Title: Pancreatic Cancer Proteome: The Proteins That Underlie Invasion, Metastasis, and Immunologic Escape
Abstract: Background & Aims: Pancreatic cancer is a highly lethal disease that has seen little headway in diagnosis and treatment for the past few decades. The effective treatment of pancreatic cancer is critically relying on the diagnosis of the disease at an early stage, which still remains challenging. New experimental approaches, such as quantitative proteomics, have shown great potential for the study of cancer and have opened new opportunities to investigate crucial events underlying pancreatic tumorigenesis and to exploit this knowledge for early detection and better intervention. Methods: To systematically study protein expression in pancreatic cancer, we used isotope-coded affinity tag technology and tandem mass spectrometry to perform quantitative proteomic profiling of pancreatic cancer tissues and normal pancreas. Results: A total of 656 proteins were identified and quantified in 2 pancreatic cancer samples, of which 151 were differentially expressed in cancer by at least 2-fold. This study revealed numerous proteins that are newly discovered to be associated with pancreatic cancer, providing candidates for future early diagnosis biomarkers and targets for therapy. Several differentially expressed proteins were further validated by tissue microarray immunohistochemistry. Many of the differentially expressed proteins identified are involved in protein-driven interactions between the ductal epithelium and the extracellular matrix that orchestrate tumor growth, migration, angiogenesis, invasion, metastasis, and immunologic escape. Conclusions: Our study is the first application of isotope-coded affinity tag technology for proteomic analysis of human cancer tissue and has shown the value of this technology in identifying differentially expressed proteins in cancer. Background & Aims: Pancreatic cancer is a highly lethal disease that has seen little headway in diagnosis and treatment for the past few decades. The effective treatment of pancreatic cancer is critically relying on the diagnosis of the disease at an early stage, which still remains challenging. New experimental approaches, such as quantitative proteomics, have shown great potential for the study of cancer and have opened new opportunities to investigate crucial events underlying pancreatic tumorigenesis and to exploit this knowledge for early detection and better intervention. Methods: To systematically study protein expression in pancreatic cancer, we used isotope-coded affinity tag technology and tandem mass spectrometry to perform quantitative proteomic profiling of pancreatic cancer tissues and normal pancreas. Results: A total of 656 proteins were identified and quantified in 2 pancreatic cancer samples, of which 151 were differentially expressed in cancer by at least 2-fold. This study revealed numerous proteins that are newly discovered to be associated with pancreatic cancer, providing candidates for future early diagnosis biomarkers and targets for therapy. Several differentially expressed proteins were further validated by tissue microarray immunohistochemistry. Many of the differentially expressed proteins identified are involved in protein-driven interactions between the ductal epithelium and the extracellular matrix that orchestrate tumor growth, migration, angiogenesis, invasion, metastasis, and immunologic escape. Conclusions: Our study is the first application of isotope-coded affinity tag technology for proteomic analysis of human cancer tissue and has shown the value of this technology in identifying differentially expressed proteins in cancer. Pancreatic adenocarcinoma is a highly aggressive tumor. There