Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “Gene expression analysis”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 55 records · Page 3Linked to original sources
PubMed2002

Global gene expression analysis of gastric cancer by oligonucleotide microarrays.

To gain molecular understanding of carcinogenesis, progression, and diversity of gastric cancer, 22 primary human advanced gastric cancer tissues and 8 noncancerous gastric tissues were analyzed by high-density oligonucleotide microarray in this study. Based on expression analysis of approximately 6800 genes, a two-way clustering algorithm successfully distinguished cancer tissues from noncancerous tissues. Subsequently, genes that were differentially expressed in cancer and noncancerous tissues were identified; 162 and 129 genes were highly expressed (P < 0.05) >2.5-fold in cancer tissues and noncancerous tissues, respectively. In cancer tissues, genes related to cell cycle, growth factor, cell motility, cell adhesion, and matrix remodeling were highly expressed. In noncancerous tissues, genes related to gastrointestinal-specific function and immune response were highly expressed. Furthermore, we identified several genes associated with lymph node metastasis including Oct-2 or histological types including Liver-Intestine Cadherin. These results provide not only a new molecular basis for understanding biological properties of gastric cancer, but also useful resources for future development of therapeutic targets and diagnostic markers for gastric cancer.

Cluster Analysis↗
PubMed1999

Gene expression analysis by transcript profiling coupled to a gene database query.

We describe an mRNA profiling technique for determining differential gene expression that utilizes, but does not require, prior knowledge of gene sequences. This method permits high-throughput reproducible detection of most expressed sequences with a sensitivity of greater than 1 part in 100,000. Gene identification by database query of a restriction endonuclease fingerprint, confirmed by competitive PCR using gene-specific oligonucleotides, facilitates gene discovery by minimizing isolation procedures. This process, called GeneCalling, was validated by analysis of the gene expression profiles of normal and hypertrophic rat hearts following in vivo pressure overload.

Animals↗
PubMed2003

Software packages for quantitative microarray-based gene expression analysis.

Microarray technology enables researchers to investigate the expression of several thousand genes simultaneously. The whole transcriptional response of these genes in normal cells or tissue, in disease condition, as an response to biological, genetical or chemical stimuli or during normal biological processes such as cell cycle or embryonic development can be investigated. This leads to a huge amount of data, from which the relevant information has to be extracted by statistical and computational methods. Several software packages for the analysis of gene expression data are available, both commercially and freely. They differ particularly with regard to the implemented analytical methods, the graphical display and the manageability. In this paper the commercial software packages arraySCOUT, GeneSpring and Spotfire DecisionSite for Functional Genomics are compared and their applicability for analysis of gene expression data is studied. Small artificial and application test datasets are used to compare the computational results of the software packages. As far as possible results are verified with standard statistical software package SAS.

Algorithms↗
PubMed2004

An oncogenic KRAS2 expression signature identified by cross-species gene-expression analysis.

Using advanced gene targeting methods, generating mouse models of cancer that accurately reproduce the genetic alterations present in human tumors is now relatively straightforward. The challenge is to determine to what extent such models faithfully mimic human disease with respect to the underlying molecular mechanisms that accompany tumor progression. Here we describe a method for comparing mouse models of cancer with human tumors using gene-expression profiling. We applied this method to the analysis of a model of Kras2-mediated lung cancer and found a good relationship to human lung adenocarcinoma, thereby validating the model. Furthermore, we found that whereas a gene-expression signature of KRAS2 activation was not identifiable when analyzing human tumors with known KRAS2 mutation status alone, integrating mouse and human data uncovered a gene-expression signature of KRAS2 mutation in human lung cancer. We confirmed the importance of this signature by gene-expression analysis of short hairpin RNA-mediated inhibition of oncogenic Kras2. These experiments identified both a pattern of gene expression indicative of KRAS2 mutation and potential effectors of oncogenic KRAS2 activity in human cancer. This approach provides a strategy for using genomic analysis of animal models to probe human disease.

Adenocarcinoma↗
PubMed2002

Optimizing gene expression analysis in archival brain tissue.

