53 resultados para MICROARRAY


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Background Congenital deletions affecting 3q11q23 have rarely been reported and only five cases have been molecularly characterised. Genotype. phenotype correlation has been hampered by the variable sizes and breakpoints of the deletions. In this study, 14 novel patients with deletions in 3q11q23 were investigated and compared with 13 previously reported patients. Methods Clinical data were collected from 14 novel patients that had been investigated by high resolution microarray techniques. Molecular investigation and updated clinical information of one cytogenetically previously reported patient were also included. Results The molecular investigation identified deletions in the region 3q12.3q21.3 with different boundaries and variable sizes. The smallest studied deletion was 580 kb, located in 3q13.31. Genotype. phenotype comparison in 24 patients sharing this shortest region of overlapping deletion revealed several common major characteristics including significant developmental delay, muscular hypotonia, a high arched palate, and recognisable facial features including a short philtrum and protruding lips. Abnormal genitalia were found in the majority of males, several having micropenis. Finally, a postnatal growth pattern above the mean was apparent. The 580 kb deleted region includes five RefSeq genes and two of them are strong candidate genes for the developmental delay: DRD3 and ZBTB20. Conclusion A newly recognised 3q13.31 microdeletion syndrome is delineated which is of diagnostic and prognostic value. Furthermore, two genes are suggested to be responsible for the main phenotype.

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Trypanosoma cruzi is an organism highly resistant to ionizing radiation. Following a dose of 500 Gy of gamma radiation, the fragmented genomic DNA is gradually reconstructed and the pattern of chromosomal bands is restored in less than 48 hours. Cell growth arrests after irradiation but, while DNA is completely fragmented, RNA maintains its integrity. In this work we compared the transcriptional profiles of irradiated and non-irradiated epimastigotes at different time points after irradiation using microarray. In total, 273 genes were differentially expressed; from these, 160 were up-regulated and 113 down-regulated. We found that genes with predicted functions are the most prevalent in the down-regulated gene category. Translation and protein metabolic processes, as well as generation of precursor of metabolites and energy pathways were affected. In contrast, the up-regulated category was mainly composed of obsolete sequences (which included some genes of the kinetoplast DNA), genes coding for hypothetical proteins, and Retrotransposon Hot Spot genes. Finally, the tyrosyl-DNA phosphodiesterase 1, a gene involved in double-strand DNA break repair process, was up-regulated. Our study demonstrated the peculiar response to ionizing radiation, raising questions about how this organism changes its gene expression to manage such a harmful stress.

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Limited information is available regarding the modulation of genes involved in the innate host response to Paracoccidioides brasiliensis, the etiologic agent of paracoccidioidomycosis. Therefore, we sought to characterize, for the first time, the transcriptional profile of murine bone marrow-derived dendritic cells (DCs) at an early stage following their initial interaction with P. brasiliensis. DCs connect innate and adaptive immunity by recognizing invading pathogens and determining the type of effector T-cell that mediates an immune response. Gene expression profiles were analyzed using microarray and validated using real-time RT-PCR and protein secretion studies. A total of 299 genes were differentially expressed, many of which are involved in immunity, signal transduction, transcription and apoptosis. Genes encoding the cytokines IL-12 and TNF-alpha, along with the chemokines CCL22, CCL27 and CXCL10, were up-regulated, suggesting that P. brasiliensis induces a potent proinflammatory response in DCs. In contrast, pattern recognition receptor (PRR)-encoding genes, particularly those related to Toll-like receptors, were down-regulated or unchanged. This result prompted us to evaluate the expression profiles of dectin-1 and mannose receptor, two other important fungal PRRs that were not included in the microarray target cDNA sequences. Unlike the mannose receptor, the dectin-1 receptor gene was significantly induced, suggesting that this beta-glucan receptor participates in the recognition of P. brasiliensis. We also used a receptor inhibition assay to evaluate the roles of these receptors in coordinating the expression of several immune-related genes in DCs upon fungal exposure. Altogether, our results provide an initial characterization of early host responses to P. brasiliensis and a basis for better understanding the infectious process of this important neglected pathogen.