is an urgent need to develop new biomarkers to allow for diagnosis of pancreatic cancer at an earlier stage, which may markedly improve the survival rate.1Brand R. The diagnosis of pancreatic cancer.Cancer J. 2001; 7: 287-297PubMed Google Scholar Our understanding of the proteins that play a role in cancer development is fundamental to the development of early biomarkers of disease and targets for effective intervention. Gene expression profiling has recently been applied to discover differentially expressed genes in pancreatic cancer.2Crnogorac-Jurcevic T. Efthimiou E. Nielsen T. Loader J. Terris B. Stamp G. Baron A. Scarpa A. Lemoine N.R. Expression profiling of microdissected pancreatic adenocarcinomas.Oncogene. 2002; 21: 4587-4594Crossref PubMed Scopus (193) Google Scholar, 3Han H. Bearss D.J. Browne L.W. Calaluce R. Nagle R.B. Von Hoff D.D. Identification of differentially expressed genes in pancreatic cancer cells using cDNA microarray.Cancer Res. 2002; 62: 2890-2896PubMed Google Scholar, 4Iacobuzio-Donahue C.A. Maitra A. Shen-Ong G.L. van Heek T. Ashfaq R. Meyer R. Walter K. Berg K. Hollingsworth M.A. Cameron J.L. Yeo C.J. Kern S.E. Goggins M. Hruban R.H. Discovery of novel tumor markers of pancreatic cancer using global gene expression technology.Am J Pathol. 2002; 160: 1239-1249Abstract Full Text Full Text PDF PubMed Scopus (279) Google Scholar, 5Iacobuzio-Donahue C.A. Maitra A. Olsen M. Lowe A.W. van Heek N.T. Rosty C. Walter K. Sato N. Parker A. Ashfaq R. Jaffee E. Ryu B. Jones J. Eshleman J.R. Yeo C.J. Cameron J.L. Kern S.E. Hruban R.H. Brown P.O. Goggins M. Exploration of global gene expression patterns in pancreatic adenocarcinoma using cDNA microarrays.Am J Pathol. 2003; 162: 1151-1162Abstract Full Text Full Text PDF PubMed Scopus (427) Google Scholar, 6Logsdon C.D. Simeone D.M. Binkley C. Arumugam T. Greenson J.K. Giordano T.J. Misek D.E. Hanash S. Molecular profiling of pancreatic adenocarcinoma and chronic pancreatitis identifies multiple genes differentially regulated in pancreatic cancer.Cancer Res. 2003; 63: 2649-2657PubMed Google Scholar While these techniques are powerful for identifying differentially expressed genes, information of the corresponding protein levels is less well known. It is evident that RNA levels do not necessarily correlate with protein levels.7Gygi S.P. Rochon Y. Franza B.R. Aebersold R. Correlation between protein and mRNA abundance in yeast.Mol Cell Biol. 1999; 19: 1720-1730Crossref PubMed Scopus (3235) Google Scholar Recently, there has been substantial interest in applying proteomic methods for the discovery of new therapeutics targets and new biomarkers for diagnosis and early detection.8Hanash S. Disease proteomics.Nature. 2003; 422: 226-232Crossref PubMed Scopus (891) Google Scholar Among these methods, the recently developed isotope-coded affinity tag (ICAT) technology provides a more comprehensive approach for the discovery and quantification of the proteome.9Gygi S.P. Rist B. Gerber S.A. Turecek F. Gelb M.H. Aebersold R. Quantitative analysis of complex protein mixtures using isotope-coded affinity tags.Nat Biotechnol. 1999; 17: 994-999Crossref PubMed Scopus (4400) Google Scholar In an effort to systematically study protein expressions in pancreatic cancer, we used ICAT to perform quantitative protein profiling of 2 pancreatic cancer tissues. Validation of the discovered proteins from ICAT analysis was performed using Western blotting, immunohistochemistry (IHC), and tissue microarray analysis. This study identified many unique proteins that have not previously been associated with pancreatic cancer or other cancers; thus, these proteins could be new targets for future development of biomarkers for early diagnosis and therapy. The data show the