Analysis of gene expression in the brain is a valuable tool to study the function of the brain under normal and pathological conditions. Although there are many techniques used to measure gene expression the validity of any such experiment is directly related to the quality of the RNA in the samples. The most readily available source of human brain tissue is post-mortem and while frozen tissue is sometimes available, most archived tissue is fixed and paraffin-embedded. The use of fixed tissue for expression analysis introduces variables, which must be considered in the experimental design. In addition, factors associated with clinical variability of the patient and with tissue procurement can affect RNA transcript levels. In order to illustrate the effects of two common tissue fixatives, formalin and ethanol, on the quality of RNA for expression analysis we compare RNA extracted from these fixed tissues to the gold standard, flash-frozen tissue. We describe RNA extraction from fixed tissue and ways to assess the quality or intactness of the RNA using reverse transcription combined with polymerase chain reaction amplification. An advantage of using archived tissue is the ease with which single cells or subpopulations of cells can be obtained by laser microdissection. The successful isolation of RNA from microdissected cells is also presented. From our results and a review of the literature we conclude that RNA from fixed tissues is a viable source of RNA for expression analysis which should enable new experimental approaches and discoveries as long as attention is given to variables that can affect RNA at all levels of analysis.

Archives↗
PubMed2005

Discovering causes and cures for cancer from gene expression analysis.

Tumorigenesis is governed by a series of complex genetic and epigenetic changes. Both mechanisms can result in either the silencing or aberrant expression of messages in a cell. Gene expression profiling techniques such as the serial analysis of gene expression (SAGE) or microarray analysis can provide global overviews of these changes, as well identify key genes and pathways involved in this process. This review outlines the current roles of these techniques in cancer research, and how they may contribute to finding not only mechanisms of this disease, but potential targets for therapy.

Biomarkers, Tumor↗
PubMed2004

Improving the sensitivity and specificity of gene expression analysis in highly related organisms through the use of electronic masks.

DNA microarrays are powerful tools for comparing gene expression profiles from closely related organisms. However, a single microarray design is frequently used in these studies. Therefore, the levels of certain transcripts can be grossly underestimated due to sequence differences between the transcripts and the arrayed DNA probes. Here, we seek to improve the sensitivity and specificity of oligonucleotide microarray-based gene expression analysis by using genomic sequence information to predict the hybridization efficiency of orthologous transcripts to a given microarray. To test our approach, we examine hybridization patterns from three Escherichia coli strains on E.coli K-12 MG1655 gene expression microarrays. We create electronic mask files to discard data from probes predicted to have poor hybridization sensitivity and specificity to cDNA targets from each strain. We increased the accuracy of gene expression analysis and identified genes that cannot be accurately interrogated in each strain using these microarrays. Overall, these studies provide guidelines for designing effective electronic masks for gene expression analysis in organisms where substantial genome sequence information is available.

DNA Probes↗
PubMed2005

Gene expression analysis of pancreatic cell lines reveals genes overexpressed in pancreatic cancer.

BACKGROUND: Pancreatic cancer is one of the leading causes of cancer-related death. Using DNA gene expression analysis based on a custom made Affymetrix cancer array, we investigated the expression pattern of both primary and established pancreatic carcinoma cell lines. METHODS: We analyzed the gene expression of 5 established pancreatic cancer cell lines (AsPC-1, BxPC-3, Capan-1, Capan-2 and HPAF II) and 5 primary isolates, 1 of them derived from benign pancreatic duct cells. RESULTS: Out of 1,540 genes which were expressed in at least 3 experiments, we found 122 genes upregulated and 18 downregulated in tumor cell lines compared to benign cells with a fold change >3. Several of the upregulated genes (like Prefoldin 5, ADAM9 and E-cadherin) have been associated with pancreatic cancer before. The other differentially regulated genes, however, play a so far unknown role in the course of human pancreatic carcinoma. By means of immunohistochemistry we could show that thymosin beta-10 (TMSB10), upregulated in tumor cell lines, is expressed in human pancreatic carcinoma, but not in non-neoplastic pancreatic tissue, suggesting a role for TMSB10 in the carcinogenesis of pancreatic carcinoma. CONCLUSION: Using gene expression profiling of pancreatic cell lines we were able to identify genes differentially expressed in pancreatic adenocarcinoma, which might contribute to pancreatic cancer development.

Adult↗
PubMed2006

Rosetta error model for gene expression analysis.

