9296 results for "cancer rna-seq"

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• RNA-Seq data from 34 CAF-S3 subset in human breast and ovarian cancers

  This dataset is related to publications Costa et al. Cancer Cell 2018 and Givel et al. Nat. Commun. 2018 which describe the identification of 4 Cancer Associated Fibroblasts (CAF) in breast and ovarian cancer. This dataset contains transcriptomic profiles obtained by RNA-Seq of 34 CAF-S3 samples from breast and ovarian Tumors.

  Dataset EGAD00001004810

• Cancer RNA-seq consisting of FASTQ paired-end reads from melanoma and lung cancer sample

  Cancer RNA-seq consisting of FASTQ paired-end reads from 6 individuals (4 melanoma, 1 lung cancer). RNASEQ was performed on illumina, Truseq capture kit, 40M-80M clusters. 2 Melanoma datasets contain reads from 1 tumor region and from a tumor derived cell line 2 Melanoma, 1 Colon and 1 lung datasets contain each reads from a single region.

  Dataset EGAD00001007951

• RNA-seq of Liver Cancer

  The French ICGC project on liver tumors is coordinated by Pr Jessica Zucman-Rossi and funded by Inca (French Institute for Cancer). The aim of the present project is to identify the catalog of somatic and germline mutations in liver tumors using whole genome (WGS) and whole exome sequencing (WGS), integrated with DNA methylation and RNA sequencing (RNA-seq) data. The present series corresponds to 161 RNA-seq samples from tumors with matched WES or WGS. Hepatocellular carcinoma (HCC) accounts for more than 90% of liver cancers, and is a major health problem. It is the 3rd cause of cancer-related mortality. Advances in genomic analyses have formed a comprehensive understanding of different underlying pathobiological layers resulting in hepatocarcinogenesis. Thus, the development of next-generation sequencing technologies has made it possible to generate more comprehensive catalogues of somatic alteration events (single nucleotide substitutions, structural variations, and epigenetic changes) in liver cancer genome than ever before.

  Study EGAS00001002879

• BarcUVa-Seq (Biology of Colorectal Cancer Risk Enhancers)

  This study was designed to generate a cohort of normal colon mucosal biopsies from the "average risk" population. Its primary aims included the development of an RNA-seq dataset of normal colon mucosal biopsies across three colon regions (ascending, transverse and descending). Further, we aimed to provide plausible prioritization to genetic components of colorectal cancer risk by employing methods such as expression quantitative trait loci analysis. In doing so, we provide a novel reference dataset for the study of colon cancer risk in the healthy colon epithelium. RNA-seq was performed in batches on an Illumina HiSeq 2500 (batch 1-7) or NovaSeq 6000 (batch 8) on a total of 463 samples. Matched genotyping data was generated on the Illumina OncoArray BeadChip. Phasing was carried out using Minimac 3 and imputation was performed using The Haplotype Reference Consortium panel on the Michigan Imputation Server.Genotype and RNA-seq data will be made available in dbGaP.

  Study phs003338

• RNAseq data to study PRPF6 regulated splice forms in colon cancer cell lines

  Alternative splicing plays critical roles in differentiation, development, and cancer (Pettigrew et al., 2008; Chen and Manley, 2009). The recent identification of specific spliceosome inhibitors has generated interest in the therapeutic potential of targeting this cellular process (van Alphen et al., 2009). Using an integrated genomic approach, we have identified PRPF6, an RNA binding component of the pre-mRNA spliceosome, as an essential driver of oncogenesis in colon cancer. Importantly, PRPF6 is both amplified and overexpressed in colon cancer, and only colon cancer cells with high PRPF6 levels are sensitive to its loss. Our data clearly point to an important role for PRPF6 in colon cancer growth and suggest that a better understanding of its role in alternative splicing in colon cancer is warranted. To determine the specific alternative splice forms that PRPF6 regulates in colon cancer, we plan three experiments: 1. The first involves knocking down expression of PRPF6 in two different cancer cell lines with 3 different siRNAs, and then completing RNA-seq to determine the gene expression changes that occur relative to a non-targeting control siRNA. Because of the role for PRPF6 in pre-mRNA splicing, we especially want to quantify the changes in splice-specific forms of all genes genome-wide to identify genes whose splicing is altered upon PRPF6 knockdown. 2. The second involves immunoprecipitating PRPF6 from two different cancer cell lines and isolating any RNA that is bound to PRPF6, since PRPF6 is an RNA-binding protein. We then want to carry out RNA-seq to identify which RNA molecules co-immunoprecipitated with PRPF6. This will help us determine possible functions for PRPF6 in regulating colon cancer growth. 3. The third involves overexpressing PRPF6 in cell lines and then carrying out RNA-seq to identify any changes in splice-specific gene expression. This will allow us to determine whether increased PRPF6 expression is sufficient to drive alternative splicing changes.

