Molecular profiling of pancreatic adenocarcinoma and chronic pancreatitis identifies multiple genes differentially regulated in pancreatic cancer (Cancer Research (2003) (2649-2657))
Craig D. LogsdonDiane M. SimeoneCharles E. BinkleyThiruvengadam ArumugamJoel K. GreensonThomas J. GiordanoDavid E. MisekRork KuickSamir Hanash
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Pancreatic cancer accounts for 2.8% of new cancer cases worldwide and is projected to become the second leading cause of cancer-related deaths by 2030. Patients of African ancestry appear to be at an increased risk for pancreatic ductal adenocarcinoma (PDAC), with more severe disease and outcomes. The purpose of this study was to map the proteomic and genomic landscape of a cohort of PDAC patients of African ancestry. Thirty tissues (15 tumours and 15 normal adjacent tissues) were obtained from consenting South African PDAC patients. Optimisation of the sample preparation method allowed for the simultaneous extraction of high-purity protein and DNA for SWATH-MS and OncoArray SNV analyses. We quantified 3402 proteins with 49 upregulated and 35 downregulated proteins at a minimum 2.1 fold change and FDR adjusted p-value (q-value) ≤ 0.01 when comparing tumour to normal adjacent tissue. Many of the upregulated proteins in the tumour samples are involved in extracellular matrix formation (ECM) and related intracellular pathways. In addition, proteins such as EMIL1, KBTB2, and ZCCHV involved in the regulation of ECM proteins were observed to be dysregulated in pancreatic tumours. Downregulation of pathways involved in oxygen and carbon dioxide transport were observed. Genotype data showed missense mutations in some upregulated proteins, such as MYPN, ESTY2 and SERPINB8. Approximately 11% of the dysregulated proteins, including ISLR, BP1, PTK7 and OLFL3, were predicted to be secretory proteins. These findings help in further elucidating the biology of PDAC and may aid in identifying future plausible markers for the disease.
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Abstract Background: Pancreatic cancer has many pathologic types, among which pancreatic ductal adenocarcinoma (PDAC) is the most common one. Bioinformatics has become a very common tool for the selection of potentially pathogenic genes. Methods: Three data sets containing the gene expression profiles of PDAC were downloaded from the gene expression omnibus (GEO) database. The limma package of R language was utilized to explore the differentially expressed genes (DEGs). To analyze functions and signaling pathways, the Database Visualization and Integrated Discovery (DAVID) was used. To visualize the protein-protein interaction (PPI) of the DEGs ,Cytoscape was performed under the utilization of Search Tool for the Retrieval of Interacting Genes (STRING). With the usage of the plug-in cytoHubba in cytoscape software, the hub genes were found out. To verify the expression levels of hub genes, Gene Expression Profiling Interactive Analysis (GEPIA) was performed. Last but not least, UALCAN analysis online tool was implemented to analyze the overall survival. Results: The 376 DEGs were highly enriched in biological processes including signal transduction, apoptotic process and several pathways, mainly associated with Protein digestion and absorption and Pancreatic secretion pathway. The expression levels of nucleolar and spindle associated protein 1 ( NUSAP1 ) and SHC binding and spindle associated 1 ( SHCBP1 ) were discovered highly expressed in pancreatic ductal adenocarcinoma tissues. NUSAP1 and SHCBP1 had a high correlation with prognosis. Conclusions: The findings of this bioinformatics analysis indicate that NUSAP1 and SHCBP1 may be key factors in the prognosis and treatment of pancreatic cancer.
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Pancreatic cancer is a polygenic disease and the fourth leading cause of cancer-associated mortality worldwide; however, the tumorigenesis of pancreatic cancer remains poorly understood. Research at a molecular level, which includes the exploration of biomarkers for early diagnosis and specific targets for therapy, may effectively aid in the diagnosis of pancreatic cancer in its early stages and in the development of targeted molecular‑biological approaches for treatment, thus improving prognosis. By conducting expression profiling in para‑carcinoma, carcinoma and relapse of human pancreatic tissues, 319 genes or transcripts with differential expression levels >3‑fold between these tissue types were identified. Further analysis with Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes demonstrated that the translation, nucleus assembly processes and molecular functions associated with vitamin B6 and pyridoxal phosphate binding in pancreatic carcinoma were abnormal. Pancreatic cancer was additionally identified to be closely associated with certain autoimmune diseases, including type I diabetes mellitus and systemic lupus erythematosus.
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Abstract Exploring the underlying mechanisms of cancer development is useful for cancer treatment. In this paper, we analyzed the transcriptome profiles from the human normal pancreas, pancreatitis, pancreatic cancer and metastatic pancreatic cancer to study the intricate associations among pancreatic cancer progression. We clustered the transcriptome data, and analyzed the differential expressed genes. WGCNA was applied to construct co-expression networks and detect important modules. Importantly we selected the module in a different way. As the pancreatic disease deteriorates, the number of differentially expressed genes increases. The gene networks of T cells and interferon are upregulated in stages. In conclusion, the network-based study provides gradually activated gene networks in the disease progression of pancreatitis, pancreatic cancer, and metastatic pancreatic cancer. It may contribute to the rational design of anti-cancer drugs.