MOTIVATION: In microarray gene expression studies, the number of replicated microarrays is usually small because of cost and sample availability, resulting in unreliable variance estimation and thus unreliable statistical hypothesis tests. The unreliable variance estimation is further complicated by the fact that the technology-specific variance is intrinsically intensity-dependent. RESULTS: The Rosetta error model captures the variance-intensity relationship for various types of microarray technologies, such as single-color arrays and two-color arrays. This error model conservatively estimates intensity error and uses this value to stabilize the variance estimation. We present two commonly used error models: the intensity error-model for single-color microarrays and the ratio error model for two-color microarrays or ratios built from two single-color arrays. We present examples to demonstrate the strength of our error models in improving statistical power of microarray data analysis, particularly, in increasing expression detection sensitivity and specificity when the number of replicates is limited.

Algorithms↗
PubMed2006

A molecular signature of epithelial host defense: comparative gene expression analysis of cultured bronchial epithelial cells and keratinocytes.

BACKGROUND: Epithelia are barrier-forming tissues that protect the organism against external noxious stimuli. Despite the similarity in function of epithelia, only few common protective mechanisms that are employed by these tissues have been systematically studied. Comparative analysis of genome-wide expression profiles generated by means of Serial Analysis of Gene Expression (SAGE) is a powerful approach to yield further insight into epithelial host defense mechanisms. We performed an extensive comparative analysis of previously published SAGE data sets of two types of epithelial cells, namely bronchial epithelial cells and keratinocytes, in which the response to pro-inflammatory cytokines was assessed. These data sets were used to elucidate a common denominator in epithelial host defense. RESULTS: Bronchial epithelial cells and keratinocytes were found to have a high degree of overlap in gene expression. Using an in silico approach, an epithelial-specific molecular signature of gene expression was identified in bronchial epithelial cells and keratinocytes comprising of family members of keratins, small proline-rich proteins and proteinase inhibitors. Whereas some of the identified genes were known to be involved in inflammation, the majority of the signature represented genes that were previously not associated with host defense. Using polymerase chain reaction, presence of expression of selected tissue-specific genes was validated. CONCLUSION: Our comparative analysis of gene transcription reveals that bronchial epithelial cells and keratinocytes both express a subset of genes that is likely to be essential in epithelial barrier formation in these cell types. The expression of these genes is specific for bronchial epithelial cells and keratinocytes and is not seen in non-epithelial cells. We show that bronchial epithelial cells, similar to keratinocytes, express components that are able to form a cross-linked protein envelope that may contribute to an effective barrier against noxious stimuli and pathogens.

Algorithms↗
PubMed2006

Gene expression analysis using filter cDNA microarrays.

The analysis of gene expression patterns by filter-based complementary (c)DNA microarray remains an important technique in the molecular biology laboratory, despite the development of large-scale cDNA microarray analysis (see Chapter 27). This chapter provides an overview of the methods necessary to carry out the production of membrane-based cDNA arrays and the subsequent synthesis and hybridization of radiolabeled cDNA probes.

Animals↗
PubMed2004

[Prediction of response to neoadjuvant chemotherapy in Barrett's carcinoma by quantitative gene expression analysis].

The potential of gene expression profiles to predict the response to neoadjuvant chemotherapy in patients with advanced adenocarcinoma of the esophagus was analyzed. Paraffin-embedded endoscopic esophageal tumor biopsies of 38 patients with advanced esophageal adenocarcinoma (Barrett's adenocarcinoma) were included. All patients underwent two cycles of cisplatin and fluorouracil (5-FU) therapy with or without additional paclitaxel (taxol) followed by abdominothoracal esophagectomy. RNA expression levels of 5-FU-metabolism associated genes thymidylate synthase (TS), thymidine phosphorylase (TP), dihydropyrimidine dehydrogenase (DPD), methylenetetrahydrofolate reductase (MTHFR), MAP7, ELF3, as well as of platinum and taxane associated related genes caldesmon, excision cross-complementing genes (ERCC1 and ERCC4) HER2-neu, DNA damage-inducible gene 45 (GADD45) and multidrug resistance genes (MDR1, MRP1) were determined using real-time RT-PCR. Expression levels were correlated with the histopathological response to chemotherapy assessed in surgically resected specimens. Responding patients showed significantly higher pretherapeutic expression levels of MTHFR (p = 0.012), Caldesmon (p = 0.016), MRP1 (p = 0.007) and MDR1 (p = 0.025). In addition, patients with high pretherapeutic MTHFR and MRP1 levels had a survival benefit after surgery (p = 0.013 and p = 0.015, respectively). Additionally, intratumoral heterogeneity of gene expression of selected genes (TP, DPD, MTHFR, HER2-neu, Caldesmon, ERCC4, MRP1) was additionally verified in 9 untreated Barrett's adenocarcinoma by examination of 5 distinct tumor areas and was observed in 12.7% (5.6%-23.5%, CI 95%) of all cases analyzed. Our results indicate that determination of mRNA levels of a few genes may be useful for the prediction of the success of neoadjuvant chemotherapy in individual cancer patients with advanced adenocarcinoma of the esophagus.

