Gene expression profiles in HepG2 cells exposed to atorvastatin
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
SUBMITTER: Camilla Stormo
PROVIDER: E-GEOD-57071 | biostudies-arrayexpress |
REPOSITORIES: biostudies-arrayexpress
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Project description:In this study we performed a genome wide analysis of the entire complement of mRNAs in clear cell renal cell carcinomas (ccRCC) by means of the Affymetrix Exon Array platform. The analyses were performed both at gene and exon level. Under our parameters over 2,000 genes resulted differentially expressed, and about 250 genes resulted alternative spliced showing differential inclusion of specific cassette exons comparing tumor and non tumoral tissues. 20 total samples were used for exon array analysis: 10 ccRCC tumor sample (T) and their matched non-tumor (NT) kidney tissues samples. All exon array data were analyzed using Partek Genomic Suite 6.4 software (Partek). The robust multi-array average (RMA) algorithm was used for the gene- and exon-level intensity analyses. Data were M-oM-,M-^Altered to consider only those probe sets included in the M-bM-^@M-^\Core Meta-ProbesetM-bM-^@M-^]. Lists of genes and exons with significant variation of the expression levels were generated by using a 0.01 FDR criterion as a significant cutoff. Partek list of significant exon-level probesets (FDR corrected p-value < 0.01) and exon level ANOVA test list were used to create - by an ad hoc Python script - a database of significant probesets from Exon Array experiments. To make more stringent the analysis criteria we excluded from the final exon lists all those cases in which Exon Array revealed a significant change at gene-level. This database we created has been used to explore simple exon skipping events using as reference ASPicDB single exon skipping splicing events after mapping Affymetrix probesets on ASPicDB .gtf files. A single exon from the Partek list was defined significantly skipped only if its adjacent exons were not, by using 0.01 and 0.05 p-value criterions as a significant cutoff for the M-bM-^@M-^\skippedM-bM-^@M-^] and adjacent exons, respectively.
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Project description:This SuperSeries is composed of the SubSeries listed below. Refer to individual Series
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Project description:By using high-density DNA microarrays, we analyzed the gene-expression profile of Hodgkin's lymphoma cell lines. RNA was extracted from established Hodgkin's lymphoma cell lines and analyzed with Affymetrix Human Exon 1.0ST microarrays.
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Project description:MicroRNAs are a group of non-coding small RNAs with lengths around 21~23nt and function as inhibitors to repress mRNA translation by targeting their 3' untranslated region. Recent research shows that microRNAs are involved in many biological processes such as cell growth, development and cancer, etc. Here, we introduce a novel artificial microRNA p-27-5p which can inhibit cell proliferation in breast cancer cell lines. This data shows the expression changes in breast cancer cell line T-47D with the effect of artificial miR-p-27-5p. 4 total samples were analyzed. We generated mimic/control paired samples with 2 repeats.
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Project description:While blood transcriptional profiling has improved diagnosis and understanding of disease pathogenesis of adult tuberculosis (TB), no studies applying gene expression profiling of children with TB have been described so far. In this study, we have compared whole blood gene expression in childhood TB patients, as well as in healthy latently infected (LTBI) and uninfected (HC) children in a cohort of Warao Amerindians in the Delta Amacuro in Venezuela. We identified a 116-gene signature set by means of random forest analysis that showed an average prediction error of 11% for TB vs. LTBI and for TB vs. LTBI vs. HC in our dataset. Furthermore, a minimal set of only 9 genes showed a significant predictive value for all previously published adult studies using whole blood gene expression, with average prediction errors between 17% and 23%. 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