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Pancreatic cancer is a devastating disease. Most pancreatic cancers (approximately 85%) are diagnosed at a late, incurable stage. The poor prognosis and late presentation of pancreatic cancer patients underscore the importance of early detection, which is the sine qua non for the fight against pancreatic cancer. It is hoped for the future that the understanding of genetic alterations will lead to the rapid discovery of an effective biomarker of pancreatic carcinogenesis. In this thesis we visited the publicly available online SAGE libraries to evaluate global gene expression in pancreatic cancer and to select novel differentially expressed genes that might serve as diagnostic markers or as a lead for further research to therapeutic targets. We confirmed the differential expression of seven genes, involved in multiple cellular processes such as signal transduction (MIC-1), differentiation (DMBT1 and Neugrin), immune response (CD74), inflammation (CXCL2), cell cycle (CEB1) and enzymatic activity (Kallikrein 6). To provide an additional set of novel potential biomarkers for pancreatic ductal adenocarcinoma we used bioinformatics tools to reanalyze microarray data in the setting of pancreatic cancer. We characterized 60 previously unassigned ESTs (expressed sequence tags) and mapped most of them to known genes. The differential expression of a subset of genes was confirmed at the protein level by immunohistochemical labeling of tissue microarrays (Inhibin Beta A and CD29) and/or at the transcript level by RT-PCR (Inhibin Beta A, AKAP12, ELK3, EIF5A2, and EFNA5). In addition, we studied the expression and prognostic significance of 14-3-3sigma and ERM family protein expression in pancreatic ductal adenocarcinomas. The protein expression was significantly more common in poorly differentiated pancreatic cancers. Moreover, we showed that pancreatic cancer is a promising cancer type to explore novel chemotherapeutic strategies to exploit the selective loss of MTAP function. We found that immunolabeling for the MTAP gene product mirrored gene status and that approximately 30% of infiltrating pancreatic adenocarcinomas had complete loss of MTAP expression. In the future, those patients whose cancers show a complete loss of MTAP expression could be offered treatment with inhibitors of the de novo purine synthesis pathway. In addition, we immunolabeled a series of pancreatic intraepithelial neoplasia (PanIN) lesions of various histologic grades for the p16 and MTAP gene products using a high-throughput PanIN tissue microarray format. We demonstrated concordant loss of p16 and MTAP protein expression in 6/73 (8%) PanINs, including five high-grade lesions and one low-grade lesion. The concordant loss of expression of both genes in PanIN lesions demonstrated that homozygous deletions of the p16 tumor suppressor gene can occur in noninvasive precursor lesions. In summary, in this thesis we described potential clinically useful biomarkers for pancreatic carcinogenesis, discovered by analyzing unique cancer specific genetic alterations, differential expressed mRNA genes and protein changes in pancreatic cancer. Our increased knowledge of the molecular changes in pancreatic cancer and in different PanIN stages may provide the basis for developing more sensitive screening strategies for early detection, to achieve or final goal; “catching the horse before it has fled the barn”.
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Across the spectrum of cancer types, pancreatic cancer has one of the worst prognoses[1, 2]. Fewer than 10% of patients diagnosed with pancreatic cancer survive more than a year after diagnosis [2]. The research surrounding pancreatic cancer has predominantly focused on the mutations associated with the disease. In contrast, the changes in expression associated with pancreatic cancer have not been studied to the same depth. Studying the transcriptional changes associated with specific types of cancer has greatly benefited to our understanding of the disease and has identified subtypes of cancers with distinct prognostic signatures that are histologically indistinguishable. These studies have facilitated the development of accurate methods to diagnose the disease as well as therapeutic treatments tailored to the specific molecular changes associated with each subtype of cancer [3-10]. Here, we have used microarrays to characterise gene expression from primary pancreatic cancer samples from 117 individuals. This information was used to identify the genes differentially expressed between tumourous and normal tissue, predict the molecular subtype of each sample and confirm the prognostic importance of the different subtypes of pancreatic ductal adenocarcinoma. The methods used to characterise the primary tumour samples were applied to a panel of cell lines and identified the first cell lines belonging to the exocrine-like subtype. To capture the changes in expression not described by microarrays analysis, this work was supplemented by RNA-Seq. This technique allowed for a high resolution, study of expression in the tumorous and normal pancreatic tissue, from two patients. The single nucleotide resolution of this data made it possible to characterise expression at the level of genes and individual transcripts. This information was used to define differential gene expression, identify alternative transcript usage, and describe novel transcriptional events arising from known pancreatic cancer genes.
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