Proteomic Analysis of Chronic Pancreatitis and Pancreatic Adenocarcinoma
2005; Elsevier BV; Volume: 129; Issue: 5 Linguagem: Inglês
10.1053/j.gastro.2005.08.012
ISSN1528-0012
AutoresTatjana Crnogorac‐Jurcevic, Rathi Gangeswaran, Vipul Bhakta, Gabriele Capurso, Samuel Lattimore, Masanori Akada, Makoto Sunamura, Wendy Prime, Fiona Campbell, Teresa A. Brentnall, Eithne Costello, John P. Neoptolemos, Nicholas R. Lemoine,
Tópico(s)Endoplasmic Reticulum Stress and Disease
ResumoBackground & Aims: Markers to differentiate among pancreatic adenocarcinoma, chronic pancreatitis, and normal pancreas would be of significant clinical utility. This study was therefore designed to analyze the proteome of such specimens and identify new candidate proteins for differential diagnosis. Methods: A PowerBlot analysis with more than 900 well-characterized antibodies was performed with tissue specimens from patients with chronic pancreatitis, pancreatic adenocarcinoma, and normal pancreas. Differential expression of selected proteins was confirmed on a larger scale by quantitative reverse transcription-polymerase chain reaction and immunohistochemistry using tissue arrays. Results: A total of 30 and 102 proteins showed significant deregulation between normal pancreas when compared with chronic pancreatitis and pancreatic adenocarcinoma, respectively, and although a substantial proportion were found similarly dysregulated in both chronic pancreatitis and pancreatic adenocarcinoma, several proteins were identified as potential disease-specific markers. Conclusions: A large number of proteins are differentially expressed in chronic pancreatitis and pancreatic adenocarcinoma compared with normal pancreas. Among these, expression analysis of UHRF1, ATP7A, and aldehyde oxidase 1 in combination could potentially provide a useful additional diagnostic tool for fine-needle aspirated or cytological specimens obtained during endoscopic investigations. Background & Aims: Markers to differentiate among pancreatic adenocarcinoma, chronic pancreatitis, and normal pancreas would be of significant clinical utility. This study was therefore designed to analyze the proteome of such specimens and identify new candidate proteins for differential diagnosis. Methods: A PowerBlot analysis with more than 900 well-characterized antibodies was performed with tissue specimens from patients with chronic pancreatitis, pancreatic adenocarcinoma, and normal pancreas. Differential expression of selected proteins was confirmed on a larger scale by quantitative reverse transcription-polymerase chain reaction and immunohistochemistry using tissue arrays. Results: A total of 30 and 102 proteins showed significant deregulation between normal pancreas when compared with chronic pancreatitis and pancreatic adenocarcinoma, respectively, and although a substantial proportion were found similarly dysregulated in both chronic pancreatitis and pancreatic adenocarcinoma, several proteins were identified as potential disease-specific markers. Conclusions: A large number of proteins are differentially expressed in chronic pancreatitis and pancreatic adenocarcinoma compared with normal pancreas. Among these, expression analysis of UHRF1, ATP7A, and aldehyde oxidase 1 in combination could potentially provide a useful additional diagnostic tool for fine-needle aspirated or cytological specimens obtained during endoscopic investigations. With its characteristically severe morbidity and almost unavoidable mortality, pancreatic ductal adenocarcinoma (PDAC) is a serious clinical problem in the Western world.1Jemal A. Murray T. Samuels A. Ghafoor A. Ward E. Thun M.J. Cancer statistics, 2003.CA Cancer J Clin. 2003; 53: 5-26Crossref PubMed Scopus (3394) Google Scholar In an effort to identify novel diagnostic and therapeutic targets, many investigators have been exploring the molecular basis of this malignancy, and various approaches for the analysis of the transcriptome have been extensively applied.2Logsdon C.D. Simeone D.M. 