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Topoisomerase 2 alpha (), HER-2/ and are genes that lie on chromosome 17 and correlate with the prognosis and prediction of target-driven therapy against tumors. In a previous study, we showed that TOP2A transcripts levels were significantly higher in soft tissue sarcomas (STS) than in benign tumors and desmoid-type fibromatoses (FM). Because these genes have been insufficiently examined in STS, we aimed to identify alterations in TOP2A and HER-2 expression by fluorescent in situ hybridization and immunohistochemistry, as well as that of survivin, and correlate them with clinicopathologic findings to assess their prognostic value. Eighteen FM and 244 STS were included. Fluorescent in situ hybridization and immunohistochemistry were performed on a tissue microarray. TOP2A and survivin were more highly expressed in sarcomas than in FM. TOP2A was an independent predictor of an unfavorable prognosis; it was combined with formerly established prognostic factors (primarily histologic grade and tumor size at diagnosis) to create a prognostic index that evaluated overall survival. Gene amplification/polysomy (13%) did not correlate with protein overexpression. Survivin and HER-2 expression were not associated with patient outcomes. These findings might become valuable in the management of patients with STS and possibly in the prospective evaluation of responses to new target-driven therapies.

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This study aimed to identify the CD24 and CD44 immunophenotypes within invasive ductal breast carcinoma (I DC) subgroups defined by immunohistochesmistry markers and to determine its influence on prognosis as well as its association with the expression of Ki-67, cytokeratins (CK5 and CK 18) and claudin-7. Immunohistochemical expression of CD44 and CD24 alone or in combination was investigated in 95 IDC cases arranged in a tissue microarray (TMA). The association with subgroups defined as luminal A and B; HER2 rich and triple negative, or with the other markers and prognosis was analyzed. CD44(+)/CD24(-) and CD44(-)/CD24(+) were respectively present in 8.4% and 16.8% of the tumors, a lack of both proteins was detected in 6.3%, while CD441(-)/CD24(+) was observed in 45.3% of the tumors. Although there was no significant correlation between subgroups and different phenotypes, the CD44(+)/CD24(-) phenotype was more common in the basal subgroups but absent in HER2 tumors, whereas luminal tumors are enriched in CD44(-)/CD24(+) and CD44(+)/CD24(+) cells. The frequency of CD44(+)/CD24(-) or CD44(-)/CD24(+) was not associated with clinical characteristics or biological markers. There was also no significant association of these phenotypes with the event free (DFS) and overall survival (OS). Single CD44(+) was evident in 57.9% of the tumors and was marginally associated to grading and not to any other tumor characteristics as well as OS and DFS. CD24(+) was positive in 74.7% of the tumors, showing a significant association with estrogen receptor, progesterone receptor and Ki-67 and a marginal association with CKI8 and claudin-7. Expression of claudin-7 and Ki-67 did not associate with the cancer subgroups, while a positive association between CK18 and the luminal subgroups was found (P=0.03). CK5, CK18 and Ki-67 expression had no influence in OS or DFS. Single CD24(+) (P=0.07) and claudin-7 positivity (P=0.05) were associated with reduced time of recurrence, suggesting a contribution of these markers to aggressiveness of breast cancer.

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Objectives: Determination of the SET protein levels in head and neck squamous cell carcinoma (HNSCC) tissue samples and the SET role in cell survival and response to oxidative stress in HNSCC cell lineages. Materials and Methods: SET protein was analyzed in 372 HNSCC tissue samples by immunohistochemistry using tissue microarray and HNSCC cell lineages. Oxidative stress was induced with the pro-oxidant tert-butylhydroperoxide (50 and 250 mu M) in the HNSCC HN13 cell lineage either with (siSET) or without (siNC) SET knockdown. Cell viability was evaluated by trypan blue exclusion and annexin V/propidium iodide assays. It was assessed caspase-3 and -9, PARP-1, DNA fragmentation, NM23-H1, SET, Akt and phosphorylated Akt (p-Akt) status. Acidic vesicular organelles (AVOs) were assessed by the acridine orange assay. Glutathione levels and transcripts of antioxidant genes were assayed by fluorometry and real time PCR, respectively. Results: SET levels were up-regulated in 97% tumor tissue samples and in HNSCC cell lineages. SiSET in HN13 cells (i) promoted cell death but did not induced caspases, PARP-1 cleavage or DNA fragmentation, and (ii) decreased resistance to death induced by oxidative stress, indicating SET involvement through caspase-independent mechanism. The red fluorescence induced by siSET in HN13 cells in the acridine orange assay suggests SET-dependent prevention of AVOs acidification. NM23-H1 protein was restricted to the cytoplasm of siSET/siNC HN13 cells under oxidative stress, in association with decrease of cleaved SET levels. In the presence of oxidative stress, siNC HN13 cells showed lower GSH antioxidant defense (GSH/GSSG ratio) but higher expression of the antioxidant genes PRDX6, SOD2 and TXN compared to siSET HN13 cells. Still under oxidative stress, p-Akt levels were increased in siNC HN13 cells but not in siSET HN13, indicating its involvement in HN13 cell survival. Similar results for the main SET effects were observed in HN12 and CAL 27 cell lineages, except that HN13 cells were more resistant to death. Conclusion: SET is potential (i) marker for HNSCC associated with cancer cell resistance and (ii) new target in cancer therapy. (C) 2012 Elsevier Ltd. All rights reserved.

