Gene level expression profiling of colorectal cancer tissue samples (test sample series)
Ontology highlight
ABSTRACT: This series is part of a larger series (GSE24549) of colorectal cancer tissue samples analyzed for global gene expression. The expression measures were used to develope a gene signature for prediction of prognosis in stage II and III colorectal cancer. Gene expression profiling of colorectal cancer tissue samples for prognosis prediction.
Project description:These samples have been analyzed for global alternative splicing variation on exon-level expression data using the FIRMA algorithm. We have identified and described transcriptome instability as a genome-wide, pre-mRNA splicing related characteristic of solid cancers. This Series consists of 19 normal colonic mucosa samples from colorectal cancer patients, and is an amendment to a larger series of colorectal cancer and adjacent normal colonic mucosa samples analyzed for gene expression at the exon-level (GSE24550).
Project description:This SuperSeries is composed of the following subset Series: GSE24549: Exon level expression profiling of colorectal cancer tissue samples (test sample series). GSE24550: Exon level expression profiling of colorectal cancer tissue samples (validation sample series). Refer to individual Series
Project description:Colorectal cancer is a heterogeneous disease molecularly characterized by inherent genomic instabilities, chromosome instability and microsatellite instability. In the present study we propose transcriptome instability as an analogue to genomic instability on the transcriptome level. Exon microarray data from two independent series of altoghether 160 colorectal cancer tissue samples was used for global alternative splicing detection using the FIRMA algorithm (aroma.affymetrix). The sample-wise amounts of these alternative splicing scores exceeding a defined threshold (deviating exon usage amounts) were summarized to provide the basis for description of transcriptome instability. This characteristic was shown to be associated with splicing factor expression levels and patient survival in both independent sample series. We analyzed genome-wide expression at the exon-level for two independent series of colorectal cancer tissue biopsies using the Affymetrix Human Exon 1.0 ST platform. This series of samples represents the validation series.
Project description:Colorectal cancer is a heterogeneous disease molecularly characterized by inherent genomic instabilities, chromosome instability and microsatellite instability. In the present study we propose transcriptome instability as an analogue to genomic instability on the transcriptome level. Exon microarray data from two independent series of altoghether 160 colorectal cancer tissue samples was used for global alternative splicing detection using the FIRMA algorithm (aroma.affymetrix). The sample-wise amounts of these alternative splicing scores exceeding a defined threshold (deviating exon usage amounts) were summarized to provide the basis for description of transcriptome instability. This characteristic was shown to be associated with splicing factor expression levels and patient survival in both independent sample series. We analyzed genome-wide expression at the exon-level for two independent series of colorectal cancer tissue biopsies using the Affymetrix Human Exon 1.0 ST platform. This series of samples represents the test series.
Project description:This series is part of a larger series (GSE24549) of colorectal cancer tissue samples analyzed for global gene expression. The expression measures were used to develope a gene signature for prediction of prognosis in stage II and III colorectal cancer.
Project description:This is the expression dataset for two studies: 1) Characterization of visceral and subcutaneous adipose tissue transcriptome and biological pathways in pregnant and non-pregnant women: Evidence for pregnancy-related regional-specific differences in adipose tissue and 2) Characterization of visceral and subcutaneous adipose tissue transcriptome in pregnant women with and without spontaneous labor at term: Implication of alternative splicing in the metabolic adaptations of adipose tissue to parturition. The studies compare expression profiles and exon usage between adipose tissue regions and groups of women (pregnant vs non-pregnant) and in labor vs not in labor. Paired design for regional differences within groups of women (identified by Subject _# in the title), and unpaired design between groups of women.
Project description:Long non-coding RNAs show highly tissue and disease specific expression profiles. We analyzed prostate cancer and normal adjacent prostate samples to identify cancer-specific transcripts and found 334 candidates, of which 15 were validated by RT-PCR. 8 Prostate cancer samples from radical prostatectomies, 12 samples from normal adjacent prostate, 12 samples from lymph node metastasis and 10 samples from transurethral resection of the prostrate were included in this study. These samples contained more than 70% cancer and less than 30% stromal tissue.
Project description:Gene Expression profiling of 170 newly diagnosed Multiple Myeloma patients Gene Expression profiling of Multiple Myeloma Cells from Healthy donors and Multiple myeloma patients were profiled using Affymetrix Exon-1.0 ST microarrays
Project description:We assessed alternative splicing in breast cancer through global profiling of transcriptomes of basal and luminal subtype cell lines using Affymetrix Human Junction Array.
Project description:Patients with oral preneoplastic lesion (OPL) have high risk of developing oral cancer. Although certain risk factors such as smoking status and histology are known, our ability to predict oral cancer risk remains poor. The study objective was to determine the value of gene expression profiling in predicting oral cancer development. Gene expression profile was measured in 86 of 162 OPL patients who were enrolled in a clinical chemoprevention trial that used the incidence of oral cancer development as a prespecified endpoint. The median follow-up time was 6.08 years and 35 of the 86 patients developed oral cancer over the course. Gene expression profiles were associated with oral cancer-free survival and used to develope multivariate predictive models for oral cancer prediction. We developed a 29-transcript predictive model which showed marked improvement in terms of prediction accuracy (with 8% predicting error rate) over the models using previously known clinico-pathological risk factors. Based on the gene expression profile data, we also identified 2182 transcripts significantly associated with oral cancer risk associated genes (P-value<0.01, single variate Cox proportional hazards model). Functional pathway analysis revealed proteasome machinery, MYC, and ribosomes components as the top gene sets associated with oral cancer risk. In multiple independent datasets, the expression profiles of the genes can differentiate head and neck cancer from normal mucosa. Our results show that gene expression profiles may improve the prediction of oral cancer risk in OPL patients and the significant genes identified may serve as potential targets for oral cancer chemoprevention. Gene expression profile was measured in 86 of 162 OPL patients who were enrolled in a clinical chemoprevention trial that used the incidence of oral cancer development as a prespecified endpoint. The median follow-up time was 6.08 years and 35 of the 86 patients developed oral cancer over the course. Gene expression profiles were associated with oral cancer-free survival and used to develope multivariate predictive models for oral cancer prediction.