important interaction and cooperation between ductal epithelium (cancer) and the extracellular matrix. Lastly and most importantly, the data provide new information at the protein level that improves our understanding of how pancreatic cancer is initiated and sustained. Tissue specimens from patients with pancreatic cancer were collected in accordance with approved human subject guidelines at the University of Washington, Virginia Mason Hospital, and the Cleveland Clinic. The specimens were obtained from surgical resections and kept frozen at −70°C in minimal essential medium with 10% dimethyl sulfoxide until use. Tissue was placed in M-PER (Pierce, Rockford, IL) with 1× Protease Inhibitor Cocktail (Pierce) and then lysed by homogenization followed by centrifugation at 14,000 rpm for 15 minutes. The supernatants were then either SpeedVac (Savant Instruments, Holbrook, NY) dried or acetone precipitated and resuspended in ICAT labeling buffer (0.05% sodium dodecyl sulfate, 50 mmol/L Tris, pH 8.3, 5 mmol/L EDTA, and 6 mol/L urea). For each sample, 1 mg protein was labeled with the acid-cleavable ICAT reagents, either the isotopically light (normal pancreas) or heavy (cancer) forms (Applied Biosystems, Foster City, CA) as previously described.10von Haller P.D. Yi E. Donohoe S. Vaughn K. Keller A. Nesvizhskii A.I. Eng J. Li X.J. Goodlett D.R. Aebersold R. Watts J.D. The application of new software tools to quantitative protein profiling via isotope-coded affinity tag (ICAT) and tandem mass spectrometry: II. Evaluation of tandem mass spectrometry methodologies for large-scale protein analysis, and the application of statistical tools for data analysis and interpretation.Mol Cell Proteomics. 2003; 2: 428-442Crossref PubMed Scopus (93) Google Scholar The labeled normal sample and the matching labeled cancer sample were combined and digested into peptides by trypsin (Promega, Madison, WI). ICAT-labeled peptides were subsequently fractionated by cation-exchange chromatography and purified by avidin-affinity chromatography. The resulting 40 fractions were then combined into 21 fractions and analyzed by microcapillary high-performance liquid chromatography/electrospray ionization/tandem mass spectrometry using an ion-trap mass spectrometer (LCQ-DecaXP; ThermoFinnigan, San Jose, CA) as previously described.11Han D.K. Eng J. Zhou H. Aebersold R. Quantitative profiling of differentiation-induced microsomal proteins using isotope-coded affinity tags and mass spectrometry.Nat Biotechnol. 2001; 19: 946-951Crossref PubMed Scopus (837) Google Scholar Tandem mass spectrometry spectra were searched against the National Cancer Institute human sequence database using SEQUEST.12Eng J. McCormack A.L. Yates J.R. An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database.J Am Soc Mass Spectrom. 1994; 5: 976-989Crossref PubMed Scopus (5602) Google Scholar The database search results were validated using the PeptideProphet program.13Keller A. Nesvizhskii A.I. Kolker E. Aebersold R. Empirical statistical model to estimate the accuracy of peptide identifications made by MS/MS and database search.Anal Chem. 2002; 74: 5383-5392Crossref PubMed Scopus (4008) Google Scholar PeptideProphet uses various SEQUEST scores and a number of other parameters to calculate a probability score for each identified peptide. The peptides were then assigned a protein identification using the ProteinProphet software.14Nesvizhskii A.I. Keller A. Kolker E. Aebersold R. A statistical model for identifying proteins by tandem mass spectrometry.Anal Chem. 2003; 75: 4646-4658Crossref PubMed Scopus (3771) Google Scholar ProteinProphet allows filtering of large-scale data sets with assessment of predictable sensitivity and false-positive identification error