11801 results for "cancer rna-seq"

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• scRNA and spatial-seq of human skin wound healing

  Human skin acute wound healing contains 12 samples of scRNA-seq and 16 samples of spatial transcriptomics, Human skin chronic wound data contains 9 samples scRNA-seq.

  Dataset EGAD50000000813

• Neuroblastoma Cell Line Circle-seq

  We generated Circle-seq data for 12 neuroblastoma cell lines to describe the landscape of extrachromosomal circular DNA in neuroblastoma.

  Study EGAS00001004796

• FASTQ Files for Human LV H3K27ac ChIP-seq

  This dataset contains 70 human LV H3K27ac ChIP-seq paired-end FASTQ files. The sequencing was performed using Illumina Hiseq 4000.

  Dataset EGAD00001004945

• ChIP-seq for GOF p53

  This dataset includes ChIP-seq data from two cell lines (HKCI-11 (GOFp53) and MIHA(WT p53)). All the experiments were performed on Illumina HiSeq 2000 platform with raw reads stored in fastq format.

  Dataset EGAD00001005449

• RNA Splicing Dysregulation in the Pathogenesis of Chronic Lymphocytic Leukemia

  RNA splicing dysregulation is a hallmark of cancers, promoting the onset and progression of disease. In chronic lymphocytic leukemia (CLL), spliceosome mutations leading to aberrant splicing occurs in approximately 20% of patients. However, the underlying mechanism for splicing defects in spliceosome unmutated CLL cases remains elusive. To discover posttranscriptional regulation of RNA splicing, we performed RNA sequencing using RNA derived from B cells from 6 healthy donors and 36 CLL patients, along with MAZTER sequencing from 9 CLL patients (8 matched with RNA sequencing cohort) and B cells from 5 healthy donors. We found that CLL cells have dysregulated m6A modification sites on transcripts from splicing factors, suggesting a critical role of m6A modification in RNA splicing dysregulation.

  Study phs003191

• Transcriptome of CD4+ T cells and CD8+ T cells in glioblastoma multiforme

  Patients included in this study were over 18 years of age and had a histology-confirmed diagnosis of glioblastoma multiforme (GBM). Exclusion criteria were the previous administration of any anti-tumor therapy including radiation therapy. All patients gave written informed consent. The study was approved by the local ethics committee (TUM Medical school) and conducted following the Declaration of Helsinki. During resection of the tumors, tumor tissue and tissue from normal appearing brain within the operative channel was collected. Blood was drawn during the surgical procedure. Single cell suspensions were prepared from the tumor tissue, the normal appearing brain, and the blood. CD4+ T cells and CD8+ T cells were sorted by flow cytometry. Only patients with a complete set of specimens (CD4+ tumor infiltrating lymphocytes (TIL), CD8+ TIL, CD4+ T cells from normal appearing brain, CD8+ T cells from normal appearing brain, blood-derived CD4+ and CD8+ T cells) containing a minimum of 1000 cells in each sorted sample were further analyzed (n=9). Total RNA was isolated from sorted cell populations using the RNAeasy Plus micro kit (Qiagen, 74034). Quality and integrity of total RNA was controlled on a Bioanalyzer 2100 (Agilent Technologies). Library preparation for bulk-sequencing of poly(A)-RNA was done as described previously (Parekh et al., 2016). Briefly, barcoded cDNA of each sample was generated with a Maxima RT polymerase (ThermoFisher Scientific, EP0742) using oligo-dT primer containing barcodes, unique molecular identifiers (UMIs) and an adaptor. Ends of the cDNAs were extended by a template switch oligo (TSO) and full-length cDNA was amplified with primers binding to the TSO-site and the adaptor. NEB UltraII FS kit was used to fragment cDNA. After end repair and A-tailing, a TruSeq adapter was ligated and 3'-end-fragments were finally amplified using primers with Illumina P5 and P7 overhangs. In comparison to previous descriptions (Parekh et al., 2016), the P5 and P7 sites were exchanged to allow sequencing of the cDNA in read 1 and barcodes and UMIs in read 2 to achieve a better cluster recognition. The library was sequenced on a NextSeq 500 (Illumina) with 59 cycles for the cDNA in read 1 and 16 cycles for the barcodes and UMIs in read 2. Data were processed using the published Drop-seq pipeline (v1.0) to generate sample- and gene-wise UMI tables (Macosko et al., 2015). Reference genome (GRCh38) was used for alignment. Transcript and gene definitions were used according to the Genecode Annotation Version 35.

  Study EGAS50000000156

• Melanoma and Cancer-Associated Fibroblast Short-Term Cultures Derived from Patient Metastases

  The purpose of this study was to obtain tissue specimens derived from patients with melanoma to generate research tools to advance our understanding of the genetics, pathogenesis, and therapeutics of melanoma. Briefly, tissue was obtained from metastatic lesions and used to generate clonal primary cell lines from melanoma cells and fibroblasts from the tumor microenvironment. RNA was extracted from low passage cell lines using Trizol reagent. cDNA libraries were prepared using the TruSeq mRNA sample preparation kit, v2 (Illumina) and sequenced on the HiSeq 2000 platform (Illumina). The submitted files are bam files that contain both unaligned and aligned reads (human genome, build hg19).

  Study phs001115

• Dynamics of tumor ecosystems and microbiome in response to neoadjuvant atezolizumab, bevacizumab, and FOLFOX treatment in patients with unresectable colorectal cancer with liver metastasis

  This study aims to explore the effects of neoadjuvant atezolizumab, bevacizumab, leucovorin, 5-fluorouracil, and oxaliplatin (ABFOLFOX) in patients with unresectable colorectal liver metastases (CRLM), focusing on the molecular dynamics of tumor ecosystems (TE) of CRLM and their impact on treatment outcomes. The study comprises two cohorts with CRLM tissue samples analyzed with RNA sequencing and immunohistochemical staining: cross-sectional cohort A (n = 60, CRLM treated with or without neoadjuvant chemotherapy) and prospective cohort B (n = 20 with serial sampling and treated with ABFOLFOX). Shotgun metagenomic sequencing was done for stool samples from cohort B.

  Study EGAS50000000677

• Dataset for NSCLC-RNA

  Rare cancer sequencing data of 45 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008846

• Dataset for liposarcoma-RNA

  Rare cancer sequencing data of 40 runs in tumor/control pairs, which were uploaded to umbrella studies. The sequencing was always paired

  Dataset EGAD00001008854