961 resultados para Microarray Analysis
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Xylella fastidiosa genome sequencing has generated valuable data by identifying genes acting either on metabolic pathways or in associated pathogenicity and virulence. Based on available information on these genes, new strategies for studying their expression patterns, such as microarray technology, were employed. A total of 2,600 primer pairs were synthesized and then used to generate fragments using the PCR technique. The arrays were hybridized against cDNAs labeled during reverse transcription reactions and which were obtained from bacteria grown under two different conditions (liquid XDM2 and liquid BCYE). All data were statistically analyzed to verify which genes were differentially expressed. In addition to exploring conditions for X. fastidiosa genome-wide transcriptome analysis, the present work observed the differential expression of several classes of genes (energy, protein, amino acid and nucleotide metabolism, transport, degradation of substances, toxins and hypothetical proteins, among others). The understanding of expressed genes in these two different media will be useful in comprehending the metabolic characteristics of X. fastidiosa, and in evaluating how important certain genes are for the functioning and survival of these bacteria in plants.
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Background: The post-genomic era has brought new challenges regarding the understanding of the organization and function of the human genome. Many of these challenges are centered on the meaning of differential gene regulation under distinct biological conditions and can be performed by analyzing the Multiple Differential Expression (MDE) of genes associated with normal and abnormal biological processes. Currently MDE analyses are limited to usual methods of differential expression initially designed for paired analysis. Results: We proposed a web platform named ProbFAST for MDE analysis which uses Bayesian inference to identify key genes that are intuitively prioritized by means of probabilities. A simulated study revealed that our method gives a better performance when compared to other approaches and when applied to public expression data, we demonstrated its flexibility to obtain relevant genes biologically associated with normal and abnormal biological processes. Conclusions: ProbFAST is a free accessible web-based application that enables MDE analysis on a global scale. It offers an efficient methodological approach for MDE analysis of a set of genes that are turned on and off related to functional information during the evolution of a tumor or tissue differentiation. ProbFAST server can be accessed at http://gdm.fmrp.usp.br/probfast.
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Alternative splicing of gene transcripts greatly expands the functional capacity of the genome, and certain splice isoforms may indicate specific disease states such as cancer. Splice junction microarrays interrogate thousands of splice junctions, but data analysis is difficult and error prone because of the increased complexity compared to differential gene expression analysis. We present Rank Change Detection (RCD) as a method to identify differential splicing events based upon a straightforward probabilistic model comparing the over-or underrepresentation of two or more competing isoforms. RCD has advantages over commonly used methods because it is robust to false positive errors due to nonlinear trends in microarray measurements. Further, RCD does not depend on prior knowledge of splice isoforms, yet it takes advantage of the inherent structure of mutually exclusive junctions, and it is conceptually generalizable to other types of splicing arrays or RNA-Seq. RCD specifically identifies the biologically important cases when a splice junction becomes more or less prevalent compared to other mutually exclusive junctions. The example data is from different cell lines of glioblastoma tumors assayed with Agilent microarrays.
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Calcineurin plays an important role in the control of cell morphology and virulence in fungi. Calcineurin is a serine/threonine-specific protein phosphatase heterodimer consisting of a catalytic subunit A and a regulatory subunit B. A mutant of Aspergillus fumigatus lacking the calcineurin A (calA) catalytic subunit exhibited defective hyphal morphology related to apical extension and branching growth, which resulted in drastically decreased filamentation. Here, we investigated which pathways are influenced by A. fumigatus calcineurin during proliferation by comparatively determining the transcriptional profile of A. fumigatus wild type and Delta calA mutant strains. Our results showed that the mitochondrial copy number is reduced in the Delta calA mutant strain, and the mutant has increased alternative oxidase (aoxA) mRNA accumulation and activity. Furthermore, we identified four genes that encode transcription factors that have increased mRNA expression in the Delta calA mutant. Deletion mutants for these transcription factors had reduced susceptibility to itraconazole, caspofungin, and sodium dodecyl sulfate (SDS). (C) 2009 Elsevier Inc. All rights reserved.
