Pancreatic Expression Database Research Articles

DNA Methylation of PI3K/AKT Pathway-Related Genes Predicts Outcome in Patients with Pancreatic Cancer: A Comprehensive Bioinformatics-Based Study.

Simple SummaryPancreatic cancer is a highly lethal malignancy. Dysregulation of epigenetic mechanisms leads to abnormal patterns of gene expression contributing to the development and progression of cancer. We explored the ability of DNA methylation of PI3K-related genes to differentiate between malignant and healthy pancreatic tissue using distinct pancreatic cancer cohorts, and found that the methylation levels of the ITGA4, SFN, ITGA2, and PIK3R1 genes are altered in tumour samples since the early stages of malignant transformation and could serve as new diagnostic tools. We also demonstrate that these alterations correlate with overall survival and recurrence-free survival of the patients suggesting that its assessment can serve as independent prognostic indicators of patients’ survival with higher sensitivity and specificity than the currently implemented biomarkers. Therefore, the methylation profile of genes involved in this pathway may be an alternative method for predicting cell malignancy and help doctors’ decisions on patient care.Pancreatic cancer (PCA) is one of the most lethal malignancies worldwide with a 5-year survival rate of 9%. Despite the advances in the field, the need for an earlier detection and effective therapies is paramount. PCA high heterogeneity suggests that epigenetic alterations play a key role in tumour development. However, only few epigenetic biomarkers or therapeutic targets have been identified so far. Here we explored the potential of distinct DNA methylation signatures as biomarkers for early detection and prognosis of PCA. PI3K/AKT-related genes differentially expressed in PCA were identified using the Pancreatic Expression Database (n = 153). Methylation data from PCA patients was obtained from The Cancer Genome Atlas (n = 183), crossed with clinical data to evaluate the biomarker potential of the epigenetic signatures identified and validated in independent cohorts. The majority of selected genes presented higher expression and hypomethylation in tumour tissue. The methylation signatures of specific genes in the PI3K/AKT pathway could distinguish normal from malignant tissue at initial disease stages with AUC > 0.8, revealing their potential as PCA diagnostic tools. ITGA4, SFN, ITGA2, and PIK3R1 methylation levels could be independent prognostic indicators of patients’ survival. Methylation status of SFN and PIK3R1 were also associated with disease recurrence. Our study reveals that the methylation levels of PIK3/AKT genes involved in PCA could be used to diagnose and predict patients’ clinical outcome with high sensitivity and specificity. These results provide new evidence of the potential of epigenetic alterations as biomarkers for disease screening and management and highlight possible therapeutic targets.

SMAC, a computational system to link literature, biomedical and expression data

High-throughput technologies have produced a large amount of experimental and biomedical data creating an urgent need for comprehensive and automated mining approaches. To meet this need, we developed SMAC (SMart Automatic Classification method): a tool to extract, prioritise, integrate and analyse biomedical and molecular data according to user-defined terms. The robust ranking step performed on Medical Subject Headings (MeSH) ensures that papers are prioritised based on specific user requirements. SMAC then retrieves any related molecular data from the Gene Expression Omnibus and performs a wide range of bioinformatics analyses to extract biological insights. These features make SMAC a robust tool to explore the literature around any biomedical topic. SMAC can easily be customised/expanded and is distributed as a Docker container (https://hub.docker.com/r/hfx320/smac) ready-to-use on Windows, Mac and Linux OS. SMAC’s functionalities have already been adapted and integrated into the Breast Cancer Now Tissue Bank bioinformatics platform and the Pancreatic Expression Database.

The Pancreatic Expression Database: 2018 update.

The Pancreatic Expression Database (PED, http://www.pancreasexpression.org) continues to be a major resource for mining pancreatic –omics data a decade after its initial release. Here, we present recent updates to PED and describe its evolution into a comprehensive resource for extracting, analysing and integrating publicly available multi-omics datasets. A new analytical module has been implemented to run in parallel with the existing literature mining functions. This analytical module has been created using rich data content derived from pancreas-related specimens available through the major data repositories (GEO, ArrayExpress) and international initiatives (TCGA, GENIE, CCLE). Researchers have access to a host of functions to tailor analyses to meet their needs. Results are presented using interactive graphics that allow the molecular data to be visualized in a user-friendly manner. Furthermore, researchers are provided with the means to superimpose layers of molecular information to gain greater insight into alterations and the relationships between them. The literature-mining module has been improved with a redesigned web appearance, restructured query platforms and updated annotations. These updates to PED are in preparation for its integration with the Pancreatic Cancer Research Fund Tissue Bank (PCRFTB), a vital resource of pancreas cancer tissue for researchers to support and promote cutting-edge research.