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Background and Aim: The identification of gastric carcinomas (GC) has traditionally been based on histomorphology. Recently, DNA microarrays have successfully been used to identify tumors through clustering of the expression profiles. Random forest clustering is widely used for tissue microarrays and other immunohistochemical data, because it handles highly-skewed tumor marker expressions well, and weighs the contribution of each marker according to its relatedness with other tumor markers. In the present study, we e identified biologically- and clinically-meaningful groups of GC by hierarchical clustering analysis of immunohistochemical protein expression. Methods: We selected 28 proteins (p16, p27, p21, cyclin D1, cyclin A, cyclin B1, pRb, p53, c-met, c-erbB-2, vascular endothelial growth factor, transforming growth factor [TGF]-beta I, TGF-beta II, MutS homolog-2, bcl-2, bax, bak, bcl-x, adenomatous polyposis coli, clathrin, E-cadherin, beta-catenin, mucin (MUC) 1, MUC2, MUC5AC, MUC6, matrix metalloproteinase [ MMP]-2, and MMP-9) to be investigated by immunohistochemistry in 482 GC. The analyses of the data were done using a random forest-clustering method. Results: Proteins related to cell cycle, growth factor, cell motility, cell adhesion, apoptosis, and matrix remodeling were highly expressed in GC. We identified protein expressions associated with poor survival in diffuse-type GC. Conclusions: Based on the expression analysis of 28 proteins, we identified two groups of GC that could not be explained by any clinicopathological variables, and a subgroup of long-surviving diffuse-type GC patients with a distinct molecular profile. These results provide not only a new molecular basis for understanding the biological properties of GC, but also better prediction of survival than the classic pathological grouping.

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A common interest in gene expression data analysis is to identify from a large pool of candidate genes the genes that present significant changes in expression levels between a treatment and a control biological condition. Usually, it is done using a statistic value and a cutoff value that are used to separate the genes differentially and nondifferentially expressed. In this paper, we propose a Bayesian approach to identify genes differentially expressed calculating sequentially credibility intervals from predictive densities which are constructed using the sampled mean treatment effect from all genes in study excluding the treatment effect of genes previously identified with statistical evidence for difference. We compare our Bayesian approach with the standard ones based on the use of the t-test and modified t-tests via a simulation study, using small sample sizes which are common in gene expression data analysis. Results obtained report evidence that the proposed approach performs better than standard ones, especially for cases with mean differences and increases in treatment variance in relation to control variance. We also apply the methodologies to a well-known publicly available data set on Escherichia coli bacterium.

