15075 results for "cancer studies using rna-seq"

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• RNA-seq of PDAC patient-derived xenograft tumors

  This dataset contains single cell RNA-seq data of stromal cells derived from two PDX models (N = 2 in total) and bulk RNA-seq data of two PDX models treated with gemcitabine and our novel antibody-drug conjugate, C6-EBET (N = 59 in total). Bulk RNA-seq experiments were performed with Agilent SureSelect Strand Specific RNA Library Prep Kit (Agilent). Single cell RNA-seq experiments were performed with Chromium Single Cell 3' Reagent Kits v2 Chemistry (10x Genomics).

  Dataset EGAD00001010130

• RNA sequencing datasets from isolated human subcutaneous and visceral adipocytes from obese and lean people

  94 human adipocyte samples isolated from whole adipose tissues using collagenase digestion of tissue and flotation of lipid-laden adipocytes, followed by RNA isolation and RNA sequencing (SMARTer Stranded Total RNA-Seq library preparation, HiSeq 4000 100-bp paired-end reads). Adipocyte samples comprise subcutaneous and visceral adipocytes isolated from obese and lean people (N=24 obese-subcutaneous, N=24 obese-visceral, N=22 control-subcutaneous, N=24 control-visceral). Human adipocyte RNA sequencing data are provided as BAM files.

  Dataset EGAD00001010195

• Comprehensive RNA repository of tissue and plasma from patients with esophageal cancer or precursor lesions

  A comprehensive RNA repository (both coding and non-coding) from 17 patients diagnosed with esophageal adenocarcinoma, high-grade dysplastic or non-dysplastic Barrett’s esophagus. Per patient, a blood plasma sample, and a healthy esophageal and disease tissue sample were collected. This dataset includes both mRNA and small RNA sequencing data (fastq.gz files) of all tissue and plasma samples. In total,102 RNA-seq libraries from 51 samples (17 plasma and 34 tissue samples) were sequenced (plasma mRNA libraries were sequenced twice).

  Dataset EGAD00001006857

• Single-cell RNA sequencing of Small Intestinal Neuroendocrine Tumors (SI-NET)

  Single-cell RNA sequencing (scRNA-seq) was performed on cells isolated from intestinal mucosa and small intestinal neuroendocrine tumors (SI-NETs) to compare their transcriptomic profiles. Tumor and adjacent normal tissues were collected from two SI-NET patients, enzymatically dissociated, and processed into single-cell suspensions. Single-cell libraries were generated using the Chromium Single Cell 3′ Reagent Kit v3 and sequenced on an Illumina platform to produce FASTQ files for downstream analysis.

  Dataset EGAD50000002265

• bulk RNA-seq of the 5 HCC patients which also have the single cell T cell RNA-seq data

  bulk RNA-seq data of the 5 HCC patinets. Single cell RNA seq data of these patients was under the accession number EGAD00001003337

  Dataset EGAD00001004148

• mRNA-seq of paired Follicular Lymphoma (FL) patient samples before and after high-grade transformation to DLBCL (FL vs. tFL) from FFPE tissues

  The aim of this study was to analyze transcriptional changes in paired samples of follicular lymphoma (FL) and its histological transformation (tFL). Transformation of FL is an adverse event in the course of the disease and is associated with poor outcome. Several genetic aberrations have been linked to tFL pathogenesis, but gene expression changes and microenvironmental interactions have not been well characterized. We performed RNA profiling of FL and paired tFL samples transformed to DLBCL (n=11 pairs). For RNA profiling, we utilized libraries prepared using RNA isolated from archival FFPE tissues.

  Study EGAS50000000941

• RNA Sequence-Based Analysis Used to Compare Breast Primary and Metastatic Tumor Pairs

  The goal of this study is to capture the transcriptome landscape of luminal and basal-like metastatic breast tumors. We will aim to show the importance of using the most up-to-date cancer biopsy for detecting cancer drivers that were often not present in the primary tumor.

  Study phs001866

• Single-cell multi-omic analysis of control and glioblastoma samples from the brain and border regions.

  Single-cell RNA-sequencing. Some samples were analyzed using CITE-Seq and samples were multiplexed and run on the same 10X reaction

  Dataset EGAD50000000045

• Single-cell/single-nucleus RNA-seq of diffuse hemispheric gliomas, H3G34-mutant.

  9 DHG-H3G34 patient samples were sequenced by paired-end scRNA-sequencing using the Smart-Seq2 protocol on a NextSeq 500 sequencer (Illumina). Illumina bcl2fastq 1.5 was used for demultiplexing. This dataset contains the resulting 9,408 fastq files of 4,704 single cells/nuclei sequenced.

  Dataset EGAD50000000760

• Ancestry and somatic profile predict acral melanoma origin and prognosis – RNA

  Acral melanoma, which is not ultraviolet (UV)-associated, is the most common type of melanoma in several low- and middle-income countries including Mexico. Latin American samples are significantly underrepresented in global cancer genomics studies, which directly affects patients in these regions as it is known that cancer risk and incidence may be influenced by ancestry and environmental exposures. To address this, we characterise the genome and transcriptome of 123 acral melanoma tumours from 92 Mexican patients, a population notable because of its genetic admixture. Compared with other studies of melanoma, we found fewer frequent mutations in classical driver genes such as BRAF, NRAS or NF1. While most patients had predominantly Amerindian genetic ancestry, those with higher European ancestry had increased frequency of BRAF mutations and a lower median number of structural variants. The tumours with activating BRAF mutations have a transcriptional profile more similar to cutaneous non-volar melanocytes, suggesting that acral melanomas in these patients may arise from a distinct cell of origin compared to other tumours arising in these locations. KIT mutations were found in a subset of these tumours, and quadruple wild-type samples (non BRAF/NRAS/NF1/KIT) differed from mutated samples in their structural genomic profile and overall and recurrence-free survival patterns. Transcriptional profiling defined three expression clusters; these characteristics were associated with recurrence-free and overall survival. We highlight potential novel low-frequency drivers, such as PTPRJ, NF2 and RDH5. Our study enhances knowledge of this understudied disease and underscores the importance of including samples from diverse ancestries in cancer genomics studies.

  Dataset EGAD00001015756