rates. Quantification of the ratio of each protein (isotopically heavy [cancer] vs light [normal]) was calculated using the ASAPRatio program.15Li X.J. Zhang H. Ranish J.A. Aebersold R. Automated statistical analysis of protein abundance ratios from data generated by stable-isotope dilution and tandem mass spectrometry.Anal Chem. 2003; 75: 6648-6657Crossref PubMed Scopus (321) Google Scholar Information about these software tools and the software tools themselves can be found online at http://www.systemsbiology.org/Default.aspx?pagename=proteomicssoftware. Classifications of the identified proteins were based on the Gene Ontology consortium using the GoMiner program.16Zeeberg B.R. Feng W. Wang G. Wang M.D. Fojo A.T. Sunshine M. Narasimhan S. Kane D.W. Reinhold W.C. Lababidi S. Bussey K.J. Riss J. Barrett J.C. Weinstein J.N. GoMiner a resource for biological interpretation of genomic and proteomic data.Genome Biol. 2003; 4: R28Crossref PubMed Google Scholar A total of 20 μg of protein from each specimen was subjected to sodium dodecyl sulfate/polyacrylamide gel electrophoresis. The gel was then blotted onto a nitrocellulose membrane according to the manufacturer's protocol (Amersham Biosciences, Piscataway, NJ). The anti–cathepsin D and anti–annexin A2 antibody were purchased from Santa Cruz Biotechnology (Santa Cruz, CA). Anti–integrin β1 antibody was from BD Biosciences (San Jose, CA). ECL Plus Kit (Amersham Biosciences) was used to detect protein in the Western blot. The tissue array was constructed from representative regions of tissue from paraffin-embedded formalin and Hollande's fixed samples, including 128 ductal adenocarcinoma samples from 47 patients and 12 normal pancreas samples from 4 patients. Two 1.5-mm-diameter cores per case were re-embedded in a tissue using a tissue arrayer (Beecher Instruments, Silver Spring, MD) according to a method described previously.17Kononen J. Bubendorf L. Kallioniemi A. Barlund M. Schraml P. Leighton S. Torhorst J. Mihatsch M.J. Sauter G. Kallioniemi O.P. Tissue microarrays for high-throughput molecular profiling of tumor specimens.Nat Med. 1998; 4: 844-847Crossref PubMed Scopus (3592) Google Scholar Serial sections of the paraffin-embedded pancreatic tissue arrays were immunohistochemically stained using a primary monoclonal immunoglobulin G1 antibody specific for annexin A2 (BD Biosciences, PharMingen, San Diego, CA). Slides were processed for antigen retrieval using microwave heating in citrate buffer (0.1 mol/L, pH 6.0) for 20 minutes, followed by cooling to room temperature and then primary antibody incubation for 32 minutes. Biotinylated anti-mouse secondary antibody and streptavidin-peroxidase were applied (ES auto stains; Ventana Medical Systems, Tucson, AZ). Diaminobenzidine substrate was applied. Results were scored as diffuse or focal and were graded (from 0 [negative] to 3+ [intensely positive]) for both neoplasm, admixed benign epithelial elements (ducts, acini, islets) and surrounding stroma. In this study, we performed 2 separate ICAT analyses using tandem mass spectrometry on 2 different pancreatic cancer samples. In the 2 cancer /normal pancreas experiments, we identified and quantified 523 and 384 unique proteins, respectively, for each experiment, with ProteinProphet scores ≥0.9 (true positive rate is >99%, and error rate is <0.9%). Taken together, the 2 experiments identified 656 unique proteins with an error rate ≤0.9%, of which 251 were identified in both experiments (Figure 1A). We next examined the cellular locations of these proteins. Cellular location was assigned according to the Gene Ontology nomenclature using the GoMiner program.16Zeeberg B.R. Feng W. Wang G. Wang M.D. Fojo A.T. Sunshine M. Narasimhan S. Kane D.W. Reinhold