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In the context of cancer diagnosis and treatment, we consider the problem of constructing an accurate prediction rule on the basis of a relatively small number of tumor tissue samples of known type containing the expression data on very many (possibly thousands) genes. Recently, results have been presented in the literature suggesting that it is possible to construct a prediction rule from only a few genes such that it has a negligible prediction error rate. However, in these results the test error or the leave-one-out cross-validated error is calculated without allowance for the selection bias. There is no allowance because the rule is either tested on tissue samples that were used in the first instance to select the genes being used in the rule or because the cross-validation of the rule is not external to the selection process; that is, gene selection is not performed in training the rule at each stage of the cross-validation process. We describe how in practice the selection bias can be assessed and corrected for by either performing a cross-validation or applying the bootstrap external to the selection process. We recommend using 10-fold rather than leave-one-out cross-validation, and concerning the bootstrap, we suggest using the so-called. 632+ bootstrap error estimate designed to handle overfitted prediction rules. Using two published data sets, we demonstrate that when correction is made for the selection bias, the cross-validated error is no longer zero for a subset of only a few genes.
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Overexpression of kallikrein 7, a proteolytic enzyme important for epithelial cell shedding, may be causally involved in carcinogenesis, particularly in tumor metastasis and invasion. In this study, we have evaluated hK7 (human kallikrein 7) protein levels by immunohistochemistry in 367 cervical histological samples including 35 cases of cervicitis, 31 low-grade cervical intraepithelial neoplasia, 51 high-grade cervical intraepithelial neoplasia (H-SIL), 197 squamous cervical carcinomas (SCC) and 53 cervical adenocarcinomas. We have observed that hK7 staining increased with the severity of cervical disease. Intense hK7 staining was found in 15.2% of cervicitis samples, in contrast to 55% of H-SIL and 68% of SCC. Moreover, 92.5% of adenocarcinomas also exhibited intense hK7 staining. Differences in the expression of hK7 could potentially be used as a biomarker for the characterization of different stages of cervical disease.
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The molecular pathology of meningiomas and shwannomas involve the inactivation of the NF2 gene to generate grade I tumors. Genomic losses at 1p and 14q are observed in both neoplasms, although more frequently in meningiomas. The inactivation of unidentified genes located in these regions appears associated with tumor progression in meningiomas, but no clues to its molecular/clinical meaning are available in schwannomas. Recent microarray gene expression studies have demonstrated the existence of molecular subgroups in both entities. In the present study, we correlated the presence of genomic deletions at 1p, 14q, and 22q with the expression patterns of 96 tumor-related genes obtained by cDNA low-density microarrays in a series of 65 tumors including 42 meningiomas and 23 schwannomas. Two expression pattern groups were identified by cDNA mycroarray analysis when compared to the expression pattern in normal control RNA in both meningiomas and schwannomas, each one with patterns similar and different from the normal control. Meningioma and schwannoma subgroups differed in the expression of 38 and 16 genes, respectively. Using MLPA and microsatellites, we identified genomic losses at 1p, 14q, and 22q at nonrandom frequencies (12.5-69%) in meningiomas and schwannomas. Losses at 22q were almost equally frequent in both molecular expression subgroups in both neoplasms. However, deletions at 1p and 14q accumulated in meningiomas with a gene expression pattern different from the normal pattern, whereas the inverse situation occurred in schwannomas. Those anomalies characterized the schwannomas with expression pattern similar to the normal control. These findings suggest that deletions at 1p and 14q enhance the development of an abnormal tumor-related gene expression pattern in meningiomas, but this fact is not corroborated in schwannomas. (C) 2010 Elsevier Inc. All rights reserved.
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To identify novel genes involved in the molecular pathogenesis of chronic lymphocytic leukemia (CLL) we performed a serial analysis of gene expression (SAGE) in CLL cells, and compared this with healthy B cells (nCD19(+)). We found a high level of similarity among CLL subtypes, but a comparison of CLL versus nCD19(+) libraries revealed 55 genes that were over-represented and 49 genes that were down-regulated in CLL. A gene ontology analysis revealed that TOSO, which plays a functional role upstream of Fas extrinsic apoptosis pathway, was over-expressed in CLL cells. This finding was confirmed by real-time reverse transcription-polymerase chain reaction in 78 CLL and 12 nCD19(+) cases (P <.001). We validated expression using flow cytometry and tissue microarray and demonstrated a 5.6-fold increase of TOSO protein in circulating CLL cells (P =.013) and lymph nodes (P =.006). Our SAGE results have demonstrated that TOSO is a novel overexpressed antiapoptotic gene in CLL.