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Some species of Trichoderma have successfully been used in the commercial biological control of fungal pathogens, e.g., Sclerotinia sclerotiorum, an economically important pathogen of common beans (Phaseolus vulgaris L.). The objectives of the present study were (1) to provide molecular characterization of Trichoderma strains isolated from the Brazilian Cerrado; (2) to assess the metabolic profile of each strain by means of Biolog FF Microplates; and (3) to evaluate the ability of each strain to antagonize S. sclerotiorum via the production of cell wall-degrading enzymes (CWDEs), volatile antibiotics, and dual-culture tests. Among 21 isolates, we identified 42.86 % as Trichoderma asperellum, 33.33 % as Trichoderma harzianum, 14.29 % as Trichoderma tomentosum, 4.76 % as Trichoderma koningiopsis, and 4.76 % as Trichoderma erinaceum. Trichoderma asperellum showed the highest CWDE activity. However, no species secreted a specific group of CWDEs. Trichoderma asperellum 364/01, T. asperellum 483/02, and T. asperellum 356/02 exhibited high and medium specific activities for key enzymes in the mycoparasitic process, but a low capacity for antagonism. We observed no significant correlation between CWDE and antagonism, or between metabolic profile and antagonism. The diversity of Trichoderma species, and in particular of T. harzianum, was clearly reflected in their metabolic profiles. Our findings indicate that the selection of Trichoderma candidates for biological control should be based primarily on the environmental fitness of competitive isolates and the target pathogen. (C) 2012 The British Mycological Society. Published by Elsevier Ltd. All rights reserved.

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To detect expression of bone morphogenetic protein 15 (BMP15) and growth differentiation factor 9 (GDF9) in oocytes, and their receptor type 2 receptor for BMPs (BMPR2) in cumulus cells in women with polycystic ovary syndrome (PCOS) undergoing in vitro fertilization (IVF), and determine if BMPR2, BMP15, and GDF9 expression correlate with hyperandrogenism in FF of PCOS patients. Prospective case-control study. Eighteen MII-oocytes and their respective cumulus cells were obtained from 18 patients with PCOS, and 48 MII-oocytes and cumulus cells (CCs) from 35 controls, both subjected to controlled ovarian hyperstimulation (COH), and follicular fluid (FF) was collected from small (10-14 mm) and large (> 18 mm) follicles. RNeasy Micro Kit (Qiagen(A (R))) was used for RNA extraction and gene expression was quantified in each oocyte individually and in microdissected cumulus cells from cumulus-oocyte complexes retrieved from preovulatory follicles using qRT-PCR. Chemiluminescence and RIA assays were used for hormone assays. BMP15 and GDF9 expression per oocyte was higher among women with PCOS than the control group. A positive correlation was found between BMPR2 transcripts and hyperandrogenism in FF of PCOS patients. Progesterone values in FF were lower in the PCOS group. We inferred that BMP15 and GDF9 transcript levels increase in mature PCOS oocytes after COH, and might inhibit the progesterone secretion by follicular cells in PCOS follicles, preventing premature luteinization in cumulus cells. BMPR2 expression in PCOS cumulus cells might be regulated by androgens.

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Lymphatic vessels serve as major routes for regional dissemination, and therefore, lymph node status is a key indicator of prognosis. To predict lymph node metastasis, tumor lymphatic density and lymphangiogenesis-related molecules have been studied in various tumor types. To our knowledge, no previous studies have evaluated the role of intratumoral lymphatic vessel density (LVD) in the behavior of vulvar carcinomas. The aim of this study was to analyze intratumoral LVD in relation to patient survival and well-characterized prognostic factors for cancer. Thirty-five patients with vulvar squamous cell carcinoma underwent vulvectomy and dissection of regional lymph nodes. Clinical records were reviewed, in addition to histological grade, peritumoral lymphatic invasion, and depth of infiltration for each case. Tissue microarray paraffin blocks were created, and lymphatic vessels were detected using immunohistochemical staining of podoplanin (D2-40 antibody). Intratumoral LVD was quantified by counting the number of stained vessels. Higher values for intratumoral LVD were associated with low-grade and low-stage tumors, and with tumors without lymphatic invasion and reduced stromal infiltration. In a univariate analysis, high intratumoral LVD was associated with a higher rate of overall survival and a lower rate of lymph node metastasis. Our results suggest that increased intratumoral LVD is associated with favorable prognosis in vulvar squamous carcinomas.