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Tainsky M.A. Docetaxel induced gene expression patterns in head and neck squamous cell carcinoma using cDNA microarray and PowerBlot.Clin Cancer Res. 2002; 8: 3910-3921PubMed Google Scholar Patients with chronic pancreatitis (CP) have an increased incidence of pancreatic cancer,19Malka D. Hammel P. Maire F. Rufat P. Madeira I. Pessione F. Levy P. Ruszniewski P. Risk of pancreatic adenocarcinoma in chronic pancreatitis.Gut. 2002; 51: 849-852Crossref PubMed Scopus (390) Google Scholar, 20Lowenfels A.B. Maisonneuve P. Cavallini G. Ammann R.W. Lankisch P.G. Andersen J.R. Dimagno E.P. Andren-Sandberg A. Domellof L. International Pancreatitis Study GroupPancreatitis and the risk of pancreatic cancer.N Engl J Med. 1993; 328: 1433-1437Crossref PubMed Scopus (1575) Google Scholar and it can be difficult to distinguish the 2 disease states clinically. We therefore also included CP specimens in our study. All pancreatic tissue specimens were obtained from the Human Biomaterials Resource Centre, Hammersmith Hospital Trust, London, and the Cancer Tissue Bank Research Centre in Liverpool. Eight pancreatic intraepithelial neoplasia specimens and 4 pancreatic liver metastases were kindly provided by Drs Teresa A. Brentnall (University of Washington, Seattle, WA) and Makoto Sunamura (Tohoku University, Japan), respectively. All specimens were obtained with full ethical approval from the host institutions. Tissue microarrays were obtained from the Cancer Tissue Bank Research Centre, Liverpool. BD PowerBlot Western array screening (http://www.bdbiosciences.com/pharmingen/products/product_features.php?key_products=26) was used to analyze 3 groups of lysates, each composed of 5 mg of pooled tissues from 5 normal pancreas (NP), CP, or PDAC samples. Clinical specimens in each disease group were selected to be histologically uniform, with a proportion of neoplastic cellularity in cancer from 50% to 80%. After sodium dodecyl sulfate-polyacrylamide gel electrophoresis and protein transfer, the filters were interrogated with around 900 primary antibodies. Alexa 680–conjugated secondary antibodies (Molecular Probes, Paisley, UK) were then applied. The signal was visualized with an Odyssey Infrared Imaging System and analyzed by densitometry by using PDQuest software (Bio-Rad). After normalization, the data for NP were compared with those for CP and PDAC; in addition, CP was compared with PDAC. The data were organized into levels of confidence with scores of 1–10, depending on the expression level and reproducibility and the intensity and the quality of the signal. All experiments were performed in triplicate. A detailed description of the immunoblotting procedure, a full list of antibodies interrogated, and data analysis are provided as supplementary data (http://sci.cancerresearchuk.org/axp/mphh/gastro05/index.html). Only the most stringent data with scores of 9 and 10 (1.5 higher overexpression or underexpression in all 9 comparisons from good-quality signals that passed visual inspection) were used in our selection of differentially expressed proteins. In addition, the statistical significance of the selected proteins was assessed by using the paired 2-tailed t test with a significance level cutoff set at P < .05. Total RNA was extracted with the TRIzol protocol (Gibco BRL), and complementary DNA was synthesized from 1 μg of total RNA by using random primers and the MultiScribe Reverse Transcription kit (Applied Biosystems, Warrington, Cheshire, UK). Quantitative reverse transcription-polymerase chain reaction primers used were as follows: ubiquitin-like, containing PHD and RING finger domains (UHRF1; 145–base pair amplicon) sense, 5′-GCCCGTTCCAGTTGTTCCT-3′; UHRF1 antisense, 5′-AACACCTGTGCCCGAAAGG-3′; ATPase, copper transporting, alpha peptide (ATP7A) (128–base pair amplicon) sense, 5′-GATGATGAGCTGTGTGGCTTGA-3′; ATP7A antisense, 