Ontology highlight
ABSTRACT:
PROVIDER: PRJEB61514 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:The aim of this study is to identify prognostic gene expression signatures associated with two molecularly distinct subtypes of colorectal cancer. Samples were taken from colorectal cancers in surgically resected specimens in 96 colorectal cancer patients. The expression profiles were determined using Affymetrix Human Genome U133Plus 2.0 arrays. This is a test set for validation of prognostic gene expression signature that was developed from GSE14333. All data were normalized by using the RMA method (affy package in R/Bioconductor).
2011-06-20 | GSE29971 | GEO
Project description:Colorectal cancer is the third most common and the second deadliest tumour type in both sexes world-wide. To understand the functional and prognostic impact of cancer-causing somatic mutations, we analysed the whole genomes and transcriptomes of 1,063 primary colorectal cancers in a population-based cohort with long-term follow-up. High quality transcriptome sequences from 1,063 tumours and 120 tissue normals enabled integration analyses of gene mutations and gene expression levels.
2024-05-15 | E-MTAB-12862 | biostudies-arrayexpress
Project description:Samples were taken from colorectal cancers in surgically resected specimens in 84 colorectal cancer patients. The expression profiles were determined using Affymetrix Human Genome U133 Plus 2.0 arrays. Comparison between the sample groups allow to identify a set of discriminating genes between MSI and MSS cancers and, furthermore, to determine the distinct characteristics of proximal and distal MSI cancers. Experiment Overall Design: Eighty-four colorectal cancer patients who had undergone surgical resection of colorectal cancer were studied. To identify molecular signatures of MSI cancers, gene expression profiles were compared between MSI and MSS cancers. Next, we examined the difference in gene expression profiles between proximal and distal MSI cancers.
2008-06-13 | E-GEOD-4554 | biostudies-arrayexpress
Project description:For the current study we performed whole genome expression profiling on two independent cohorts of clinically annotated, localized Ewing sarcoma (ES) tumors in an effort to identify and validate prognostic gene signatures. ES specimens were obtained from the Children’s Oncology Group (COG) and whole genome expression profiling performed using Affymetrix Human Exon 1.0 ST arrays. Lists of differentially expressed genes between survivors and non-survivors were used to identify prognostic gene signatures
2014-11-11 | GSE63155 | GEO
Project description:For the current study we performed whole genome expression profiling on two independent cohorts of clinically annotated, localized Ewing sarcoma (ES) tumors in an effort to identify and validate prognostic gene signatures. ES specimens were obtained from the Children’s Oncology Group (COG) and whole genome expression profiling performed using Affymetrix Human Exon 1.0 ST arrays. Lists of differentially expressed genes between survivors and non-survivors were used to identify prognostic gene signatures
2014-11-11 | GSE63156 | GEO
Project description:Medullary breast cancers (MBC) display a basal profile, but a favorable prognosis. We hypothesized that a previously published 368-gene expression signature associated with MBC might serve to define a prognostic classifier in basal cancers. We collected public gene expression and histoclinical data of 2145 invasive early breast adenocarcinomas. We developed a Support Vector Machine (SVM) classifier based on this 368-gene list in a learning set, and tested its predictive performances in an independent validation set. Then, we assessed its prognostic value and that of six prognostic signatures for disease-free survival (DFS) in the remaining 2034 samples. The SVM model accurately classified all MBC samples in the learning and validation sets. A total of 466 cases were basal across other sets. The SVM classifier separated them into two subgroups, subgroup 1 (resembling MBC) and subgroup 2 (not resembling MBC). Subgroup 1 exhibited 71% 5-year DFS, whereas subgroup 2 exhibited 50% (p=9.93E-05). The classifier outperformed the classical prognostic variables in multivariate analysis, conferring lesser risk for relapse in subgroup 1 (HR=0.52, p=3.9E-04). This prognostic value was specific to the basal subtype, in which none of the other prognostic signatures was informative. The IPC series contained frozen tumor samples obtained from 266 early breast cancer patients who underwent initial surgery in our institution between 1992 and 2004. They included 227 cases previously reported {Finetti, 2008 #1758} and 39 additional cases, all similarly profiled using Affymetrix U133 Plus 2.0 human oligonucleotide microarrays as previously described {Finetti, 2008 #1758}. The study was approved by the IPC review board, and informed consent was available for each case. Gene expression data of 266 BCs were quantified by using whole-genome DNA microarrays (HG-U133 plus 2.0, Affymetrix).
2010-05-05 | E-GEOD-21653 | biostudies-arrayexpress
Project description:Prostate cancer is the most common malignancy in men. Yet, the modest benefit of treatment highlights the unmet need for prognostic biomarkers in prostate cancer (1). Few large prostate oncogenome resources currently exist that combine the molecular and clinical outcome data necessary for prognostic discovery. To determine the extent to which genomic aberrations reflect the risk of prostate cancer-specific outcomes, we profiled more than 100 primary prostate cancers with long-term follow-up for genome-wide copy number alterations (CNA). We also updated the long-term clinical outcome (median 8 years) of an additional independent cohort of 181 primary prostate cancers that we previously profiled for CNA and expression changes (2). Together, we found that CNA burden across the genome, defined as the percent of the tumor genome affected by CNA, is prognostic for recurrence and metastasis in these two cohorts. This prognostic significance of CNA is independent of Gleason grade, a major existing histopathological prognostic variable in prostate cancer. Moreover, in intermediate-risk Gleason 7 prostate cancers that show a wide range of outcomes, CNA burden is also prognostic for biochemical recurrence, independent of prostate-specific antigen or nomogram score. CNA burden therefore has the potential to stratify patients by their risk of recurrence in an otherwise intermediate risk subpopulation. We further demonstrate that CNA burden can be established in diagnostic FFPE needle biopsies using low-input whole genome sequencing. Together, this work highlights the potential of oncogenomics to identify useful and clinically amenable prognostic factors that may inform prostate cancer outcome and treatment.
2014-06-30 | GSE54691 | GEO
Project description:By the use of whole genome transcription analysis, we aimed to develop a gene expression classifier to increase the likelihood of identifying stage II colorectal cancer (CRC) samples with a poor prognostic outcome. Gene expression measurement were measured by the GeneChip® Human Exon 1.0 ST Arrays from Affymetrix. We analyzed genome-wide expression at the gene-level for an independent series of colorectal cancer tissue biopsies using the Affymetrix Human Exon 1.0 ST platform.
2012-01-06 | E-GEOD-29638 | biostudies-arrayexpress
Project description:By the use of whole genome transcription analysis, we aimed to develop a gene expression classifier to increase the likelihood of identifying stage II colorectal cancer (CRC) samples with a poor prognostic outcome. Gene expression measurement were measured by the GeneChip® Human Exon 1.0 ST Arrays from Affymetrix.
2012-01-06 | GSE29638 | GEO
Project description:Colorectal cancer is the third most common and the second deadliest tumour type in both sexes world-wide. To understand the functional and prognostic impact of cancer-causing somatic mutations, we analysed the whole genomes and transcriptomes of 1,063 primary colorectal cancers in a population-based cohort with long-term follow-up. High quality transcriptome sequences from 1,063 tumours and 120 tissue normals enabled integration analyses of gene mutations and gene expression levels.
2023-09-25 | PRJEB61514 | EVA