgap.nci.nih.gov/), and the UniGene EST ProfileViewer (EPV), Digital Gene Expression Displayer (DGED), and xProfiler analysis (http://www.ncbi.nlm.nih.gov/). EPV expression values are calculated as transcript/million. DGED values are calculated as the sequence odds ratios. A pool of 5 normal kidney libraries and 7 kidney cancer libraries (RCC) were chosen for the DGED analysis. For SAGE, relative expression values were used for analyses (defined as tags/200,000). Specimens from both tissues and cell lines were incorporated into the analyses. Tags that map to more than one gene and those that are not informative (no expression values in either cancer or normal) were excluded; cases with discrepancy between different tags were also excluded. Immunohistochemical Validation. Paraffin blocks were sectioned 4 µm thick, mounted on slides, and dried overnight. Sections were deparaffinized in xylene and rehydrated through decreasing graded alcohols. Slides were immunostained using the Benchmark XT (Ventana, Tucson, AZ) with monoclonal antibodies for vimentin (Ventana, Tucson, AZ) and phosphoS6 ribosomal protein (Cell Signaling Tech, Danver, MA).
Immune complex was visualized by incubating with diaminobenzidine (DAB), and sections were counterstained with hematoxylin. Five pairs of normal and cancer from the same patient were examined. Slides were reviewed and scored (% positivity and density) independently by two pathologists. Dot Blot Sample Preparation. Fresh frozen tissue samples that were stored in -80 °C were used for protein validation experiments. Frozen tissues were weighted and suspended in cold 1X PSB buffer with protease inhibitors (Roche Diagnostics, Indianapolis, IN) to a concentration of 2.5% (w/v). Samples were homogenized with a hand-held tissue homogenizer for 30 s on ice and subsequently centrifuged for 1min at 8000 rpm The supernatant of the homogenate was used for the dot blot experiments. Dot Blot. Five pairs of coupled normal can cancer tissues were tested by immunoblotting. Two microliters of tissue homogenate for each sample was added to labeled nitrocellulose membrane and dried in a warming chamber for 1 h. The membrane was then blocked with TBS-T buffer (pH 7.5) with 5% milk for 1 h at RT. The membrane was probed with monoclonal antibodies for NNMT, LDHAL6B and SERPING1 (Cedarlane Laboratories Canada, Burlington, ON) diluted in TBS-T buffer as per manufacturer′s recommendation. The membranes were washed 3 times in TBS-T buffer and probe with HRP conjugated anit-mouse antibody (Fisher Scientific Canada, Ottawa, ON) for 30 min in RT. The membranes were washed 3 times in TBS-T buffer and once in TBS buffer. 3 mL of chemiluminescent reagent (Diagnostic Product Corporation, Gwynedd, UK) was added to each membrane and the membranes were incubated for 3 min. The membranes were then exposed to X-ray film for 30 min.
Results We identified a total of 937 proteins in both runs. Using cutoff values of 1.5 fold for overexpression and 0.67 fold for Journal of Proteome Research • Vol. 8, No. 8, 2009 3803
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Figure 5. Immunohistochemistry validation of protein expression using antibodies to Vimentin in normal kidney tissue (A) and clear cell RCC (B). Staining was minimally presence in the normal tubules whereas significant staining was observed in the cancerous tissue from the same patient confirming protein overexpression in RCC.
Figure 6. Representative dot-blot validation of protein expressions of three highly overexpressed proteins in normal tissue (N) versus clear cell RCC tissue (C). Luminescence signals were significantly increased in the cancer sample.
