N-cadherin is differentially expressed in histological subtypes of papillary renal cell carcinoma

ABSTRACT: BACKGROUND: Papillary renal cell carcinoma (RCC) represents a rare tumor, which is divided, based on histological criteria, into two subtypes. In contrast to type I papillary RCC type II papillary RCC shows a worse prognosis. So far, reliable immunohistochemical markers for the distinction of these subtypes are not available. METHODS: In the present study the expression of N(neural)-, E(epithelial)-, P(placental)-, und KSP(kidney specific)-cadherin was examined in 22 papillary RCC of histological type I and 18 papillary RCC of histological type II (n = 40). RESULTS: All papillary RCC type II displayed a membranous expression for N-cadherin, whereas type I did not show any membranous positivity for N-cadherin. E-cadherin exhibited a stronger, but not significant, membranous as well as cytoplasmic expression in type II than in type I papillary RCC. A diagnostic relevant expression of P- and KSP-cadherin could not be demonstrated in both tumor entities. CONCLUSION: Thus N-cadherin represents the first immunhistochemical marker for a clear cut differentiation between papillary RCC type I and type II and could be a target for therapy and diagnostic in the future. Virtual slides The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/2011556982761733.     

Detecting multivariate differentially expressed genes

Background  

Gene expression is governed by complex networks, and differences in expression patterns between distinct biological conditions may therefore be complex and multivariate in nature. Yet, current statistical methods for detecting differential expression merely consider the univariate difference in expression level of each gene in isolation, thus potentially neglecting many genes of biological importance.     

差异表达基因的分离策略

生命活动的各个进程伴随着不同基因的选择性开启和关闭。如何有效地分离克隆各种差异表达的基因,成为分子生物学研究的一个努力方向,大量差异基因分离策略因之问世。本文扼要介绍了近年来发展的几种主要分离策略,并详细介绍一种新的差异基因分离方法--抑制差减杂交。     

Identification of differentially expressed genes related to metabolic syndrome induced with high-fat diet in E3 rats

Understanding the genes differentially expressing in aberrant organs of metabolic syndrome (MetS) facilitates the uncovering of molecular mechanisms and the identification of novel therapeutic targets for the disease. This study aimed to identify differentially expressed genes related to MetS in livers of E3 rats with high-fat-diet-induced metabolic syndrome (HFD-MetS). E3 rats were fed with high-fat diet for 24 weeks to induce MetS. Then, suppression subtractive hybridization (SSH) technology was used to identify the genes differentially expressed between HFD-MetS and control E3 rat livers. Twenty positive recombinant clones were chosen randomly from forward subtractive library and sent to sequence. BLAST analysis in GenBank database was used to determine the property of each cDNA fragment. In total, 11 annotated genes, 3 ESTs, and 2 novel gene fragments were identified by SSH technology. The expression of four genes (Alb, Pip4k2a, Scd1, and Tf) known to be associated with MetS and other five genes (Eif1, Rnase4, Rps12, Rup2, and Tmsb4) unknown to be relevant to MetS was significantly up-regulated in the livers of HFD-MetS E3 rats compared with control rats using real-time quantitative PCR (RT-qPCR). By analyzing the correlations between the expression of these nine genes and serum concentrations of TG, Tch, HDL-C, and LDL-C, we found that there were significant positive correlations between TG and the expression of five genes (Alb, Eif1, Pip4k2a, Rps12, and Tmsb4x), Tch and three genes (Rnase4, Scd1, and Tmsb4x), and LDL-C and two genes (Rnase4 and Scd1), as well there were significant negative correlations between HDL-C and the expression of three genes (Rup2, Scd1, and Tf). This study provides important clues for unraveling the molecular mechanisms of MetS.     

Ranking analysis for identifying differentially expressed genes

Microarrays allow researchers to examine the expression of thousands of genes simultaneously. However, identification of genes differentially expressed in microarray experiments is challenging. With an optimal test statistic, we rank genes and estimate a threshold above which genes are considered to be differentially expressed genes (DE). This overcomes the embarrassing shortcoming of many statistical methods to determine the cut-off values in ranking analysis. Experiments demonstrate that our method is a good performance and avoids the problems with graphical examination and multiple hypotheses testing that affect alternative approaches. Comparing to those well known methods, our method is more sensitive to data sets with small differentially expressed values and not biased in favor of data sets based on certain distribution models.     