  Study EGAS00001000761

• Lung cancer RNA Seq Data Access Committee

  Dac EGAC00001003317

• Profiling RNA Translation in Pediatric Medulloblastoma

  This study provides RNA-sequencing and ribosome profiling data for patient-derived cell lines and patient tissue samples for children with medulloblastoma. Ribosome profiling is a variant protocol of RNA-sequencing that directly sequences ribosome-bound RNA fragments only. Associated RNA-seq and Ribo-seq data obtained separately for some cancer cell lines can be found on the NCBI SRA as PRJNA957428. Samples were processed for poly-A mRNA sequencing using the Roche Kapa Kit. Ribosome profiling was performed as described in the manuscript referenced below (Hofman et al.) and based on the article by McGlincy et al., Methods (2017). Ribo-seq data were analyzed for sample quality using RiboseQC (Calviello, Nat Struct Mol Biol, 2020). These data were used to quantify P-sites of open reading frames. RNAseq and ribo-seq data were integrated and compared to determine translational efficiency values using TPM (Ribo-seq) over TPM (RNA-seq) as the metric. Ribo-seq data on 14 samples and RNAseq data on 21 samples derived from 16 fresh-frozen surgical samples for medulloblastoma and 5 autopsy samples are available through dbGaP. A second cohort of 4 medulloblastoma samples (RNAseq, Ribo-seq) related to this study can be found on the EGA at EGAS00001007426.

  Study phs003446

• RNA-seq data of breast cancer HCI011 model from Ros et al (2020)

  RNA-seq data of the HCI011 and HCI011R models, GDC032 treated and control (total of 21 samples), from the paper: FOXM1 is a biomarker of resistance to PI3Kα inhibition in ER+ breast cancer that is detectable using metabolic imaging (Ros et al, 2020)

  Dataset EGAD00001006245

• bulk RNA-Seq of colorectal cancer patient samples

  Study EGAS00001006666

• BPH Tissues for Cell Culture and Analysis - Spatial Transcriptomics Identifies Candidate Stromal Drivers of Benign Prostatic Hyperplasia

  The study goal was to apply spatial transcriptomics to identify candidate stromal drivers of benign prostatic hyperplasia (BPH). Laser microdissection (LCM) with RNA-seq of the stroma adjacent to hyperplastic and budding BPH ducts identified secreted factors including IGF1 and CXCL13. Single‐cell RNA sequencing (scRNA‐seq) revealed their co-expression from BPH fibroblasts. Additional studies sought to define similarities to embryonic prostate development, as well as therapeutic applications. Subjects were those who underwent prostatectomy for prostate or bladder cancer, with concurrent BPH, and who consented to use of surplus tissue for research studies. LCM/RNA-seq studies included 5 patients, and scRNA-seq studies included 1 patient. Phenotypic data submitted to this study include patient age, sex, prostate size, and international prostate symptom score (IPSS). Sequencing was done on an Illumina NovaSeq 6000 (LCM/RNAseq) and Illumina HiSeq X-Ten (scRNA-seq). The data processing pipeline utilized Kallisto (LCM/RNA-seq) and Cell Ranger (scRNA-seq). Data submitted are raw sequencing (Fastq) files.

  Study phs003477