underexpression, we were able to identify 168 underexpressed proteins and 156 proteins that were overexpressed in RCC compared to their normal counterparts. These cutoff values were used by us for selecting proteins for further statistical analyses in previous studies28 and were found to perform satisfactorily. Table 1 shows the distribution of over- and underexpressed proteins in RCC specimens in the two runs. There were 65 proteins that were recognized in both runs as being underexpressed, while 34 proteins as being overex3804
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Siu et al. pressed. There was a statistically significant positive correlation of normalized protein levels identified in both runs (rp ) 0.695, p > 0.001) (Figure 2). Cellular localization of our extracted proteins was determined using the GO analysis and most proteins were assigned to cellular compartments: 34% of the proteins were cytoplasmic, 13% nuclear, 11% were localized to the mitochondria, 4% membranous, and 6% were extracellular proteins (Figure 3A). A comparable distribution was seen for the overexpressed proteins (Figure 3B). Proteins of cytoplasmic and membranous distribution are of particular importance, as they may have potential for use as tumor markers in biological fluids. Table 2 shows a partial list of the top dysregulated proteins in RCC compared to their normal counterparts, along with their chromosomal locations: 49 proteins demonstrated an average increase of g2.0 fold; nine proteins showed g3.0 fold average increase in expression levels. Underexpressed proteins ranged from 0.04 to 0.67 in expression relative to normal tissues. To examine if these dysregulations were specific to RCC, or represented a nonspecific response by the kidney cells, we analyzed two control specimens simultaneously: a transitional cell carcinoma (TCC, with a distinct origin from the transitional urothelium of the kidney) and kidney tissue from a case of endstage glomerulonephritis. Protein expressions were normalized using expression levels in normal kidney tissue, and expressed as fold changes of normal. The normalized average expression in RCC was lower than normalized noncancerous renal failure in 454 proteins and higher in 483 proteins (p ) 0.020) (Figure 4A). RCC normalized average expression values were also lower than those of normalized TCC in 415 proteins, and higher in 522 proteins (p ) 0.005) (Figure 4B). Table 3 shows the relative normalized protein expression ratios between RCC, renal failure and TCC. We also compared our results with four previously published reports of differential protein expression in kidney cancer.29-32 There are 24 proteins from our list of proteins that were identified in the other reports; 15 of them were shown to be differentially expressed by one report, five identified by two additional reports, and four recognized by three studies (Table 4). We performed an in silico validation of our results by different approaches. In addition, we did a thorough literature search for individual up- and downregulated proteins: a few of our dysregulated proteins were found to be previously reported as tumor markers for RCC (vide infra). We also performed a database search through the Swiss-Prot Knowledgebase. Table 5 shows a partial list of proteins from our list with documented expression in the kidney. To validate our protein findings and examine whether they are reflected at the mRNA level, we performed SAGE and EST analysis. SAGE data verified overexpression of 65% of all genes with informative expression data (data not shown). When focusing on proteins that were identified in both runs, the SAGE mRNA data were in concordance with the protein results in 84% of cases (Table 6). EST analysis of informative genes using both the EST ProfileViewer and the Digital Gene Expression Displayer search engines confirmed upregulation in cancer compared to normal in 74% of cases by at least one search engine (Table 7). Immuohistochemistry and dot blot were employed to further validate the protein expression results. Vimentin, which was found to overexpressed in RCC in our MS/MS analyses, was validated by immunohistochemistry (Figure 5). Staining for
Proteomics in Kidney Cancer vimentin antibodies was weak or largely absent in normal cancer tissue while significant staining can be observed in RCC tissue. Similar result was obtained using phospho-S6 ribosomal protein (another overexpressed protein) antibodies (Data not shown). Three highly overexpressed proteins were selected for dot blot analysis. (Figure 6). Dot blot analysis showed all 3 proteins were overexpressed in RCC tissue, further confirming the MS/MS results. To validate the potential clinical utility of the dysregulated proteins as potential serum biomarkers, we performed a literature and bioinformatics search (through public databases) of our upregulated proteins and were able to identify 23 of these proteins being secreted in the blood (data not shown). We are currently performing individual validation of few of these biomarkers in serum of RCC patients.
