Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Analysis of clinical bladder cancer classification according to microarray expression profiles

ABSTRACT: Using Affymetrix microarray technology we analyzed the gene expression profiles of the most important pathological categories of bladder cancer in order to detect potential marker genes. Applying an unsupervised cluster algorithm we observed clear differences between tumor and control samples, as well as between superficial and muscle invasive tumors. According to cluster results, the T1 high grade tumor type presented a global genetic profile which could not be distinguished from invasive cases. We described a new measure to classify differentially expressed genes and we compared it against the B-rank statistic as a standard method. According to this new classification method, the biological functions overrepresented in top differentially expressed genes when comparing tumor versus control samples were associated with growth, differentiation, immune system response, communication, cellular matrix and enzyme regulation. Comparing superficial versus invasive samples, the most important overrepresented biological category was growth and, specifically, DNA synthesis and mitotic cytoskeleton. On the other hand, some under expressed genes have been clearly related to muscular tissue contamination in control samples. Finally, we demonstrated that a pool strategy could be a good option to detect the best differentially expressed genes between two compared conditions. Keywords: disease state analysis

ORGANISM(S): Homo sapiens

PROVIDER: GSE7476 | GEO | 2007/07/01

SECONDARY ACCESSION(S): PRJNA100283

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Transcription profiling of human bladder cancers to develop a clinical classification according to microarray expression profiles

Project description:Using Affymetrix microarray technology we analyzed the gene expression profiles of the most important pathological categories of bladder cancer in order to detect potential marker genes. Applying an unsupervised cluster algorithm we observed clear differences between tumor and control samples, as well as between superficial and muscle invasive tumors. According to cluster results, the T1 high grade tumor type presented a global genetic profile which could not be distinguished from invasive cases. We described a new measure to classify differentially expressed genes and we compared it against the B-rank statistic as a standard method. According to this new classification method, the biological functions overrepresented in top differentially expressed genes when comparing tumor versus control samples were associated with growth, differentiation, immune system response, communication, cellular matrix and enzyme regulation. Comparing superficial versus invasive samples, the most important overrepresented biological category was growth and, specifically, DNA synthesis and mitotic cytoskeleton. On the other hand, some under expressed genes have been clearly related to muscular tissue contamination in control samples. Finally, we demonstrated that a pool strategy could be a good option to detect the best differentially expressed genes between two compared conditions. Experiment Overall Design: We analyzed gene expression profiles in normal bladder tissues (controls), low grade superficial tumor samples (pathologically classified as Ta low grade, named as Ta), high grade superficial tumors with an unclear clinical behavior (T1 high grade, named as T1) and high grade muscle invasive tumors (pathologically classified as T2, T3 or T4, named as T2+). We analyzed data using a sub-pooling strategy. The number of individual samples on every pool was: controls (4, 4, 4), Ta (5, 5, 5), T1 (5, 4, 4) and T2+ (5, 5, 5).

2008-06-15 | E-GEOD-7476 | biostudies-arrayexpress

IGR_BLADDER_GE_STUDY_JA

Project description:Background: Although histology is the corner stone for decision analysis in bladder cancer, it still needs refinement and new tools to better identify tumours with the highest risk of progression or recurrence. Objective: We designed a prospective study to evaluate whether microarray analysis could find a molecular signature for pathologic staging and/or grading. Design, setting and participants: Prospective multicentric study conducted from September 2007 to May 2008 (108 bladder tumours (45 pTa, 35 pT1 and 28> pT1)). Microarray analysis was performed with Agilent Technologies Human Whole Genome 4 x 44K oligonucleotide microarrays and used a method of dual color type versus a reference consists of a pool of tumours. From the lists of genes provided by the BrB Class Comparison analyses, we validated the microarray results of 38 selected differentially expressed genes by RT-QPCR in another bladder tumour cohort (n = 95). Results: The cluster M-^Ssuperficial vs invasive stageM-^T properly classified 92.9% of invasive stages and 66.3% of superficial stages. Among the superficial tumours, the cluster analysis showed that pT1b tumours were closer to invasive stages than pT1a tumours. We also found molecular differences between low and high-grade superficial tumours. However these differences were less caricatural than the one observed for staging.Conclusions: We confirm that the histopathological classification into subgroups pTa, pT1a and pT1b may have a translation in a M-^Smolecular signatureM-^T with a continuous progression of deregulation (overexpression or repression of these genes) from superficial (pTa) to more invasive (pT1a then b stage).