Adenocarcinoma↗
PubMed2004

Gene expression analysis of the pro-oestrous-stage rat uterus reveals neuroligin 2 as a novel steroid-regulated gene.

In the present study, differential gene expression in the uteri of ovariectomised (OVX) and pro-oestrous rats (OVX v. pro-oestrus pair) was investigated using cDNA expression array analysis. Differential uterine gene expression in OVX rats and progesterone (P(4))-injected OVX rats (OVX v. OVX + P(4) pair) was also examined. The uterine gene expression profiles of these two sets of animals were also compared for the effects of P(4) treatment. RNA samples were extracted from uterine tissues and reverse transcribed in the presence of [alpha(32)P]-dATP. Membrane sets of rat arrays were hybridised with cDNA probe sets. Northern blot analysis was used to validate the relative gene expression patterns obtained from the cDNA array. Of the 1176 cDNAs examined, 23 genes showed significant (>two-fold) changes in expression in the OVX v. pro-oestrus pair. Twenty of these genes were upregulated during pro-oestrus compared with their expression in the OVX rat uterus. In the OVX v. OVX + P(4) pair, 22 genes showed significant (>two-fold) changes in gene expression. Twenty of these genes were upregulated in the OVX + P(4) animals. The genes for nuclear factor I-XI, afadin, neuroligin 2, semaphorin Z, calpain 4, cyclase-associated protein homologue, thymosin beta-4X and p8 were significantly upregulated in the uteri of the pro-oestrus and OVX + P(4) rats of both experimental pairs compared with the OVX rat uteri. These genes appear to be under the control of P(4). One of the most interesting findings of the present study is the unexpected and marked expression of the neuroligin 2 gene in the rat uterus. This gene is expressed at high levels in the central nervous system and acts as a nerve cell adhesion factor. According to Northern blot analysis, neuroligin 2 gene expression was higher during the pro-oestrus and metoestrus stages than during the oestrus and dioestrus stages of the oestrous cycle. In addition, neuroligin 2 mRNA levels were increased by both 17beta-oestradiol (E(2)) and P(4), although P(4) administration upregulated gene expression to a greater extent than injection of E(2). These results indicate that neuroligin 2 gene expression in the rat uterus is under the control of both E(2) and P(4), which are secreted periodically during the oestrous cycle.

Animals↗
PubMed2006

Gene Class expression: analysis tool of Gene Ontology terms with gene expression data.

Serial analysis of gene expression (SAGE) technology produces large sets of interesting genes that are difficult to analyze directly. Bioinformatics tools are needed to interpret the functional information in these gene sets. We present an interactive web-based tool, called Gene Class, which allows functional annotation of SAGE data using the Gene Ontology (GO) database. This tool performs searches in the GO database for each SAGE tag, making associations in the selected GO category for a level selected in the hierarchy. This system provides user-friendly data navigation and visualization for mapping SAGE data onto the gene ontology structure. This tool also provides graphical visualization of the percentage of SAGE tags in each GO category, along with confidence intervals and hypothesis testing.

Animals↗
PubMed1999

[Use of high-density filters ("DNA arrays") for gene expression analysis in human tissues].