The pancreatic expression database: recent extensions and updates

The Pancreatic Expression Database (PED, http://www.pancreasexpression.org) is the only device currently available for mining of pancreatic cancer literature data. It brings together the largest collection of multidimensional pancreatic data from the literature including genomic, proteomic, microRNA, methylomic and transcriptomic profiles. PED allows the user to ask specific questions on the observed levels of deregulation among a broad range of specimen/experimental types including healthy/patient tissue and body fluid specimens, cell lines and murine models as well as related treatments/drugs data. Here we provide an update to PED, which has been previously featured in the Database issue of this journal. Briefly, PED data content has been substantially increased and expanded to cover methylomics studies. We introduced an extensive controlled vocabulary that records specific details on the samples and added data from large-scale meta-analysis studies. The web interface has been improved/redesigned with a quick search option to rapidly extract information about a gene/protein of interest and an upload option allowing users to add their own data to PED. We added a user guide and implemented integrated graphical tools to overlay and visualize retrieved information. Interoperability with biomart-compatible data sets was significantly improved to allow integrative queries with pancreatic cancer data.

BioMart: driving a paradigm change in biological data management

BioMart: driving a paradigm change in biological data management

Using BioMart as a framework to manage and query pancreatic cancer data

We describe the Pancreatic Expression Database (PED), the first cancer database originally designed based on the BioMart infrastructure. The PED portal brings together multidimensional pancreatic cancer data from the literature including genomic, proteomic, miRNA and gene expression profiles. Based on the BioMart 0.7 framework, the database is easily integrated with other BioMart-compliant resources, such as Ensembl and Reactome, to give access to a wide range of annotations alongside detailed experimental conditions. This article is intended to give an overview of PED, describe its data content and work through examples of how to successfully mine and integrate pancreatic cancer data sets and other BioMart resources.Database URL: http://www.pancreasexpression.org

Abstract 51: International Cancer Genome Consortium Data Portal – A ‘one-stop-shop’ for genomic, transcriptomic, and epigenomic data

Abstract The International Cancer Genome Consortium (ICGC) (www.icgc.org) orchestrates a multi-national effort to catalogue genomic abnormalities in 50 different tumor types. For each type 500 pairs of matched tumor and normal tissues will be studied using multiple technologies including DNA sequencing, expression profiling, and epigenetic analysis. ICGC data will be linked to cancer etiology, drug response, and patient survival. Each ICGC member institute generates its site-specific data independently and stores it in a local database. These databases are federated using BioMart technology (www.biomart.org) and made available to the public through the ICGC data portal (dcc.icgc.org). To the user, the multiple databases appear as a single integrated database. The ICGC data portal provides a platform for scientists to search, download, and analyze a broad range of cancer data. In addition to the data generated by the ICGC members, the portal includes data from other projects such as The Cancer Genome Atlas (TCGA) and Tumor Sequencing Project (TSP). Currently, the portal contains data sets from 15 cancer types. Data sets are divided into two categories: open access and controlled access. Open access data sets are available to the entire research community and include somatic mutations (simple, copy number variations, and structural rearrangements), gene and miRNA expression, DNA methylation, and exon junctions. Controlled access data sets, such as germline mutations, are available to users who have been authorized according to ICGC policies. The portal also includes annotation data from other publically available databases such as Ensembl, KEGG, Reactome, and the Pancreatic Expression Database. All data in the portal is exposed through a variety of user interfaces and can be processed using available analytical and visualization tools. For instance, users can perform gene- or pathway-centric analyses, such as compare mutation frequencies in specific genes or pathways, or compare mutation patterns between multiple tumor samples. Furthermore, by selecting diverse query and output criteria, users can retrieve data based on different molecular and clinical covariates. The portal enables users to conveniently retrieve, characterize, and compare cancer data from different sources through a single point of access. As more ICGC data is added and portal's functionality is further enhanced, it is expected that it will become an increasingly important resource for cancer researchers with diverse user requirements. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 51. doi:10.1158/1538-7445.AM2011-51

The Pancreatic Expression database: 2011 update

The Pancreatic Expression database (PED, http://www.pancreasexpression.org) has established itself as the main repository for pancreatic-derived -omics data. For the past 3 years, its data content and access have increased substantially. Here we describe several of its new and improved features, such as data content, which now includes over 60 000 measurements derived from transcriptomics, proteomics, genomics and miRNA profiles from various pancreas-centred reports on a broad range of specimen and experimental types. We also illustrate the capabilities of its interface, which allows integrative queries that can combine PED data with a growing number of biological resources such as NCBI, Ensembl, UniProt and Reactome. Thus, PED is capable of retrieving and integrating different types of -omics, annotations and clinical data. We also focus on the importance of data sharing and interoperability in the cancer field, and the integration of PED into the International Cancer Genome Consortium (ICGC) data portal.