2013-08-13 | E-MTAB-993 | biostudies-arrayexpress

Classification of carcinoma in situ lesions in human bladder cancer

Project description:The presence of carcinoma in situ (CIS) lesions in the urinary bladder is associated with a high risk of disease progression to a muscle invasive stage. In this study, we used microarray expression profiling to examine the gene expression patterns in superficial transitional cell carcinoma (sTCC) with surrounding CIS (13 patients), without surrounding CIS lesions (15 patients), and in muscle invasive carcinomas (mTCC; 13 patients). Hierarchical cluster analysis separated the sTCC samples according to the presence or absence of CIS in the surrounding urothelium. We identified a few gene clusters that contained genes with similar expression levels in transitional cell carcinoma (TCC) with surrounding CIS and invasive TCC. However, no close relationship between TCC with adjacent CIS and invasive TCC was observed using hierarchical cluster analysis. Expression profiling of a series of biopsies from normal urothelium and urothelium with CIS lesions from the same urinary bladder revealed that the gene expression found in sTCC with surrounding CIS is found also in CIS biopsies as well as in histologically normal samples adjacent to the CIS lesions. Furthermore, we also identified similar gene expression changes in mTCC samples. We used a supervised learning approach to build a 16-gene molecular CIS classifier. The classifier was able to classify sTCC samples according to the presence or absence of surrounding CIS with a high accuracy. This study demonstrates that a CIS gene expression signature is present not only in CIS biopsies but also in sTCC, mTCC, and, remarkably, in histologically normal urothelium from bladders with CIS. Identification of this expression signature could provide guidance for the selection of therapy and follow-up regimen in patients with early stage bladder cancer. Keywords: Tissue group comparisons (+-CIS)

2005-09-19 | GSE3167 | GEO

Tobacco smoking transcriptional imprinting contributes to urothelial cancer

Project description:Smoking is a major risk factor for Urothelial carcinoma (UC). However the complex mechanisms, how smoking promotes carcinogenesis and tumour progression, remain obscure. A microarray based approached was therefore performed to detect the smoking derived gene expression alteration in non-malignant and malignant urothelial tissues from patients with superficial or invasive UC. Smoking enhanced cell migration and response to tissue damages. In non-malignant tissues smoking induced immune response and altered the cytoskeleton. In urothelial carcinoma, smoking altered extracellular and chromosome structures. Smoking affected tissues from patients with invasive carcinomamore strongly, up-regulating particularly growth factors and oncogenes in non-malignant tissue of patients with invasive but not with superficial carcinoma. In former smokers, comparable changes were seen in tissues form patients with invasive disease while they were minor or reversed in tissue of patients with superficial disease. Best but not complete tissue repair was suggestedfor non-malignant tissue from patients with superficial tumours. we used microarray techniques to define gene expression profile which may explain the contribution of smoking to urothelial carcinoma. Gene expression profiling using GeneChip HG-U133A Plus 2.0 (Affymetrix) was performed on non-malignant and carcinogenic urothelium from six patients with superficial and six with invasive urothelial carcinoma, each group including two current, former, and non-smokers.

2013-03-20 | E-GEOD-21142 | biostudies-arrayexpress

Transcription profiling of human urinary superficial transitional cell carcinoma with surrounding carcinoma in situ (CIS) lesions, without surrounding CIS lesions, and muscle invasive carcinoma

2007-11-28 | E-GEOD-3167 | biostudies-arrayexpress

Tobacco smoking transcriptional imprinting contributes to urothelial cancer

2013-03-20 | GSE21142 | GEO

MicroRNA and gene expression changes in human cerebral aneurysms [RNA-seq]

Project description:Cerebral Aneurysm tissues and superficial temporal arteries were compared through RNA-Seq to identify differentially expressed genes, and through microRNA microarray to identify differentially expressed microRNAs. Significant miR:gene pairs were identified for potential microRNA silencing regualtory effects. To study differentially expressed genes and microRNAs in cerebral aneurysms, we collected 7 cerebral aneurysm tisses and 10 superficial temporal artery (STA) tisses from 17 individuals. We performed RNA-Seq and microRNA microarray analysis on these samples, and compared them using STA as a control.

2016-02-24 | E-GEOD-66238 | biostudies-arrayexpress

Bulk RNA-seq of adult homeostatic microglia from 4 brain regions

Project description:To compare microglial regional heterogeneity, we generated bulk RNA-seq profiles of postnatal day 60 microglia, sorted by TMEM119+ (also CD45lowCD11b+), from cortex (CTX), cerebellum (CB), hippocampus (HIP), striatum (STR) regions. For each sample, 3000 microglia were FACS sorted into RLT lysis buffer for total RNA extraction, followed by Smart-seq library preparation and Illumina Nextseq (sequence depth 10-20 million per sample). Consistent with our scRNA-seq data, samples from 4 regions were highly correlated (R>0.99), and individual samples did not cluster according to tissue origins, suggesting striking similarities between homeostatic microglia from different brain regions. Moreover, we could not detect any differentially expressed genes (FDR < 0.05) between regions from the bulk samples. These data suggest that classical adult microglia with homeostatic signatures (e.g. Tmem119), as the most dominant microglial population in the healthy brain, have little transcriptomic heterogeneity across brain regions.

2018-12-31 | GSE123021 | GEO

ETV5 transcription program links BDNF and promotion of EMT at invasive front of endometrial carcinomas.

Project description:We have characterized the transcriptional program regulated by ETV5 at the invasive front of endometrial carcinomas, by comparing it with non-invasive areas of the same tumor samples. In total 12 samples; 6 superficial (non-invasive) tumor areas and the corresponding invasive tumor zones.

2014-11-05 | E-GEOD-57870 | biostudies-arrayexpress