W.C. Lababidi S. Bussey K.J. Riss J. Barrett J.C. Weinstein J.N. GoMiner a resource for biological interpretation of genomic and proteomic data.Genome Biol. 2003; 4: R28Crossref PubMed Google Scholar The proteins identified fell into all categories of cellular components (Figure 1B). A large portion of the identified proteins were extracellular (10.5%) or membrane (16.5%) proteins, suggesting a great potential of using this approach to identify protein biomarkers. Membrane and extracellular proteins are excellent candidates for biomarker development because they have the best chance of being shed into the circulatory system. Cytoplasmic proteins made up almost one third of the proteins identified. Sixteen percent of the proteins were not annotated by the Gene Ontology nomenclature or the cellular locations of these proteins were still unknown. The quantification of each protein is presented as a protein ratio between cancer and normal pancreas. A scatter plot for the ratios of all proteins quantified in both experiments is presented in Figure 2. The protein ratios ranged from 0.05 to 12.66, with an average ratio of 0.91, indicating that the ratios of abundance between cancer and normal pancreas were approaching 1 for the majority of the proteins. For cancer sample 1, a total of 111 proteins showed an abundance change of at least 2-fold in cancer, representing differential expression by at least 2-fold (Figure 3A), of which 60 were overexpressed and 51 were underexpressed. For cancer sample 2, a total of 90 proteins were differentially expressed by at least 2-fold (Fig. 3A), of which 60 were overexpressed and 30 were underexpressed. Altogether, 201 differentially expressed (ratios were >2.0 or <0.5) proteins were found in the 2 ICAT experiments. Among them, 50 were differentially expressed in both cancer samples. Thus, there were 151 individual and separate proteins identified in the 2 experiments with a differential expression ratio >2.0 or <0.5. Of these proteins, 90 were overexpressed and 61 were underexpressed in at least one cancer sample (Table 1, Table 2).Figure 3Differentially expressed proteins in pancreatic cancer. (A) For cancer sample 1 and sample 2, 111 proteins and 90 proteins were differentially expressed by at least 2-fold, respectively. The ratios for the rest of the proteins were between 0.5 and 2.0, which represented <2-fold abundance change in cancer samples. (B) For the 251 proteins identified in both experiments, 50 were differentially expressed in both cancer samples by at least 2-fold, while 25 were only differentially expressed in sample 1, and 16 were only differentially expressed in sample 2. The ratios for the rest of the 160 proteins were between 0.5 and 2 for both samples. Protein ratio represents protein abundance in cancer/normal.View Large Image Figure ViewerDownload (PPT)Table 1Up-regulated Proteins in Pancreatic CancerDatabase IDProtein nameRatio (cancer/normal)SDReported in pancreasReferenceSW:143S_HUMAN14-3-3 protein σ (stratifin)aDifferentially expressed in 2 cancer samples. When a protein was differentially expressed in both cancer samples, the ratio presented here was the average from both samples.2.731.18Yes6Logsdon C.D. Simeone D.M. Binkley C. Arumugam T. Greenson J.K. Giordano T.J. Misek D.E. Hanash S. Molecular profiling of pancreatic adenocarcinoma and chronic pancreatitis identifies multiple genes differentially regulated in pancreatic cancer.Cancer Res. 2003; 63: 2649-2657PubMed Google Scholar, 24Nakamura T. Furukawa Y. Nakagawa H. Tsunoda T. Ohigashi H. Murata K. Ishikawa O. Ohgaki K. Kashimura N. Miyamoto M. Hirano S. Kondo S. Katoh H. Nakamura Y. Katagiri T. Genome-wide cDNA microarray analysis of gene expression profiles in pancreatic cancers using populations of tumor cells and normal ductal epithelial cells selected for purity by laser microdissection.Oncogene. 