Bladder tumour stage classification
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE89 | GEO | 2002/12/08
SECONDARY ACCESSION(S): PRJNA85471
REPOSITORIES: GEO
Similar Datasets
Project description:Bladder tumours used to construct a tumour stage classifier
2007-09-28 | E-GEOD-89 | biostudies-arrayexpress
Project description:Expression profiling of superficial bladder tumours to delineate the expression pattern differences between non-recurring and recurring tumours. Keywords = bladder cancer, superficial, recurrence, prediction Keywords: other
2002-12-08 | GSE88 | GEO
Project description:In this study gene expression profiles for 307 cases of advanced bladder cancers were compared to molecular phenotype at the tumor cell level. TUR-B tissue for RNA extraction was macrodissected from the close vicinity of the tissue sampled for immunohistochemistry to ensure high-quality sampling and to minimize the effects of intra-tumor heterogeneity. Despite excellent agreement between gene expression values and IHC-score at the single marker level, broad differences emerge when samples are clustered at the global mRNA versus tumor cell (IHC) levels. Classification at the different levels give different results in a systematic fashion, which implicates that analysis at both levels is required for optimal subtype-classification of bladder cancer.
2017-03-13 | GSE83586 | GEO
Project description:Expression data derived from this analysis was used for transcriptional subtyping and to compare expression features of genomic subgroups In this dataset, we include the expression data obtained from 113 stage Ta and 104 stage T1 bladder tumours
2021-02-22 | GSE163209 | GEO
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
Project description:Expression data derived from this analysis was used to compare expression signatures between genomic subgroups identified from DNA copy number analysis. In this dataset, we include the expression data obtained from 79 stage Ta primary bladder tumours
2018-06-15 | GSE93527 | GEO
Project description:Previous studies successfully revealed molecular characteristics of bladder cancers, dealing with non-muscle invasive bladder cancer and muscle invasive bladder cancer, separately. At the molecular level, however, there is a great need to aggregate these subtypes, which may share biological characteristics. This study aimed to identify distinct molecular subtypes of BC and the clinical and/or biological characteristics of each subtype. We used seven gene expression data sets for bladder cancer, which included data from 118 primary bladder cancer samples and 27 recurrent bladder tumor tissues from the Yonsei University Severance Hospital. Hierarchical clustering revealed four molecular subtypes of BC with different clinical outcomes: class 1 with low-grade NMIBC and the best prognosis; class 2 characterized by active FGFR3 and inhibited immune response pathways; class 3 with high-grade NMIBC and the worst progression-free survival; and class 4 mainly comprised of MIBC along with EMT activation. By applying the classifier based on these characteristics, we stratified all BC samples into newly identified molecular subtypes. When comparing previously reported subtypes, our subtypes well agreed with their molecular characteristics regardless of breast cancer-based biology, and showed a strong prognostic relevance in class 3. Integrative analysis of mutation and gene expression suggested that class 3 may have the potential benefit from anti-PD-L1 immunotherapy. Our classifier, constructed by NMIBC and MIBC integration, successfully stratified BC patients into distinct subtypes with different clinical outcomes and a possible treatment option.
2018-10-03 | GSE120736 | GEO
Project description:To develop a classifier based on microRNAs for primary tumor site identification of liver core biopsies and to explore the influence of surrounding normal liver tissue on classification. Tissue samples from 333 patients, corresponding to one of the following ten assay classes, were obtained from archives of the pathology department, Copenhagen University Hospital, Rigshospitalet, Denmark: Lung cancer, breast cancer, gastric/cardia cancer, colorectal cancer, bladder cancer, pancreatic cancer, hepatocellular carcinoma, cholangiocarcinoma, squamous cell cancers of different origin, and normal liver tissue
2013-10-19 | E-GEOD-51429 | biostudies-arrayexpress
Project description:Childhood acute lymphoblastic leukemia (ALL) comprises a large group of genetic subtypes with a favorable prognosis characterized by a TEL-AML1-fusion, hyperdiploidy (>50 chromosomes) or E2A-PBX1 fusion and a smaller group with unfavorable outcome characterized by either a BCR-ABL-fusion, MLL-rearrangement or T-ALL. About 25% of precursor B-ALL are currently genetically unclassified and have an intermediate prognosis. The present study used genome-wide strategies to reveal new biological insights and advance the prognostic classification of childhood ALL. A double-loop cross validation was used to construct a classifier based on gene expression in ALL cells from 190 newly diagnosed cases (COALL cohort, GEO GSE13425) with a prediction accuracy of 90%. T-ALL, TEL-AML1-positive, hyperdiploid and E2A-rearranged cases were identified with 100% sensitivity and ≥94% specificity. The classifier accuracy was confirmed in an independent cohort of 107 cases (87.9%, DCOG cohort, GEO GSE13351). Keywords: gene expression study for classification of ALL subtypes
2009-01-26 | GSE13425 | GEO
Project description:A Cartes dM-^RIdentite des Tumeurs (CIT) project from the french Ligue Nationale Contre le Cancer (http://cit.ligue-cancer.net/). Seven transcriptome datasets corresponding to a new series of 85 MIBC (Muscle-invasive bladder cancer) and six publicly-available series (298 MIBC) were analyzed. Tumors were classified by consensus clustering. Overall survival was determined from Kaplan-Meier curves. The role of the EGFR pathway was investigated by pathway bioinformatics analysis, determination of the expression levels of its components (by microarray, RT-qPCR and western blot) and of EGFR copy number by CGH arrays. A 40-gene transcriptomic classifier was used to identify basal-like cell lines and a basal-like mouse model. Please note M-^QMIBC molecular subtype': tumors classification using consensus clustering.
2014-06-01 | E-MTAB-1803 | biostudies-arrayexpress