Selecting differentially expressed genes from microarray experiments

High throughput technologies, such as gene expression arrays and protein mass spectrometry, allow one to simultaneously evaluate thousands of potential biomarkers that could distinguish different tissue types. Of particular interest here is distinguishing between cancerous and normal organ tissues. We consider statistical methods to rank genes (or proteins) in regards to differential expression between tissues. Various statistical measures are considered, and we argue that two measures related to the Receiver Operating Characteristic Curve are particularly suitable for this purpose. We also propose that sampling variability in the gene rankings be quantified, and suggest using the "selection probability function," the probability distribution of rankings for each gene. This is estimated via the bootstrap. A real dataset, derived from gene expression arrays of 23 normal and 30 ovarian cancer tissues, is analyzed. Simulation studies are also used to assess the relative performance of different statistical gene ranking measures and our quantification of sampling variability. Our approach leads naturally to a procedure for sample-size calculations, appropriate for exploratory studies that seek to identify differentially expressed genes.     

Statistical methods for ranking differentially expressed genes

In the analysis of microarray data the identification of differential expression is paramount. Here I outline a method for finding an optimal test statistic with which to rank genes with respect to differential expression. Tests of the method show that it allows generation of top gene lists that give few false positives and few false negatives. Estimation of the false-negative as well as the false-positive rate lies at the heart of the method.     

Finding differentially expressed genes for pattern generation

MOTIVATION: It is important to consider finding differentially expressed genes in a dataset of microarray experiments for pattern generation. RESULTS: We developed two methods which are mainly based on the q-values approach; the first is a direct extension of the q-values approach, while the second uses two approaches: q-values and maximum-likelihood. We present two algorithms for the second method, one for error minimization and the other for confidence bounding. Also, we show how the method called Patterns from Gene Expression (PaGE) (Grant et al., 2000) can benefit from q-values. Finally, we conducted some experiments to demonstrate the effectiveness of the proposed methods; experimental results on a selected dataset (BRCA1 vs BRCA2 tumor types) are provided. CONTACT: alhajj@cpsc.ucalgary.ca.     

Adaptive thresholds to detect differentially expressed genes in microarray data

To detect changes in gene expression data from microarrays, a fixed threshold for fold difference is used widely. However, it is not always guaranteed that a threshold value which is appropriate for highly expressed genes is suitable for lowly expressed genes. In this study, aiming at detecting truly differentially expressed genes from a wide expression range, we proposed an adaptive threshold method (AT). The adaptive thresholds, which have different values for different expression levels, are calculated based on two measurements under the same condition. The sensitivity, specificity and false discovery rate (FDR) of AT were investigated by simulations. The sensitivity and specificity under various noise conditions were greater than 89.7% and 99.32%, respectively. The FDR was smaller than 0.27. These results demonstrated the reliability of the method.     

Detecting differentially expressed genes by relative entropy

DNA microarray experiments have generated large amount of gene expression measurements across different conditions. One crucial step in the analysis of these data is to detect differentially expressed genes. Some parametric methods, including the two-sample t-test (T-test) and variations of it, have been used. Alternatively, a class of non-parametric algorithms, such as the Wilcoxon rank sum test (WRST), significance analysis of microarrays (SAM) of Tusher et al. (2001), the empirical Bayesian (EB) method of Efron et al. (2001), etc., have been proposed. Most available popular methods are based on t-statistic. Due to the quality of the statistic that they used to describe the difference between groups of data, there are situations when these methods are inefficient, especially when the data follows multi-modal distributions. For example, some genes may display different expression patterns in the same cell type, say, tumor or normal, to form some subtypes. Most available methods are likely to miss these genes. We developed a new non-parametric method for selecting differentially expressed genes by relative entropy, called SDEGRE, to detect differentially expressed genes by combining relative entropy and kernel density estimation, which can detect all types of differences between two groups of samples. The significance of whether a gene is differentially expressed or not can be estimated by resampling-based permutations. We illustrate our method on two data sets from Golub et al. (1999) and Alon et al. (1999). Comparing the results with those of the T-test, the WRST and the SAM, we identified novel differentially expressed genes which are of biological significance through previous biological studies while they were not detected by the other three methods. The results also show that the genes selected by SDEGRE have a better capability to distinguish the two cell types.     