2004; 23: 2385-2400Crossref PubMed Scopus (224) Google ScholarSW:ACTC_HUMANActin, α cardiacaDifferentially expressed in 2 cancer samples. When a protein was differentially expressed in both cancer samples, the ratio presented here was the average from both samples.3.791.91SW:ACTB_HUMANActin, cytoplasmic 1 (β-actin)4.243.93Yes6Logsdon C.D. Simeone D.M. Binkley C. Arumugam T. Greenson J.K. Giordano T.J. Misek D.E. Hanash S. Molecular profiling of pancreatic adenocarcinoma and chronic pancreatitis identifies multiple genes differentially regulated in pancreatic cancer.Cancer Res. 2003; 63: 2649-2657PubMed Google Scholar, 24Nakamura T. Furukawa Y. Nakagawa H. Tsunoda T. Ohigashi H. Murata K. Ishikawa O. Ohgaki K. Kashimura N. Miyamoto M. Hirano S. Kondo S. Katoh H. Nakamura Y. Katagiri T. Genome-wide cDNA microarray analysis of gene expression profiles in pancreatic cancers using populations of tumor cells and normal ductal epithelial cells selected for purity by laser microdissection.Oncogene. 2004; 23: 2385-2400Crossref PubMed Scopus (224) Google ScholarSW:ACTG_HUMANActin, cytoplasmic 2aDifferentially expressed in 2 cancer samples. When a protein was differentially expressed in both cancer samples, the ratio presented here was the average from both samples.2.602.08SW:CAP1_HUMANAdenylyl cyclase–associated protein 1 (cap 1)aDifferentially expressed in 2 cancer samples. When a protein was differentially expressed in both cancer samples, the ratio presented here was the average from both samples.2.200.25SW:AKAC_HUMANA-kinase anchor protein 123.090.07SW:ADHB_HUMANAlcohol dehydrogenase β chain2.360.13SW:AMBP_HUMANAMBP protein precursor2.720.22SW:ANX4_HUMANAnnexin IV (lipocortin IV) (endonexin I)2.923.51Yes24Nakamura T. Furukawa Y. Nakagawa H. Tsunoda T. Ohigashi H. Murata K. Ishikawa O. Ohgaki K. Kashimura N. Miyamoto M. Hirano S. Kondo S. Katoh H. Nakamura Y. Katagiri T. Genome-wide cDNA microarray analysis of gene expression profiles in pancreatic cancers using populations of tumor cells and normal ductal epithelial cells selected for purity by laser microdissection.Oncogene. 2004; 23: 2385-2400Crossref PubMed Scopus (224) Google ScholarSW:ANX1_HUMANAnnexin I (lipocortin I) (calpactin II)2.290.77Yes4Iacobuzio-Donahue C.A. Maitra A. Shen-Ong G.L. van Heek T. Ashfaq R. Meyer R. Walter K. Berg K. Hollingsworth M.A. Cameron J.L. Yeo C.J. Kern S.E. Goggins M. Hruban R.H. Discovery of novel tumor markers of pancreatic cancer using global gene expression technology.Am J Pathol. 2002; 160: 1239-1249Abstract Full Text Full Text PDF PubMed Scopus (279) Google Scholar, 5Iacobuzio-Donahue C.A. Maitra A. Olsen M. Lowe A.W. van Heek N.T. Rosty C. Walter K. Sato N. Parker A. Ashfaq R. Jaffee E. Ryu B. Jones J. Eshleman J.R. Yeo C.J. Cameron J.L. Kern S.E. Hruban R.H. Brown P.O. Goggins M. Exploration of global gene expression patterns in pancreatic adenocarcinoma using cDNA microarrays.Am J Pathol. 2003; 162: 1151-1162Abstract Full Text Full Text PDF PubMed Scopus (427) Google Scholar, 21Grutzmann R. Foerder M. Alldinger I. Staub E. Brummendorf T. Ropcke S. Li X. Kristiansen G. Jesnowski R. Sipos B. Lohr M. Luttges J. Ockert D. Kloppel G. Saeger H.D. Pilarsky C. Gene expression profiles of microdissected pancreatic ductal adenocarcinoma.Virchows Arch. 2003; 443: 508-517Crossref PubMed Scopus (92) Google ScholarSW:ANX2_HUMANAnnexin II (lipocortin II) (calpactin I heavy chain)aDifferentially expressed in 2 cancer samples. When a protein was differentially expressed in both cancer samples, the ratio presented here was the average from both samples.2.680.61SW:APOD_HUMANApolipoprotein D precursor (APOD)2.410.52SW:BASI_HUMANExtracellular matrix metalloproteinase inducer2.540.69SW:APOH_HUMANβ2-glycoprotein I precursor (apolipoprotein H)aDifferentially expressed in 2 cancer samples. When a protein was differentially expressed in both cancer samples, the ratio presented here was the average from both samples.1.980.70SW:B2MG_HUMANβ2-microglobulin precursor4.030.78SW:PGS2_HUMANBone/cartilage proteoglycan I precursor (biglycan)aDifferentially expressed in 2 cancer samples. When a protein was differentially expressed in both cancer samples, the ratio presented here was the average from both samples.2.590.50Yes4Iacobuzio-Donahue C.A. Maitra A. Shen-Ong G.L. van Heek T. Ashfaq R. Meyer R. Walter K. Berg K. Hollingsworth M.A. Cameron J.L. Yeo C.J. Kern S.E. Goggins M. Hruban R.H. Discovery of novel tumor markers of pancreatic cancer using global gene expression technology.Am J Pathol. 2002; 160: 1239-1249Abstract Full Text Full Text PDF PubMed Scopus (279) Google ScholarSW:CATB_HUMANCathepsin B precursor3.022.86SW:CATD_HUMANCathepsin D precursoraDifferentially expressed in 2 cancer samples. When a protein was differentially expressed in both cancer samples, the ratio presented here was the average from both samples.3.442.13SW:CBF_HUMANCCAAT-box-binding transcription factor2.040.01SW:C166_HUMANCD166 antigen precursor2.540.15PIR1:KUHUCeruloplasmin precursor (ec 1.16.3.1) (ferroxidase)3.381.77SW:CLI4_HUMANChloride intracellular channel protein 43.680.16SWN:COAC_HUMANCoactosin-like protein2.201.62SW:COF1_HUMANCofilin2.051.14Yes22Holzmann K. Kohlhammer H. Schwaenen C. Wessendorf S. Kestler H.A. Schwoerer A. Rau B. Radlwimmer B. Dohner H. Lichter P. Gress T. Bentz M. Genomic DNA-chip hybridization reveals a higher incidence of genomic amplifications in pancreatic cancer than conventional comparative genomic hybridization and leads to the identification of novel candidate genes.Cancer Res. 2004; 64: 4428-4433Crossref PubMed Scopus (134) Google Scholar, 24Nakamura T. Furukawa Y. Nakagawa H. Tsunoda T. Ohigashi H. Murata K. Ishikawa O. Ohgaki K. Kashimura N. Miyamoto M. Hirano S. Kondo S. Katoh H. Nakamura Y. Katagiri T. Genome-wide cDNA microarray analysis of gene expression profiles in pancreatic cancers using populations of tumor cells and normal ductal epithelial cells selected for purity by laser microdissection.Oncogene. 2004; 23: 2385-2400Crossref PubMed Scopus (224) Google ScholarSW:CA34_HUMANCollagen α 3 (IV) chain precursor2.500.46SW:CFAB_HUMANComplement factor B precursor2.080.33Yes19Aguirre A.J. Brennan C. Bailey G. Sinha R. Feng B. Leo C. Zhang Y. Zhang J. Gans J.D. Bardeesy N. Cauwels C. Cordon-Cardo C. Redston M.S. DePinho R.A. Chin L. High-resolution characterization of the pancreatic adenocarcinoma genome.Proc Natl Acad Sci U S A. 2004; 101: 9067-9072Crossref PubMed Scopus (236) Google ScholarSW:CYSR_HUMANCysteine-rich protein 12.860.88Yes24Nakamura T. Furukawa Y. Nakagawa H. Tsunoda T. Ohigashi H. Murata K. Ishikawa O. Ohgaki K. Kashimura N. Miyamoto M. Hirano S. Kondo S. Katoh H. Nakamura Y. Katagiri T. Genome-wide cDNA microarray analysis of gene expression profiles in pancreatic cancers using populations of tumor cells and normal ductal epithelial cells selected for purity by laser microdissection.Oncogene. 