The human (PEDB) and mouse (mPEDB) Prostate Expression Databases

The Prostate Expression Databases (PEDB and mPEDB) are online resources designed to allow researchers to access and analyze gene expression information derived from the human and murine prostate, respectively. Human PEDB archives more than 84 000 Expressed Sequence Tags (ESTs) from 38 prostate cDNA libraries in a curated relational database that provides detailed library information including tissue source, library construction methods, sequence diversity and sequence abundance. The differential expression of each EST species can be viewed across all libraries using a Virtual Expression Analysis Tool (VEAT), a graphical user interface written in Java for intra- and inter-library sequence comparisons. Recent enhancements to PEDB include (i) the development of a murine prostate expression database, mPEDB, that complements the human gene expression information in PEDB, (ii) the assembly of a non-redundant sequence set or ‘prostate unigene’ that represents the diversity of gene expression in the prostate, and (iii) an expanded search tool that supports both text-based and BLAST queries. PEDB and mPEDB are accessible via the World Wide Web at http://www.pedb.org and http://www.mpedb.org.     

READ: RIKEN Expression Array Database

READ, the RIKEN Expression Array Database, is a database of expression profile data from the RIKEN mouse cDNA microarray. It stores the microarray experimental data and information, and provides Web interfaces for researchers to use to retrieve, analyze and display their data. The goals for READ are to serve as a storage site for microarray data from ongoing research in the RIKEN mouse encyclopedia project and to provide useful links and tools to decipher biologically important information. The gene information is based mainly on the fully annotated FANTOM database. READ can be accessed at http://read.gsc.riken.go.jp/. READ also provides a search tool [READ integrates gene expression neighbor (RINGENE)] for genes with similarities in expression profiling.     

GEO(Gene Expression Omnibus ）:高通量基因表达数据库

 GEO(Gene Expression Omnibus)数据库包括高通量实验数据的广泛分类,有单通道和双通道以微阵列为基础的对mRNA丰度的测定;基因组DNA和蛋白质分子的实验数据;其中包括来自以非阵列为基础的高通量功能基因组学和蛋白质组学技术的数据也被存档,例如基因表达系列分析（serial analysis of gene expression,SAGE）和蛋白质鉴定技术.迄今为止,GEO数据库包含的数据含概10 000个杂交实验和来自30种不同生物体的SAGE库.本文概述了GEO数据库的查询和浏览,数据下载和格式,数据分析,贮存与更新,并着重分析GEO数据浏览器中控制词汇的使用,阐述了GEO数据库的数据挖掘以及GEO在分子生物学领域中的应用前景.GEO可由此公众网址直接登陆http://www.ncbi.nlm.nih.gov/projects/geo/.     

GXD: a gene expression database for the laboratory mouse. The Gene Expression Database Group.

The Gene Expression Database (GXD) is a community resource that stores and integrates expression information for the laboratory mouse, with a particular emphasis on mouse development, and makes these data freely available in formats appropriate for comprehensive analysis. GXD is implemented as a relational database and integrated with the Mouse Genome Database (MGD) to enable global analysis of genotype, expression and phenotype information. Interconnections with sequence databases and with databases from other species further extend GXD's utility for the analysis of gene expression data. GXD is available through the Mouse Genome Informatics Web Site at http://www.informatics.jax.org/     

Evaluation of NUF2 and GMNN Expression in Prostate Cancer: Potential Biomarkers for Prostate Cancer Screening

Background:Prostate cancer (PC) is one of the most abundant cancers among men, and In Iran, has been responsible for 6% of all deaths from cancer in men. NUF2 and GMNN genes are considered as loci of susceptibility to tumorigenesis in humans. Alterations in expression of these genes have been reported in various malignancies. The aim of our study was to test whether different NUF2 and GMNN expression levels are associated with PC incidence and hence, might be considered as new molecular tools for PC screening.Methods:Biopsy samples from 40 PC patients and 41 healthy Iranian men were used to determine the relative gene expression. After RNA extraction and cDNA synthesis, samples were analyzed using TaqMan Quantitative Real time PCR. Patients’ background information, included smoking habits and family histories of PC, were recorded. Stages and grades of their PC were classified by the TNM tumor, node, metastasis (TMN) staging system based on standard guidelines.Results:NUF2 expression did not significantly differ between the groups, while GMNN expression was significantly greater in the PC specimens than in the controls.Conclusion:Regarding the significant role of GMNN in various tumor phenotypes, and its importance in PC progression, the alteration in GMNN expression in PC samples vs. controls indicate that the genetic profiling of this cancer might be considered to personalize therapy for each patient in the future.Key Words: Family history, Geminin (GMNN), Tumor staging, NUF2, Prostate cancer     

GDB: the Human Genome Database.