Discovery of genes expressed in Hydra embryogenesis

Hydra's remarkable capacity to regenerate, to proliferate asexually by budding, and to form a pattern de novo from aggregates allows studying complex cellular and molecular processes typical for embryonic development. The underlying assumption is that patterning in adult hydra tissue relies on factors and genes which are active also during early embryogenesis. Previously, we reported that in Hydra the timing of expression of conserved regulatory genes, known to be involved in adult patterning, differs greatly in adults and embryos (Fr?bius, A.C., Genikhovich, G., Kürn, U., Anton-Erxleben, F. and Bosch, T.C.G., 2003. Expression of developmental genes during early embryogenesis of Hydra. Dev. Genes Evol. 213, 445-455). Here, we describe an unbiased screening strategy to identify genes that are relevant to Hydra vulgaris embryogenesis. The approach yielded two sets of differentially expressed genes: one set was expressed exclusively or nearly exclusively in the embryos, while the second set was upregulated in embryos in comparison to adult polyps. Many of the genes identified in hydra embryos had no matches in the database. Among the conserved genes upregulated in embryos is the Hydra orthologue of Embryonic Ectoderm Development (HyEED). The expression pattern of HyEED in developing embryos suggests that interstitial stem cells in Hydra originate in the endoderm. Importantly, the observations uncover previously unknown differences in genes expressed by embryos and polyps and indicate that not only the timing of expression of developmental genes but also the genetic context is different in Hydra embryos compared to adults.     

A recombinase-based selection of differentially expressed bacterial genes

Bacterial genes are often differentially expressed in response to specific environmental conditions. We have devised a method to identify regulated bacterial promoters, such that transient promoter expression leads to a permanent and selectable change in bacterial phenotype. This system consists of a promoterless derivative of cre, the phage P1 recombinase, carried on a plasmid, and two chromosomal loxP sites, the targets of the Cre recombinase. The loxP sites flank npt, conferring kanamycin resistance, and sacB, which confers sensitivity to sucrose, allowing positive selection for both the presence and absence of this chromosomal cassette. Fusion of active promoters to cre induces recombination of the loxP sites and deletion of intervening DNA, allowing selection on media containing sucrose, while inactive promoters fail to induce recombination and so remain resistant to kanamycin. We tested the system in Salmonella typhimurium using a known regulated promoter, that from the araBAD operon, and found it to be a sensitive indicator of gene expression over a wide range of promoter induction. We then used this system to identify S. typhimurium genes that are specifically expressed when bacteria interact with cultured epithelial cells and identified a novel DNA fragment, not found in E. coli, which might represent part of a new pathogenicity island.     

In search of differentially expressed genes and proteins

A great challenge for modern cell biology is the successful examination of the co-expression of thousands of genes under physiological or pathological conditions and how the expression patterns define the different states of a single cell, tissue or a microorganism. Gene expression can be analyzed today on a large scale by advanced technical approaches for differential screening of proteins and mRNAs. The identification of differentially expressed mRNAs has been successfully applied to understand gene function and the underlying molecular mechanism(-s) of differentiation, development and disease state. Analysis of gene expression by the systematic mapping of thousands of proteins present in a cell or tissue can be achieved by the use of two-dimensional (2D) gel electrophoresis, quantitative computer image analysis, and protein identification techniques. In this article, we comment on some of these techniques and try to stress their advantages and drawbacks. We show how data from RNA/DNA mapping, sequence information from genome projects and protein pattern profiling can be linked with each other and annotated. These comprehensive approaches permit the study of differential gene and protein expressions in cells or tissues.     

脱发相关差异表达基因的生物信息学分析

利用生物信息学方法分析脱发相关差异表达基因,有望帮助了解脱发发生发展的分子机制。本研究从NCBI的子数据库GEO中选择基因表达谱GSE45512和GSE45513数据集,利用R语言limma工具包,筛选出两个物种斑秃样本与正常样本的共同显著差异表达基因。对这部分基因进行功能注释和蛋白互作网络分析,同时对全部差异表达基因进行基因集富集分析。结果发现,人头皮斑秃样本共筛选出225个差异表达基因;C3H/HeJ小鼠自发斑秃皮肤样本共筛选出337个差异表达基因;两个物种的共同显著差异表达基因有23个。GO功能富集分析和蛋白互作网络分析显示,这部分差异基因显著富集于免疫相关功能,并且彼此间存在蛋白互作关系。基因集富集分析显示两个物种的差异基因都能显著富集到趋化因子信号通路、细胞因子受体相互作用、金葡菌感染及抗原加工与呈递通路;而且人的下调差异基因不仅映射到了人类表型数据库的脱发表型,也映射到皮肤附属物病理相关表型。综上所述,本研究通过生物信息方法分析脱发皮肤组织与正常皮肤组织的差异表达基因,最终筛选出23个在人和小鼠中共同存在的显著差异表达基因;此外,分析发现脱发与免疫过程及皮肤附属物病变密切相关,这些结果为脱发的诊断和治疗提供了新思路。