5′-GCTGTTTTACTGTTGTCTCCAGTCA-3′. Reactions containing 10 ng of complementary DNA, primers, and SYBR Green sequence-detection reagents (Applied Biosystems) were assayed on an ABI7700 sequence-detection system (Applied Biosystems), and PCR product measured in real time as an increase in SYBR Green fluorescence. Data were analyzed by using the Sequence Detector program v1.9.1 (Applied Biosystems). All the assays were performed in triplicate and normalized with 18S endogenous control. Quantitative gene expression was compared with the average value of all normal samples set arbitrarily at 1. The analysis was performed by using a pancreatic cancer–specific tissue microarray with 180 cores, of which 110 were PDAC (55 cases spotted in duplicate). The remaining were normal pancreatic (30 individual cases in duplicate), kidney, and lung cores. Full clinical history (age, sex, tumor size, resection margins, grade, TNM stage, lymph nodes, perineural or vascular invasion, and survival data) was provided. Immunohistochemistry was also performed on CP tissue microarray containing cores representing 24 normal and 24 CP specimens with normal tissue controls as on pancreatic ductal adenocarcinoma tissue microarray. Immunostaining was performed with anti-UHRF1 and anti–aldehyde oxidase 1 (AOX1) monoclonal antibodies, both diluted 1:200 (all from BD Transduction Laboratory, Cowley, Oxford, UK) by using a Ventana Discovery System (Ventana, Tucson, AZ) according to the protocols provided (http://www.ventanadiscovery.com). UHRF1 was considered positive if immunoreactivity was present in at least 5% of the cell nuclei, and AOX1 was scored as absent, weak, or strong. To assess the significance of staining patterns among CP, PDAC, and NP and to explore the associations between the immunoreactivity in tumor samples and clinical data, the Fisher exact test and logistic regression were used, respectively. The analysis was performed within the R statistical environment on a Linux platform. Table 1 shows that 30 proteins were significantly deregulated in comparison between CP and NP, whereas 102 (54 up-regulated and 48 down-regulated) proteins were differentially expressed between PDAC and NP (Table 2). It also shows that only 27 of all deregulated proteins were previously disclosed in earlier studies on PDAC.2Logsdon C.D. Simeone D.M. Binkley C. Arumugam T. Greenson J.K. Giordano T.J. Misek D.E. Kuick R. Hanash S. 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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) Bruton agammaglobulinemia tyrosine kinaseBTK6.28bP < .05.Cytoplasmic or membrane associated/tyrosine kinase activity, induction of apoptosis by extracellular signalsNo A-RafARAF14.4bP < .05.Cytosolic/proto-oncogene; serine/threonine kinaseNo C-jun/JNK (pT183/pY185) Phosphospecific (54 kilodaltons)MAPK84.19bP < .05.Cytoplasmic/activated by environmental stress and pro-inflammatory cytokinesYes (2Logsdon C.D. Simeone D.M. Binkley C. Arumugam T. Greenson J.K. Giordano T.J. Misek D.E. Kuick R. 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, 22Han 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) TGF-β–inducible early growth response protein 2TIEG23.13bP < .05.Nuclear/Kruppel-like transcription factor of Spl family, p18; translation regulatory activityNo Tubulin, α1TUBA13.1bP < .05.Cytoplasmic/major constituent of microtubulesYes (2Logsdon C.D. Simeone D.M. Binkley C. Arumugam T. Greenson J.K. Giordano T.J. Misek D.E. Kuick R. 