The Genome Database (GDB, http://www.gdb.org ) is a public repository of data on human genes, clones, STSs, polymorphisms and maps. GDB entries are highly cross-linked to each other, to literature citations and to entries in other databases, including the sequence databases, OMIM, and the Mouse Genome Database. Mapping data from large genome centers and smaller mapping efforts are added to GDB on an ongoing basis. The database can be searched by a variety of methods, ranging from keyword searches to complex queries. Major functionality extensions in the last year include the ongoing computation of integrated human genome maps, called Comprehensive Maps, and the use of those maps to support positional queries and graphic displays. The capabilities of the GDB map viewer (Mapview) have been extended to include map printing and the graphical display of ad hoc query results. The HUGO Nomenclature Committee continues to curate the proposed and official gene symbols and related data in collaboration with GDB. As genome research shifts its emphasis from mapping to sequencing and functional analysis, the scope of the GDB schema is being extended. We are in the process of adding representations of gene function and expression, and improving our representation of human polymorphism and mutation.     

PTEN蛋白表达与前列腺癌病理分期的关系

目的：探讨PTEN蛋白在前列腺癌组织中的表达及其临床意义。方法：应用免疫组织化学S-P方法,检测前列腺癌及良性前列腺增生组织中PTEN蛋白的表达。结果：PTEN蛋白表达阳性随肿瘤细胞病理分级、临床分期的增高,PTEN蛋白阳性表达率降低。结论：PTEN蛋白异常表达在前列腺癌的进展中有重要作用,检测PTEN蛋白的表达有助于判断病情及预后。     

PTEN蛋白表达与前列腺癌病理分期的关系

目的:探讨PTEN蛋白在前列腺癌组织中的表达及其临床意义。方法:应用免疫组织化学S-P方法,检测前列腺癌及良性前列腺增生组织中PTEN蛋白的表达。结果:PTEN蛋白表达阳性随肿瘤细胞病理分级、临床分期的增高,PTEN蛋白阳性表达率降低。结论:PTEN蛋白异常表达在前列腺癌的进展中有重要作用,检测PTEN蛋白的表达有助于判断病情及预后。     

ALFRED: the ALelle FREquency Database. Update

Elaboration of ALFRED (http://alfred.med.yale.edu) is being continued in two directions. One of which is developing tools for efficiently annotating the entries and checking the integrity of the data already in the database while the other is to increase the quantity and accessibility of data. Information contained in ALFRED such as, polymorphic sites, number of populations and frequency tables (one sample typed for one site) has significantly increased.     

miRNA Expression Analyses in Prostate Cancer Clinical Tissues

A critical challenge in prostate cancer (PCa) clinical management is posed by the inadequacy of currently used biomarkers for disease screening, diagnosis, prognosis and treatment. In recent years, microRNAs (miRNAs) have emerged as promising alternate biomarkers for prostate cancer diagnosis and prognosis. However, the development of miRNAs as effective biomarkers for prostate cancer heavily relies on their accurate detection in clinical tissues. miRNA analyses in prostate cancer clinical specimens is often challenging owing to tumor heterogeneity, sampling errors, stromal contamination etc. The goal of this article is to describe a simplified workflow for miRNA analyses in archived FFPE or fresh frozen prostate cancer clinical specimens using a combination of quantitative real-time PCR (RT-PCR) and in situ hybridization (ISH). Within this workflow, we optimize the existing methodologies for miRNA extraction from FFPE and frozen prostate tissues and expression analyses by Taqman-probe based miRNA RT-PCR. In addition, we describe an optimized method for ISH analyses formiRNA detection in prostate tissues using locked nucleic acid (LNA)- based probes. Our optimized miRNA ISH protocol can be applied to prostate cancer tissue slides or prostate cancer tissue microarrays (TMA).