Most technical strategies for the analysis of gene expression in tissues are able to study only one protein or RNA product at the same time. A new recent method referred to as < > or < > is able to analyze simultaneously several hundreds of different genes. The DNA array is a nylon membrane on which are spotted equal amounts of cDNAs corresponding to different genes. This filter is hybridized with a < > probe synthesized with mRNA derived from the tissue analyzed. The result gives a global profile of gene expression within the tissue and allows quantitative and comparative analysis between different tissues or cell types.

DNA, Complementary↗
PubMed2006

The Arabidopsis co-expression tool (ACT): a WWW-based tool and database for microarray-based gene expression analysis.

We present a new WWW-based tool for plant gene analysis, the Arabidopsis Co-Expression Tool (ACT), based on a large Arabidopsis thaliana microarray data set obtained from the Nottingham Arabidopsis Stock Centre. The co-expression analysis tool allows users to identify genes whose expression patterns are correlated across selected experiments or the complete data set. Results are accompanied by estimates of the statistical significance of the correlation relationships, expressed as probability (P) and expectation (E) values. Additionally, highly ranked genes on a correlation list can be examined using the novel clique finder tool to determine the sets of genes most likely to be regulated in a similar manner. In combination, these tools offer three levels of analysis: creation of correlation lists of co-expressed genes, refinement of these lists using two-dimensional scatter plots, and dissection into cliques of co-regulated genes. We illustrate the applications of the software by analysing genes encoding functionally related proteins, as well as pathways involved in plant responses to environmental stimuli. These analyses demonstrate novel biological relationships underlying the observed gene co-expression patterns. To demonstrate the ability of the software to develop testable hypotheses on gene function within a defined biological process we have used the example of cell wall biosynthesis genes. The resource is freely available at http://www.arabidopsis.leeds.ac.uk/ACT/

Arabidopsis↗
PubMed2006

Usefulness of the 5' region of the cDNA encoding acidic ribosomal phosphoprotein P0 conserved among rats, mice, and humans as a standard probe for gene expression analysis in different tissues and animal species.

Housekeeping genes are often used as internal standards for gene expression analysis. When steady-state transcript levels of 4 typically used housekeeping genes, i.e., beta-actin, glyceraldehyde 3-phosphate dehydrogenase, cyclophilin, and acidic ribosomal phosphoprotein P0 (36B4), were evaluated in various rat tissues, the 36B4 gene seemed to be the most suitable as a standard to compare the expression levels of genes among different tissues. Next, for possible quantitative comparison of the expression level of this gene among different animal species, we compared the nucleotide sequence of the cDNA of 36B4 among rats, mice, and humans. As a result, highly conserved regions showing more than 97.5% identities were observed in the 5' portion of its open reading frame. When samples of synthesized mRNA encoding rat, mouse, and human 36B4 were hybridized with the entire cDNA encoding rat 36B4 as a probe, hybridization signals of mRNAs of mouse and human 36B4 were much weaker than those of mRNA encoding rat 36B4. However, when they were hybridized with an oligonucleotide probe corresponding to the highly conserved regions, they showed similar signal intensities. Thus, these highly conserved regions of the cDNA encoding 36B4 were concluded to be an effective standard for use in gene expression analysis.

Animals↗
PubMed2005

A class of models for analyzing GeneChip gene expression analysis array data.

BACKGROUND: Various analytical methods exist that first quantify gene expression and then analyze differentially expressed genes from Affymetrix GeneChip gene expression analysis array data. These methods differ in the choice of probe measure (quantification of probe hybridization), summarizing multiple probe intensities into a gene expression value, and analysis of differential gene expression. Research papers that describe these methods focus on performance, and how their approaches differ from others. To better understand the common features and differences between various methods, and to evaluate their impact on the results of gene expression analysis, we describe a class of models, referred to as generalized probe models (GPMs), which encompass various currently available methods. RESULTS: Using an empirical dataset, we compared different formulations of GPMs, and GPMs with three other commonly used methods, i.e. MAS 5.0, dChip, and RMA. The comparison shows that, on a genome-wide scale , different methods yield similar results if the same probe measures are chosen. CONCLUSION: In this paper we present a general framework, i.e. GPMs, which encompasses various methods. GPMs permit the use of a wide range of probe measures and facilitate appropriate comparison between commonly used methods. We demonstrate that the dissimilar results stem primarily from different choice of probe measures, rather than other factors.

Algorithms↗