12310 results for "cancer rna-seq"

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• Nasal MicroRNA during Bronchiolitis and Age 6y Asthma: MARC-35 Cohort

  We analyzed data from the 35th Multicenter Airway Research Collaboration (MARC-35) study to examine the relationship between nasal airway microRNAs (miRNAs) and the risk of developing childhood asthma. MARC-35 is a multicenter prospective cohort study which enrolled infants (age Structured interviews of the parents/guardians were performed by site investigators and clinical details were collected via emergency department and hospital inpatient chart reviews. Following the index hospitalization, trained study personnel conducted telephone interviews with parents/legal guardians at six-month intervals. Participants also underwent nasal swab sampling conducted by trained site personnel during the index bronchiolitis hospitalization and during an in-person follow-up examination at about age 6 years. Total RNA was extracted from nasal swab specimens using Trizol LS reagent (ThermoFisher Scientific, Waltham, MA) and the Direct-zol RNA Miniprep Kit (Zymo Research, Irvine, CA). Small RNA-seq was performed using the PerkinElmer NEXTFLEX® small RNA-seq v3 kit with Unique Dual Indexes (PerkinElmer, Waltham, MA) and sequenced on an Illumina NovaSeq6000 sequencer using an S2 50bp PE Flowcell (Illumina, San Diego, CA). Through dbGaP, limited de-identified phenotypic data (e.g., demographics) and RNA-seq FASTQ files will be made available. Among a cohort of infants hospitalized with bronchiolitis, we applied a small RNA sequencing approach and found miRNAs were associated with asthma risk factors, and highly expressed in lung tissue and immune cells (e.g., TH cells, neutrophils). They also played key roles in mechanisms relating to asthma, such as innate immunity, airway remodeling, and IgE regulation.

  Study phs003564

• Whole transcriptome profiling of liquid biopsies from tumour xenografted mouse models: validation cohort

  Total RNA sequencing (SMARTer Stranded Total RNA-Seq Kit v2) data of extracellular RNA (exRNA) from liquid biopsies of the validation PDX/CDX cohort

  Dataset EGAD00001009413

• Genomic, transcriptomic and epigenomic profiling of GCTB

  Giant cell tumor of bone is a rare bone neoplasm driven by oncogenic hotspot G34W/L/V mutations in the non-canonical histone variant H3.3. This study provides primary data of genomic (WGS), transcriptomic (RNA-seq) and epigenomic (histone mark ChIP-seq, WGBS, ATAC-seq) profiling of primary GCTB samples from several cohorts in Germany and Korea. We confirm the H3.3-G34 mutations as sole genetic drivers of GCTB, and describe the epigenetic alterations in mutant GCTB stromal cells.

  Study EGAS00001003730

• Mapping the lineage-retained antigen landscape in neuroblastoma

  Neuroblastoma is among the deadliest pediatric solid tumors, and relapse is frequently driven by lineage plasticity between adrenergic (ADRN) and mesenchymal (MES) cell states that escape conventional and targeted therapies. This project aims to identify tumor antigens that are retained across both lineage states and are therefore suitable for T-cell–based immunotherapy resistant to lineage switching, including non-canonical antigens derived from the "dark proteome." To support this aim, we generated paired RNA-sequencing and ribosome profiling (Ribo-seq) data from neuroblastoma cell lines, patient-derived tumor organoids, and primary patient tumors spanning ADRN and MES states. The RNA-seq data enable custom transcriptome assembly to recover non-reference and non-canonical transcripts, while Ribo-seq maps actively translated canonical and non-canonical open reading frames at codon resolution. Together these data provide the molecular substrate for systematic discovery of lineage-retained, translation-supported neuroblastoma antigens.

  Study EGAS00001008445

• Exploration of coding and non-coding variants in cancer using GenomePaint.

  GenomePaint (https://proteinpaint.stjude.org/genomepaint) is a dynamic visualization platform for whole-genome, whole-exome, transcriptome, and epigenomic data, featuring a novel design that captures the inter-relatedness between DNA variations and RNA expression. Regulatory non-coding variants can be inspected and discovered along with coding variants, and their functional impact further explored by examining 3D genome and/or ChIP-seq data generated from cancer cell lines. Further, GenomePaint correlates mutation and expression patterns with patient outcomes, and can display external data such as adult cancer datasets and user-provided custom tracks. We used GenomePaint to analyze multi-omics data from 3,652 pediatric cancers representing 16 histotypes, and demonstrate the visualization features through examples, including two that led to new insights into oncogenic mechanisms in pediatric cancer. The first is the discovery of a new class of pathogenic recurrent variants that cause aberrant splicing, disrupting the RING domain of CREBBP, a driver gene frequently mutated in relapsed pediatric leukemia. The second is the cis-activation of the MYC oncogene in a subset of B-lineage acute lymphoblastic leukemia (B-ALL) via duplication of the NOTCH1-MYC enhancer (N-ME), previously discovered only in T-lineage ALL. The regulatory impact of N-ME enhancer amplification was initially confirmed by allelic imbalance in published gene expression and ChIP-seq data and verified by additional Capture-C and fluorescence in situ hybridization data generated by follow-up experiments. These examples demonstrate the power of GenomePaint in enabling not only data visualization but also integrative genomic analysis that can lead to novel biological insight for follow-up experimental validation.

  Study EGAS00001004669

• RNA-seq of pcALCL

  RNA sequencing of frozen tumor biopsies from patients with primary cutaneous anaplastic large cell lymphoma. 12 samples. Illumina HiSeq 4000.

  Dataset EGAD00001006178

• rna-seq data of multiple myeloma patients

  6 samples from individuals with multiple myeloma with selective elimination of immunosuppressive T cells, rna sequencing

  Dataset EGAD00001006866

• RNA-seq data from the EUROBATS Project in four tissues: Fat, LCL, Skin and whole Blood.

  EUROBATS consists of RNA-seq data for female twins in lisphoblastoide cell lines (LCL), abdominal adipose tissue (Fat), abdominal skin tissue (Skin) and whole blood (Blood). The samples are a combination of MZ twins, DZ twins and singletons. The goal of the project is to find biomarkers related with the ageing process and cellular senescence.

  Study EGAS00001000805

• single-cell RNA-seq Case-Control study of children progressing to Type1 diabetes

  It was a single-cell RNA sequencing study on the PBMC samples from four Finnish children at risk of developing Type 1 diabetes and their gender age and HLA matched control children. All four Case children were positive for multiple islet specific autoantibodies and two of them also progressed to clinical disease during the follow up whereas the control children remain negative for all autoantibodies. Single-cell analysis confirmed some of the signatures obtained from the bulk data. It identified that high IL32 in case samples in the bulk RNA-seq was contributed mainly by activated T cells and NK cells. Trajectory analysis of the scRNA-seq data suggested that IL32 expression increased as the T cells moved towards activated state.

  Dataset EGAD00001005768

• SATB1 KO Treg and Teff cells: RNA-seq

  We expanded human Treg and CD4+ Teff cells for 9 days, re-activated the cells and performed CRISPR/Cas9 RNP-mediated KO at day 11. The cells were treated with a pro-inflammatory cytokine IL-12 on day 12. 4 days after editing, living cells were FACS-sorted, the RNA was extracted and prepared for RNA-seq. AAVS1 KO served as control. The transcription factors SATB1 and two other targets (included to increase the robustness of the dataset) were knocked-out. Three biological replicates have been applied.

  Dataset EGAD50000001277