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) CofilinCFL12.44cP < .001.Cytoskeletal and nuclear/actin polymerisation; Rho signalingNo CeruloplasminCP2.31bP < .05.Plasma membrane bound/copper-binding glycoproteinNo Argininosuccinate synthetaseASS2.26bP < .05.Cytoplasmic/synthetase activityNoDown-regulated proteins (n = 15) Major vault protein (LRP)MVP−7.56bP < .05.Cytoplasmic/nuclear/overexpressed in multidrug-resistant cancerNo Golgi SNARE (15 kilodalton protein)BETL1−7.45bP < .05.Cytoplasmic/blocked early in transport 1 homolog (S cerevisiae)-likeNo Superoxide dismutase 2SOD2−7.02bP < .05.Mitochondrial matrix protein/destroys toxic free radicalsNo DoublecortinDCX−6.6bP < .05.Cytoplasmic/neurogenesis, microtubule binding activityNo Protein kinase C substratePRKCSH−5.38Intracellular/binds calcium; involved in protein kinase cascadeNo GRIM-19GRIM19−5.2bP < .05.Mitochondrial/NADH dehydrogenase activity, involved in interferon/retinoic acid induced cell deathNo Mitogen-activated protein kinase 1 (42 kilodaltons)MAPK1/ERK2−4.98bP < .05.Cytosolic/serine-threonine kinase activated by insulin and nerve growth factorNo Cell division cycle 42CDC42−3.25bP < .05.Plasma membrane/small GTPase; regulation of cell morphologyNo Protein kinase, cAMP dependent, type II, betaPKARIIb/PRKAR2B−3.13bP < .05.Nuclear/cAMP-dependent protein kinase type II β regulatory chainNo Ndr/serine/threonine kinase 38 (51 kilodaltons)Ndr/STK38−2.85bP < .05.Nuclear/serine-threonine kinase; regulation of cell morphogenesis and proliferationNo Epithelial protein lost in neoplasm (100 kilodaltons)EPLIN−2.7bP < .05.Cytoplasmic/inhibits actin filament depolymerization and cross-links filaments in bundlesNo BH3-interacting domain death agonistBID−2.5bP < .05.Cytoplasmic/proapoptotic proteinNo Protein-tyrosine phosphatase, nonreceptor type 6 (PTPN6)PTPIC/SHP1−2.34bP < .05.Cytoplasmic/protein tyrosine phosphatase activityNo Receptor-interacting serine threonine kinase 2RIPK2/RICK−2.19bP < .05.Cytoplasmic/receptor-interacting serine-threonine kinase 2, potentiates caspase 8–mediated apoptosisNo Peroxisomal dodecenoyl-CoA isomerasePECI−2.05bP < .05.Peroxisomal/binds Acyl-CoANoNOTE. Numbers in parentheses are the numbers of the cited references.NC, not calculable (number/0); PKA, protein kinase A; CAMP, cyclic adenosine monophosphate; NADH, reduced nicotinamide adenine dinucleotide; GTPase, guanosine triphosphatase.a Minus sign indicates down-regulation.b P < .05.c P < .001. Open table in a new tab Table 2Differentially Expressed Proteins Between Pancreatic Cancer (PDAC) and Normal Pancreas (NP) and Chronic Pancreatitis (CP)ProteinHUGO nameAverage-fold change, PDAC vs NPAverage-fold change, PDAC vs CPLocalization and functionPreviously reported in PDACUp-regulated proteins (n = 54) Adaptin dAP3D1NC1.08Cytoplasmic/protein and vesicle transporterNo Fatty acid synthaseFASNNC−3.47aP < .05.Cytoplasmic/catalyzes the formation of long-chain fatty acidsNo HIF-1αHIF1ANC1.29 NSNuclear/induction of oxygen-regulated genesYes (21Iacobuzio-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) Ubiquitin-like, containing PHD and RING finger domainsUHRF1/ICBP90NCNCNuclear/DNA-binding protein; regulates expression of topoisomerase alphaNo Menkes disease–associated proteinATP7A/MNKNCNCMembrane ATPase/transmembrane copper transportNo Plakophilin 3PKP3NC3.67aP < .05.Nuclear/associated with desmosomesNo Thrombospondin 2THBS2NCNCMembrane glycoprotein/mediates cell–cell and cell–matrix interactionsYes (6Friess H. Ding J. Kleeff J. Fenkell L. Rosinski J.A. Guweidhi A. Reidhaar-Olson J.F. Korc M. Hammer J. Buchler M.W. Microarray-based identification of differentially expressed growth- and metastasis-associated genes in pancreatic cancer.Cell Mol Life Sci. 2003; 60: 1180-1199PubMed Google Scholar, 21Iacobuzio-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, 36Goto M. Shinno H. Ichihara A. Isozyme patterns of branched-chain amino acid transaminase in human tissues and tumors.Gann. 1977; 68: 663-667PubMed Google Scholar) Kalinin B1 (Laminin β 3)LAMB343.73bP < .001.33.85aP < .05.Extracellular/attachment, cell migrationYes (2Logsdon C.D. Simeone D.M. Binkley C. Arumugam T. Greenson J.K. Giordano T.J. Misek D.E. Kuick R. Hanash S. Molecular profiling of pancreatic adenocarcinoma and chronic pancreatitis identifies multiple genes differentially regulated in pancreatic cancer.Cancer Res. 2003; 63
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