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Oral cancer is the eighth most prevalent cancer worldwide. It causes significant mortality and morbidity rates, which have motivated the search for prognostic factors to better tailor the individual management of oral squamous cell carcinoma patients. Nucleophosmin is a multifunctional protein that is involved in many cellular activities, such as, regulation of the tumor suppressor genes TP53 and p14(ARF). and is associated with proliferative and growth suppressive roles in the cell. Nucleophosmin is overexpressed in many solid tumors in human, including tumors of the colon, liver, stomach, ovary, and prostate. In this study, we analyzed the expression of nucleophosmin, Ki-67, and p53 by immunohistochemistry in oral squamous cell carcinomas. Less than 10% of nuclear staining was observed in 90.3%, 50.6%, and 65.3% of the cases for nucleophosmin, p53, and Ki-67, respectively. Expression of p53 was not significantly associated with any of the clinicopathologic parameters analyzed. Increased expression of Ki-67 was associated with the presence of lymph node metastasis (P < .0001), advanced stages of disease (P = .0030), tumors occurring in the floor of mouth (P = .0018), and moderately/well-differentiated tumors (P = .0287). Local recurrence was associated with higher expression of nucleophosmin (P = .0233), and disease-free survival rate was significantly better in patients with low expression of nucleophosmin. Multivariate analysis suggested that expression of nucleophosmin could be an independent prognostic factor for oral squamous cell carcinoma patients. (C) 2010 Elsevier Inc. All rights reserved.
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Motivation: This paper introduces the software EMMIX-GENE that has been developed for the specific purpose of a model-based approach to the clustering of microarray expression data, in particular, of tissue samples on a very large number of genes. The latter is a nonstandard problem in parametric cluster analysis because the dimension of the feature space (the number of genes) is typically much greater than the number of tissues. A feasible approach is provided by first selecting a subset of the genes relevant for the clustering of the tissue samples by fitting mixtures of t distributions to rank the genes in order of increasing size of the likelihood ratio statistic for the test of one versus two components in the mixture model. The imposition of a threshold on the likelihood ratio statistic used in conjunction with a threshold on the size of a cluster allows the selection of a relevant set of genes. However, even this reduced set of genes will usually be too large for a normal mixture model to be fitted directly to the tissues, and so the use of mixtures of factor analyzers is exploited to reduce effectively the dimension of the feature space of genes. Results: The usefulness of the EMMIX-GENE approach for the clustering of tissue samples is demonstrated on two well-known data sets on colon and leukaemia tissues. For both data sets, relevant subsets of the genes are able to be selected that reveal interesting clusterings of the tissues that are either consistent with the external classification of the tissues or with background and biological knowledge of these sets.
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The current approach to prostate cancer diagnosis has major limitations including the inability of prostate-specific antigen (PSA) assays to accurately differentiate between prostate cancer and benign prostate hyperplasia (BPH) and the imprecision of transrectal ultrasound (TRUS) biopsy sampling. We have employed cDNA microarray screening to compare gene expression patterns in BPH and tumour samples to identify expression markers that may be useful in discriminating between these conditions. Screening of 3 individual cDNA arrays identified 8 genes with expression 3-fold greater in 6 tumour tissues than in 1 nontumour sample and I BPH sample. Real-time PCR was used to confirm the overexpression of these 8 genes and 12 genes selected from the literature against a panel of 17 tumours and I 1 BPH samples. Two genes, delta-catenin (delta-catenin; CTNND2) and prostate-specific membrane antigen (PSMA; FOLH1), were significantly overexpressed in prostate cancer compared to BPH. Prostate epithelial cells stained positively for S-catenin and PSMA in our prostate cancer tissues, whereas the majority of our BPH tissues were negative for both markers. Thus we have identified delta-catenin (not previously associated with prostatic adenocarcinoma) and confirmed the potential of PSMA as potential candidates for the diagnosis and management of prostate cancer. (C) 2002 Wiley-Liss. Inc.
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Microarray allow to monitoring simultaneously thousands of genes, where the abundance of the transcripts under a same experimental condition at the same time can be quantified. Among various available array technologies, double channel cDNA microarray experiments have arisen in numerous technical protocols associated to genomic studies, which is the focus of this work. Microarray experiments involve many steps and each one can affect the quality of raw data. Background correction and normalization are preprocessing techniques to clean and correct the raw data when undesirable fluctuations arise from technical factors. Several recent studies showed that there is no preprocessing strategy that outperforms others in all circumstances and thus it seems difficult to provide general recommendations. In this work, it is proposed to use exploratory techniques to visualize the effects of preprocessing methods on statistical analysis of cancer two-channel microarray data sets, where the cancer types (classes) are known. For selecting differential expressed genes the arrow plot was used and the graph of profiles resultant from the correspondence analysis for visualizing the results. It was used 6 background methods and 6 normalization methods, performing 36 pre-processing methods and it was analyzed in a published cDNA microarray database (Liver) available at http://genome-www5.stanford.edu/ which microarrays were already classified by cancer type. All statistical analyses were performed using the R statistical software.