Discussion This study is the first report of quantitative protein analysis of clear cell RCC utilizing isotope affinity tags (iTAQ). Using this approach, we identified dysregulated proteins in clear cell RCC by direct tissue analysis. The robustness of the analysis is confirmed by the good positive correlation between the two runs (rp ) 0.695, p > 0.001) (Figure 2). Proteomic analyses are directly related to clinical applications, since proteins determine the ultimate phenotypic expression in cancer tissues. Advanced features of our mass spectrometric analyses include simultaneous profiling of multiple admixed specimens, which helps to eliminate artifactual differences due to differential protein losses during purification or separation.33 In addition, the use of four isotopic labels allows quantitative comparison among four different tissue samples. Differences between our results and other previously published proteomics analyses of RCC 34-37 could be attributed to technical differences (i.e., the mass spectral approaches) and the nature of the material analyzed (e.g., cell culture vs tissue). Another important factor to consider is that our protocol is the first to allow “quantitative” analysis of protein expression. Other important factors to be considered are tumor heterogeneity and the differences in histological types of the tumors analyzed. We have attempted to validate our results by using multiple bioinformatics approaches. There was 60 -70% agreement on average, between our results and two independent databases (EST and SAGE) (Tables 6 and 7) analyzed by multiple search engines. Differences and inconsistencies between our protein analysis and these RNA data could be attributed to the possibility of post-transcriptional modifications and the inherited inaccuracy of SAGE quantification, especially at low expression levels. Other technical issues with EST library construction, like subtraction or normalization, could have also be potential sources of discrepancy. Literature searches revealed that many of our dysregulated proteins were previously reported as potential tumor markers for kidney cancer. Major vault protein (MVP), also known as lung resistance-related protein (LRP) (Table 2, No. 32), has been documented to be expressed in the normal kidney tubules.38 A recent study showed that this protein is upregulated in RCC.39 Overexpression of LRP predicts a poor response to chemotherapy. In addition to kidney cancer, LRP is also upregulated in ovarian cancer and other malignancies.40,41 Another gene that is upregulated in clear-cell RCC is the adipose differentiation-related protein (ADFP). ADFP (Table 2, No. 33) is a lipid storage droplet-associated protein, and its transcription is
research articles considered to be regulated by the von Hippel-Lindau/hypoxiainducible factor pathway.42 Patients with higher AFDP levels showed significantly better survival than those who did not.43 Nicotinamide N-methyltransferase (NNMT) (Table 2, No. 7) was also shown to be upregulated in RCC. Patients with higher NNMT levels showed significantly better survival than those expressing lower levels.44 In addition to RCC, NNMT was also shown to be dysregulated in other malignancies, including stomach adenocarcinoma, glioblastoma, papillary thyroid carcinoma, and oral squamous cell carcinoma.45 NNMT catalyzes the N-methylation of nicotinamide and is involved in xenobiotic metabolism. Nicotinamide is a part of the NAD molecule, which is involved in many important biological processes including cellular resistance and energy production.46 Preliminary evidence suggest that NNMT might have potential therapeutic applications in cancer.47 A recent study has also shown that it might help in predicting response to radiation in bladder cancer.48 Thymidine phosphorylase (TP) (Table 2, No.31), known as a platelet-derived endothelial cell growth factor (PDECGF), is a mitogenic and angiogenic factor derived from platelets.49 TP levels were found to be higher in various types of malignant tumor, including RCC, than the adjacent nonneoplastic tissues.50 TP was found to be an unfavorable independent prognostic factor in RCC.51 Alpha-Crystallin β (Table 2, No. 23) is a major protein component of the vertebrate eye lens with chaperone-like functions and is a member of the small heat-shock protein HSP20. It has been shown to be dysregulated in RCC.52 Pinder et al. reported that 90% of RCC tissue specimens they studied show strong staining by anti-Rβ-Crystallin antibody.53 The biological significance of the overexpression of Rβ-Crystallin in RCC carcinogenesis is unclear. It probably represents a stress response in RCC given the fact that Rβ-Crystallin is a member of small heat-shock protein HSP20 family. In addition, we identified a number of new biomarkers. For example: Prothymosin alpha (Table 2, No. 81) is a small, highly acidic, nuclear protein that has been proposed to play a role in cell proliferation and immune regulation.54 Prothymosin alpha has recently been proposed to be a potential marker of proliferation in patients with thyroid cancer.55 This protein was implicated in various other cancers, including gastric, lung, liver, colon, breast, and head-and-neck cancers.56-61 Another interesting protein is the tryptophan 5-monooxygenase activation protein (Table 2, No. 2), which is a protein kinasedependent activator of tyrosine and tryptophan hydroxylases, and is an endogenous inhibitor of protein kinase C. This family of proteins mediates signal transduction by binding to phosphoserine-containing protein.62 Poly (rc)-binding protein 2 (Table 2, No. 9), another up regulated protein, is a poly(rc)binding protein with translational regulatory function.63 CAPNS1 (calpain, small subunit 1) (Table 2, No. 3), belongs to the calcium-dependent cysteine proteinases that has a regulatory function. It was recently reported as a cancer-associated gene64 that undergoes alternative splicing in cancer tissues leading to removal of the N-terminal glycine-rich domain implicated in interaction with lipids.64 In conclusion, our quantitative mass spectrometry analysis revealed a number of potential proteins that are dysregulated in RCC. This was verified by in silico analysis and literature review. Whether these proteins are consistently differentially expressed in RCC will require a larger-scale analysis in the future. We are currently pursuing validation of these potential Journal of Proteome Research • Vol. 8, No. 8, 2009 3805
research articles biomarkers with an independent technique and assessment of their clinical utility in RCC diagnosis and prognosis. Abbreviations: RCC, renal cell carcinoma; TCC, transitional (urothelial) cell carcinoma; LC-MS/MS, liquid chromatography-tandem mass spectrometry.
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