2004; 23: 2385-2400Crossref PubMed Scopus (224) Google ScholarSW:CRP2_HUMANCysteine-rich protein 2 (crp2)2.270.54SW:ABP2_HUMANEndothelial actin-binding proteinaDifferentially expressed in 2 cancer samples. When a protein was differentially expressed in both cancer samples, the ratio presented here was the average from both samples.2.401.59SW:FBL4_HUMANEpidermal growth factor–containing fibulin-like extracellular matrix protein 1aDifferentially expressed in 2 cancer samples. When a protein was differentially expressed in both cancer samples, the ratio presented here was the average from both samples.2.320.21Yes22Holzmann K. Kohlhammer H. Schwaenen C. Wessendorf S. Kestler H.A. Schwoerer A. Rau B. Radlwimmer B. Dohner H. Lichter P. Gress T. Bentz M. Genomic DNA-chip hybridization reveals a higher incidence of genomic amplifications in pancreatic cancer than conventional comparative genomic hybridization and leads to the identification of novel candidate genes.Cancer Res. 2004; 64: 4428-4433Crossref PubMed Scopus (134) Google ScholarGP:AF123887_TEROIL2.441.78SW:ECM1_HUMANExtracellular matrix protein 1 precursor2.160.20SW:FSC1_HUMANFascin, actin bundling protein2.570.70Yes24Nakamura T. Furukawa Y. Nakagawa H. Tsunoda T. Ohigashi H. Murata K. Ishikawa O. Ohgaki K. Kashimura N. Miyamoto M. Hirano S. Kondo S. Katoh H. Nakamura Y. Katagiri T. Genome-wide cDNA microarray analysis of gene expression profiles in pancreatic cancers using populations of tumor cells and normal ductal epithelial cells selected for purity by laser microdissection.Oncogene. 2004; 23: 2385-2400Crossref PubMed Scopus (224) Google ScholarSW:FHN1_HUMANFibrillin 1 precursoraDifferentially expressed in 2 cancer samples. When a protein was differentially expressed in both cancer samples, the ratio presented here was the average from both samples.2.160.17SW:FIBB_HUMANFibrinogen β chain precursor2.271.73SW:FIBG_HUMANFibrinogen γ chain precursoraDifferentially expressed in 2 cancer samples. When a protein was differentially expressed in both cancer samples, the ratio presented here was the average from both samples.2.360.44SW:FINC_HUMANFibronectin precursoraDifferentially expressed in 2 cancer samples. When a protein was differentially expressed in both cancer samples, the ratio presented here was the average from both samples.2.400.70Yes4Iacobuzio-Donahue C.A. Maitra A. Shen-Ong G.L. van Heek T. Ashfaq R. Meyer R. Walter K. Berg K. Hollingsworth M.A. Cameron J.L. Yeo C.J. Kern S.E. Goggins M. Hruban R.H. Discovery of novel tumor markers of pancreatic cancer using global gene expression technology.Am J Pathol. 2002; 160: 1239-1249Abstract Full Text Full Text PDF PubMed Scopus (279) Google ScholarSW:FBL1_HUMANFibulin 1 precursor2.630.38SW:FLNA_HUMANFilamin A (α-filamin)2.940.80SW:LEG1_HUMANGalectin-1 (β-galactoside–binding lectin 1-14-I)aDifferentially expressed in 2 cancer samples. When a protein was differentially expressed in both cancer samples, the ratio presented here was the average from both samples.2.572.09Yes6Logsdon C.D. Simeone D.M. Binkley C. Arumugam T. Greenson J.K. Giordano T.J. Misek D.E. Hanash S. Molecular profiling of pancreatic adenocarcinoma and chronic pancreatitis identifies multiple genes differentially regulated in pancreatic cancer.Cancer Res. 2003; 63: 2649-2657PubMed Google ScholarSW:GELS_HUMANGelsolin precursor2.380.65Yes4Iacobuzio-Donahue C.A. Maitra A. Shen-Ong G.L. van Heek T. Ashfaq R. Meyer R. Walter K. Berg K. Hollingsworth M.A. Cameron J.L. Yeo C.J. Kern S.E. Goggins M. Hruban R.H. Discovery of novel tumor markers of pancreatic cancer using global gene expression technology.Am J Pathol. 2002; 160: 1239-1249Abstract Full Text Full Text PDF PubMed Scopus (279) Google ScholarSW:TGM2_HUMANGlutamine γ-glutamyltransferase2.770.48SW:G3P2_HUMANGlyceraldehyde-3-phosphate dehydrogenase2.361.60Yes2Crnogorac-Jurcevic T. Efthimiou E. Nielsen T. Loader J. Terris B. Stamp G. Baron A. Scarpa A. Lemoine N.R. Expression profiling of microdissected pancreatic adenocarcinomas.Oncogene. 2002; 21: 4587-4594Crossref PubMed Scopus (193) Google Scholar, 22Holzmann K. K
Publication Year: 2005
Publication Date: 2005-10-01
Language: en
Type: letter
Indexed In: ['crossref', 'pubmed']
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