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Background: Gene expression analysis has emerged as a major biological research area, with real-time quantitative reverse transcription PCR (RT-QPCR) being one of the most accurate and widely used techniques for expression profiling of selected genes. In order to obtain results that are comparable across assays, a stable normalization strategy is required. In general, the normalization of PCR measurements between different samples uses one to several control genes (e. g. housekeeping genes), from which a baseline reference level is constructed. Thus, the choice of the control genes is of utmost importance, yet there is not a generally accepted standard technique for screening a large number of candidates and identifying the best ones. Results: We propose a novel approach for scoring and ranking candidate genes for their suitability as control genes. Our approach relies on publicly available microarray data and allows the combination of multiple data sets originating from different platforms and/or representing different pathologies. The use of microarray data allows the screening of tens of thousands of genes, producing very comprehensive lists of candidates. We also provide two lists of candidate control genes: one which is breast cancer-specific and one with more general applicability. Two genes from the breast cancer list which had not been previously used as control genes are identified and validated by RT-QPCR. Open source R functions are available at http://www.isrec.isb-sib.ch/similar to vpopovic/research/ Conclusion: We proposed a new method for identifying candidate control genes for RT-QPCR which was able to rank thousands of genes according to some predefined suitability criteria and we applied it to the case of breast cancer. We also empirically showed that translating the results from microarray to PCR platform was achievable.
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SUMMARY: Large sets of data, such as expression profiles from many samples, require analytic tools to reduce their complexity. The Iterative Signature Algorithm (ISA) is a biclustering algorithm. It was designed to decompose a large set of data into so-called 'modules'. In the context of gene expression data, these modules consist of subsets of genes that exhibit a coherent expression profile only over a subset of microarray experiments. Genes and arrays may be attributed to multiple modules and the level of required coherence can be varied resulting in different 'resolutions' of the modular mapping. In this short note, we introduce two BioConductor software packages written in GNU R: The isa2 package includes an optimized implementation of the ISA and the eisa package provides a convenient interface to run the ISA, visualize its output and put the biclusters into biological context. Potential users of these packages are all R and BioConductor users dealing with tabular (e.g. gene expression) data. AVAILABILITY: http://www.unil.ch/cbg/ISA CONTACT: sven.bergmann@unil.ch
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Preoperative chemoradiation significantly improves oncological outcome in locally advanced rectal cancer. However there is no effective method of predicting tumor response to chemoradiation in these patients. Peripheral blood mononuclear cells have emerged recently as pathology markers of cancer and other diseases, making possible their use as therapy predictors. Furthermore, the importance of the immune response in radiosensivity of solid organs led us to hypothesized that microarray gene expression profiling of peripheral blood mononuclear cells could identify patients with response to chemoradiation in rectal cancer. Thirty five 35 patients with locally advanced rectal cancer were recruited initially to perform the study. Peripheral blood samples were obtained before neaodjuvant treatment. RNA was extracted and purified to obtain cDNA and cRNA for hybridization of microarrays included in Human WG CodeLink bioarrays. Quantitative real time PCR was used to validate microarray experiment data. Results were correlated with pathological response, according to Mandard´s criteria and final UICC Stage (patients with tumor regression grade 1-2 and downstaging being defined as responders and patients with grade 3-5 and no downstaging as non-responders). Twenty seven out of 35 patients were finally included in the study. We performed a multiple t-test using Significance Analysis of Microarrays, to find those genes differing significantly in expression, between responders (n = 11) and non-responders (n = 16) to CRT. The differently expressed genes were: BC 035656.1, CIR, PRDM2, CAPG, FALZ, HLA-DPB2, NUPL2, and ZFP36. The measurement of FALZ (p = 0.029) gene expression level determined by qRT-PCR, showed statistically significant differences between the two groups. Gene expression profiling reveals novel genes in peripheral blood samples of mononuclear cells that could predict responders and non-responders to chemoradiation in patients with locally advanced rectal cancer. Moreover, our investigation added further evidence to the importance of mononuclear cells' mediated response in the neoadjuvant treatment of rectal cancer.