A Web-Based Platform for Mining Pancreatic Expression Datasets

Background and Aims: We have recently constructed the pancreatic expression database (http://www.pancreasex-pression.org/). It is a freely available web-based tool that currently holds 7,636 gene expression measurements from various pancreatic cancer types, precursor lesions (PanINs) and chronic pancreatitis. It was constructed with the aim of enabling mining of the large body of publicly available genomic/proteomic data in the pancreatic field and thus furthering our understanding of the complex basis of pancreatic cancer within the realm of systems biology. Methods and Results: Annotated, integrated pancreatic datasets were stored in a data management system based on the BioMart technology alongside public annotations. Within our first database report we incorporated 32 datasets and have also included several easy examples of its usage. We now provide further, more sophisticated query types that we hope will serve as a prototype for possible questions of interest that might be addressed in the pancreatic biology community. Conclusion: The systematic understanding of the pathobiology underlying pancreatic cancer initiation and progression is a prerequisite for devising timely diag- nostic tools and successful drug target discovery. As the database is only as comprehensive as its contents and as user-friendly as its query engine, we invite the pancreatic biology community for feedback and submission of further data-sets.

Genome-Wide Analysis of Pancreatic Cancer Using Microarray-Based Techniques

Background/Aims: Microarray-based comparative genomic hybridisation (CGH) has allowed high-resolution analysis of DNA copy number alterations across the entire cancer genome. Recent advances in bioinformatics tools enable us to perform a robust and highly sensitive analysis of array CGH data and facilitate the discovery of novel cancer-related genes. Methods: We analysed a total of 29 pancreatic ductal adenocarcinoma (PDAC) samples (6 cell lines and 23 microdissected tissue specimens) using 1-Mb-spaced CGH arrays. The transcript levels of all genes within the identified regions of genetic alterations were then screened using our Pancreatic Expression Database. Results: In addition to 238 high-level amplifications and 35 homozygous deletions, we identified 315 minimal common regions of ‘non-random’ genetic alterations (115 gains and 200 losses) which were consistently observed across our tumour samples. The small size of these aberrations (median size of 880 kb) contributed to the reduced number of candidate genes included (on average 12 Ensembl-annotated genes). The database has further specified the genes whose expression levels are consistent with their copy number status. Such genes were UQCRB, SQLE, DDEF1, SLA, ERICH1 and DLC1, indicating thatthese may be potential target candidates within regions of aberrations. Conclusion: This study has revealed multiple novel regions that may indicate the locations of oncogenes or tumour suppressor genes in PDAC. Using the database, we provide a list of novel target genes whose altered DNA copy numbers could lead to significant changes in transcript levels in PDAC.

Pancreatic Expression database: a generic model for the organization, integration and mining of complex cancer datasets

BackgroundPancreatic cancer is the 5th leading cause of cancer death in both males and females. In recent years, a wealth of gene and protein expression studies have been published broadening our understanding of pancreatic cancer biology. Due to the explosive growth in publicly available data from multiple different sources it is becoming increasingly difficult for individual researchers to integrate these into their current research programmes. The Pancreatic Expression database, a generic web-based system, is aiming to close this gap by providing the research community with an open access tool, not only to mine currently available pancreatic cancer data sets but also to include their own data in the database.DescriptionCurrently, the database holds 32 datasets comprising 7636 gene expression measurements extracted from 20 different published gene or protein expression studies from various pancreatic cancer types, pancreatic precursor lesions (PanINs) and chronic pancreatitis. The pancreatic data are stored in a data management system based on the BioMart technology alongside the human genome gene and protein annotations, sequence, homologue, SNP and antibody data. Interrogation of the database can be achieved through both a web-based query interface and through web services using combined criteria from pancreatic (disease stages, regulation, differential expression, expression, platform technology, publication) and/or public data (antibodies, genomic region, gene-related accessions, ontology, expression patterns, multi-species comparisons, protein data, SNPs). Thus, our database enables connections between otherwise disparate data sources and allows relatively simple navigation between all data types and annotations.ConclusionThe database structure and content provides a powerful and high-speed data-mining tool for cancer research. It can be used for target discovery i.e. of biomarkers from body fluids, identification and analysis of genes associated with the progression of cancer, cross-platform meta-analysis, SNP selection for pancreatic cancer association studies, cancer gene promoter analysis as well as mining cancer ontology information. The data model is generic and can be easily extended and applied to other types of cancer. The database is available online with no restrictions for the scientific community at .