11925 results for "cancer rna-seq"

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• 10x Genomics VDJ single cell sequencing and 10x Genomics sc RNA-Seq (5 individuals/17 VDJ runs,19 scRNA runs))

  single cell RNAseq and TCR sequencing data of 5 individuals. 10x Genomics VDJ single cell sequencing (17 runs) and 10x Genomics sc RNA-Seq (19 runs). The VDJ sequencing was done on a Nextseq 550 using the Chromium Single Cell VDJ Reagent Kit. The RNA-Seq was done either on HiSeq4000 or NovaSeq 6000 using the Chromium Single Cell 5 Reagent Kit.

  Dataset EGAD00001009986

• Transcriptional and DNA binding profiling of CEBPE in REH acute lymphoblastic leukaemia cells using shRNA RNA-Seq and ChIP-Seq

  Dac EGAC00001000891

• Tumorigenic role of Musashi-2 in aggressive mantle cell lymphoma

  SOX11 is overexpressed in the highly aggressive conventional mantle cell lymphoma (MCL) but rarely in the indolent non-nodal MCL subtype. We performed RNA-seq of SOX11+ and SOX11- MCLs to search for SOX11-regulated genes that contributed to MCL clinical evolution, identifying Musashi-2 (MSI2) stem cell-related gene. By performing RNA-seq upon MSI2 knockdown or inhibition in MCL, we identified MSI2 as a potential target to inhibit drug resistance in aggressive MCLs.

  Study EGAS00001006613

• Evaluation of protocols for rRNA depletion-based RNA sequencing of nanogram inputs of mammalian total RNA

  Next generation RNA-Sequencing (RNA-seq) is a flexible approach that can be applied to e.g. global quantification of transcript expression, the characterization of RNA structure such as splicing patterns and profiling of expressed mutations. Many RNA-seq protocols require up to microgram levels of total RNA input amounts to generate high quality data, and thus remain impractical for the limited starting material amounts typically obtained from rare cell populations, such as those from early developmental stages or from laser micro-dissected clinical samples. Here, we present an assessment of the contemporary ribosomal RNA depletion-based protocols, and identify those that are suitable for inputs as low as 1-10 ng of intact total RNA and 100-500 ng of partially degraded RNA from formalin-fixed paraffin-embedded tissues.

  Dataset EGAD00001005316

• IBC RNA-seq

  RNA sequencing of IBC patients

  Dataset EGAD00001011649

• Colorectal Microenvironment Spatial Mapping

  In colorectal cancer (CRC), interactions between tumor and immune cells within the tumor microenvironment are critical drivers of tumorigenesis, immune evasion, prognosis, and therapeutic response. Therefore, a deeper understanding of the spatial organization of cancer and immune cells within the CRC microenvironment is urgently needed. In this study, we aimed to characterize distinct cell populations and patterns of spatial gene expression associated with CRC tumorigenesis and immune infiltration. Spatial transcriptomics (ST) was performed on CRC and adjacent normal tissues to investigate the patterns of gene expression. Integrative ST data and publicly available single-cell RNA sequencing (scRNA-seq) data analysis tools were used for data integration, dimension reduction, clustering, and differential expression analysis. This study identified novel immunosuppressive cell populations and immune regulatory factors in CRC. Further functional validation of these findings will be essential to assess their potential as therapeutic targets.

  Study EGAS00001008254

• A Unifying Paradigm for Transcriptional Heterogeneity and Squamous Features in Pancreatic Ductal Adenocarcinoma

  Pancreatic cancer expression profiles largely reflect a classical or basal-like phenotype. The extent to which these profiles vary within a patient is unknown. We integrated evolutionary analysis and expression profiling in multiregion sampled metastatic pancreatic cancers, finding that squamous features are the histologic correlate of an RNA-seq defined basal-like subtype. In patients with coexisting basal/squamous and classical/glandular morphology, phylogenetic studies revealed that squamous morphology represented a subclonal population in an otherwise classical/glandular tumor. Cancers with squamous features were significantly more likely to have clonal mutations in chromatin modifiers, intercellular heterogeneity for MYC amplification, and entosis. These data provide a unifying paradigm for integrating basal-type expression profiles, squamous histology, and somatic mutations in chromatin modifier genes in the context of clonal evolution of pancreatic cancer.  

  Study EGAS00001003974

• Single cell Human Kidney ADPKD

  This ADPKD project has 3 different experiments, 7 different single cell RNA-seq data, 5 different single nuclei RNA-seq data and 6 different bulk ATAC-seq data objects.

  Dataset EGAD00001009328

• ProstOmics - Spatial Transcriptomics

  Spatial transcriptomics data (ST) from 32 human prostate tissue samples originating from 8 prostate cancer patients (5 patients with post-surgery relapse). The ST data was acquired using the Visium Spatial Gene Expression kit which resulted in over 20 000 spatially defined spots for the 32 tissue samples. The raw transcriptomics data is RNA-seq. The individual samples have information on patient origin and sample type (cancer, cancer-adjacent field-effect normal or normal sample far from cancer). Each ST spot has metadata such as sample origin, histology class (stroma, normal epithelium, cancer of various grading etc), number of cells and estimated cell type fractions. Patient metadata include information of age at surgery, time (months) until reported relapse, total follow-up time, pre-surgery PSA, post-surgery T-stage and metastasis status.

  Dataset EGAD50000000603

• Breast Cancer - immune clusters - RNA-seq

  In this study we use expression data from breast cancer tumors to define immune clusters in breast cancer. Immune clusters have gradual levels of immune infiltration. In the intermediate immune infiltration cluster, we found a worse prognosis which is independent of known clinicopathological features. We also found the immune clusters associated with treatment response. Further using gene expression data and deconvolution algorithms to dissect the immune contexture of the clusters.

  Study EGAS00001003631