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Background: A recent microarray study identified a set of genes whose combined expression patterns were predictive of poor outcome in a cohort of adult adrenocortical tumors (ACTs). The difference between the expression values measured by qRT-PCR of DLGAP5 and PINK1 genes was the best molecular predictor of recurrence and malignancy. Among the adrenocortical carcinomas, the combined expression of BUB1B and PINK1 genes was the most reliable predictor of overall survival. The prognostic and molecular heterogeneity of ACTs raises the need to study the applicability of these molecular markers in other cohorts. Objective: To validate the combined expression of BUB1B, DLGAP5, and PINK1 as outcome predictor in ACTs from a Brazilian cohort of adult and pediatric patients. Patients and methods: BUB1B, DLGAP5, and PINK1 expression was assessed by quantitative PCR in 53 ACTs from 52 patients - 24 pediatric and 28 adults (one pediatric patient presented a bilateral asynchronous ACT). Results: DLGAP5 PINK1 and BUB1B PINK1 were strong predictors of disease-free survival and overall survival, respectively, among adult patients with ACT. In the pediatric cohort, these molecular predictors were only marginally associated with disease-free survival but not with overall survival. Conclusion: This study confirms the prognostic value of the combined expression of BUB1B, DLGAP5, and PINK1 genes in a Brazilian group of adult ACTs. Among pediatric ACTs, other molecular predictors of outcome are required.

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Abstract Background One goal of gene expression profiling is to identify signature genes that robustly distinguish different types or grades of tumors. Several tumor classifiers based on expression profiling have been proposed using microarray technique. Due to important differences in the probabilistic models of microarray and SAGE technologies, it is important to develop suitable techniques to select specific genes from SAGE measurements. Results A new framework to select specific genes that distinguish different biological states based on the analysis of SAGE data is proposed. The new framework applies the bolstered error for the identification of strong genes that separate the biological states in a feature space defined by the gene expression of a training set. Credibility intervals defined from a probabilistic model of SAGE measurements are used to identify the genes that distinguish the different states with more reliability among all gene groups selected by the strong genes method. A score taking into account the credibility and the bolstered error values in order to rank the groups of considered genes is proposed. Results obtained using SAGE data from gliomas are presented, thus corroborating the introduced methodology. Conclusion The model representing counting data, such as SAGE, provides additional statistical information that allows a more robust analysis. The additional statistical information provided by the probabilistic model is incorporated in the methodology described in the paper. The introduced method is suitable to identify signature genes that lead to a good separation of the biological states using SAGE and may be adapted for other counting methods such as Massive Parallel Signature Sequencing (MPSS) or the recent Sequencing-By-Synthesis (SBS) technique. Some of such genes identified by the proposed method may be useful to generate classifiers.

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Abstract Background Transcript enumeration methods such as SAGE, MPSS, and sequencing-by-synthesis EST "digital northern", are important high-throughput techniques for digital gene expression measurement. As other counting or voting processes, these measurements constitute compositional data exhibiting properties particular to the simplex space where the summation of the components is constrained. These properties are not present on regular Euclidean spaces, on which hybridization-based microarray data is often modeled. Therefore, pattern recognition methods commonly used for microarray data analysis may be non-informative for the data generated by transcript enumeration techniques since they ignore certain fundamental properties of this space. Results Here we present a software tool, Simcluster, designed to perform clustering analysis for data on the simplex space. We present Simcluster as a stand-alone command-line C package and as a user-friendly on-line tool. Both versions are available at: http://xerad.systemsbiology.net/simcluster. Conclusion Simcluster is designed in accordance with a well-established mathematical framework for compositional data analysis, which provides principled procedures for dealing with the simplex space, and is thus applicable in a number of contexts, including enumeration-based gene expression data.

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Abstract Background Several mathematical and statistical methods have been proposed in the last few years to analyze microarray data. Most of those methods involve complicated formulas, and software implementations that require advanced computer programming skills. Researchers from other areas may experience difficulties when they attempting to use those methods in their research. Here we present an user-friendly toolbox which allows large-scale gene expression analysis to be carried out by biomedical researchers with limited programming skills. Results Here, we introduce an user-friendly toolbox called GEDI (Gene Expression Data Interpreter), an extensible, open-source, and freely-available tool that we believe will be useful to a wide range of laboratories, and to researchers with no background in Mathematics and Computer Science, allowing them to analyze their own data by applying both classical and advanced approaches developed and recently published by Fujita et al. Conclusion GEDI is an integrated user-friendly viewer that combines the state of the art SVR, DVAR and SVAR algorithms, previously developed by us. It facilitates the application of SVR, DVAR and SVAR, further than the mathematical formulas present in the corresponding publications, and allows one to better understand the results by means of available visualizations. Both running the statistical methods and visualizing the results are carried out within the graphical user interface, rendering these algorithms accessible to the broad community of researchers in Molecular Biology.