11789 results for "cancer rna-seq"

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• Southern African Prostate Cancer Study (SAPCS) 70 whole bloods

  new 70 germline WGS data of prostate cancer patients

  Dataset EGAD50000001626

• Exome Sequencing of Gastric Cancer Samples

  We perform whole exome sequencing on 50 pairs of gastric cancer and matched normal samples.

  Dataset EGAD00001006957

• Genentech gallbladder cancer study - exome

  Genentech gallbladder cancer study - exome

  Dataset EGAD00001004853

• 65 prostate cancer cases transcriptome sequencing

  65 prostate cancer cases transcriptome sequencing

  Dataset EGAD00001000975

• SPECTA Lung cancer VCF files

  SPECTA Lung cancer VCF files

  Dataset EGAD00001006260

• Research on the identification of cancer stem cells for peidatric and adult malignancies.

  In recent years, "cancer stem cells" have been reported to exist in many cancers. Many reports have shown that they are the main cause of cancer recurrence. The aim of this study is to test whether cancer stem cells can be identified using patient samples. We will perform omics analysis of rare pediatric cancers by integrating genomics, transcriptomics, proteomics and metabolomics, including somatic and germline mutations, to deepen our understanding of the biological characteristics of the target disease. Based on the knowledge gained and clinical information from patients, we will conduct preclinical pharmacological studies using mouse models and in vitro models to investigate the therapeutic effects of novel therapeutic agents and novel therapeutic strategies such as cellular therapies.

  Study JGAS000623

• Bulk transcriptomic analyses of monocyte-derived dendritic cells treated with CES1i

  The data published here contains bulk RNA-sequencing (RNAseq) data as obtainedfrom monocyte-derived dendritic cells in treated with/without LPS and with/without CESi (WWL113). Sequencing was performed in a paired-ended fashion on the NovaSeq6000.

  Dataset EGAD50000000344

• Small RNA sequencing of human oocytes and early embryos

  This dataset contains single-end fastq files for human oocytes (n=12), zygotes (n=5), and early embryos (n=10) sequenced with Illumina NextSeq500. The sequencing libraries were prepared from single oocytes, zygotes, and embryos in two batches.

  Dataset EGAD50000000227

• Single-nucleus RNA-sequencing of normal adrenal cortex and adrenocortical tumors

  This study reports a single-nucleus transcriptome atlas of steroid, stromal and immune cells in 38 human normal adrenals and adrenocortical tumors.

  Study EGAS50000000585

• Integrated Multiomics Uncovers Distinct Macrophage Alterations in Human Metabolic dysfunction-Associated Steatohepatitis Progression

  We report single-nuclei RNA sequencing data (n=18) from human livers in metabolic dysfunction-associated steatotic liver disease (MASLD) patients. there are 5 MASH, 4 MASL, 5 control obese and 4 control lean samples.

  Dataset EGAD50000001129

• Kidney tumour_DNA (2018-09-19)

  This is a bulk DNA and RNA sequencing study of human renal tumours . This dataset contains all the data available for this study on 2018-09-19.

  Dataset EGAD00001004346

• Data set for pan.met study

  The dataset referenced by EGA Study ID EGAS00001004208 includes 20 human exome sequencing data and 11 human RNA sequencing data from tumor or normal tissues. Each sequencing data includes two pair-end short read files in fastq format.

  Dataset EGAD00001005957

• Whole transcriptome profiling of osteosarcoma

  This dataset contains 86 osteosarcoma samples and their matched normals that underwent RNA sequencing using size fractionation, NuGEN Ovation Ultralow Library System V2 preparation, and paired-end sequencing on Illumina HiSeq 2000.

  Dataset EGAD00001008435

• Comparative transcriptome of CD34+ hematopoietic progenitors from myeloproliferative patients and control donors

  Comparative transcriptome of CD34+ hematopoietic progenitors from 4 myeloproliferative patients (MPN) and 4 control donors performed by RNA-Sequencing.

  Dataset EGAD00001007531

• Whole transcriptome profiling of osteosarcoma

  This dataset contains 86 osteosarcoma samples and their matched normals that underwent RNA sequencing using size fractionation, NuGEN Ovation Ultralow Library System V2 preparation, and paired-end sequencing on Illumina HiSeq 2000.

  Dataset EGAD00001008436

• Transcriptome Analysis of CLL patient samples

  This dataset includes Illumina RNA Sequencing Data for 59 chronic lymphocytic leukemia patient samples. 57 samples are single end, 2 samples are paired end sequencing.

  Dataset EGAD00001008320

• RNASeq read files of glioblastoma PDX samples

  This dataset contains 46 fastq files of paired-end RNA sequencing of an Illumina®️ TrueSeq stranded mRNA library of 23 glioblasoma PDX samples.

  Dataset EGAD00001010196

• Gene expression profile of mesothelial cells from peritoneal adhesion biopsies

  RNA-sequencing analysis was carried out on mesothelial cells isolated from peritoneal adhesion biopsies and compared to control human peritoneal mesothelial cells to identify a mesothelial-to-mesenchymal transition (MMT)-related gene signature in patients.

  Dataset EGAD00001008324

• Single-cell Transcriptome for glioblastoma intra-tumoral heterogeneity

  Single-Cell RNA Sequencing of 355 cells isolated from 7 tissue fragments of 3 patients corresponding to locally adjacent tumor, multifocal with recurrence and sections segregated by a marker of tumor cellularity (5-ALA).

  Dataset EGAD00001002249

• Sequencing data for oesophageal / related samples - Foley, Shorthouse et al (RNA)

  Data supporting: "SMAD4 and KCNQ3 Alterations are Associated with Lymph Node Metastases in Oesophageal Adenocarcinoma" RNAseq (FASTQ files) 6 samples

  Dataset EGAD00001011065

• Skin Fibroblast Transcriptome of Type 2 Diabetic Patients

  Comparative analysis of transcriptomes of skin fibroblasts in 17 Type 2 Diabetic individuals by RNA sequencing.

  Dataset EGAD00001004980

• Sequencing data for oesophageal / related samples - Kazachenka et al (RNA)

  Data supporting: "The transcriptional landscape of endogenous retroelements delineates esophageal adenocarcinoma subtypes" RNAseq for 279 samples

  Dataset EGAD00001011076

• Exploration of CNV’s and SNV’s in cancers with well-known genetic rearrangements: Identification of additional genetic changes in rearrangements-driven cancer

  Specific cancers are well known to have specific gene rearrangements. Representative cancer is chronic myeloid leukemia (CML) which is famous for BCR/ABL gene rearrangements. By the way, also genetic rearrangements are not infrequently found in acute leukemia, and those include CBFB/MYH11 rearrangements, PML/RARA rearrangements, AML/ETO rearrangements, and BCR/ABL rearrangements. The situation is same is for lung adenocarcinoma of non-smokers and prostate cancers, and ALK and RET gene rearrangements are recently found. However, not all these rearrangements are sufficient to develop cancer, and it is suggested that some genetic aberrations are required for the development and progression of cancer in addition to these genetic rearrangements. In this study, we have a plan to explore the existence of additional genetic changes including single nucleotide variants (SNV’s) and copy number variations (CNV’s) in cancers with specific genetic rearrangements so as to define additional genetic changes that confer full oncogenic potential to cancer cells. So as to look at SNV’s and small indels, we are going to use whole exome sequencing (WES) data. And as for CNV’s, we are going to use array-CGH data. Also, to check the functionality of these genetic changes, we are going to use whole transcriptome data (WTS). In addition, in samples with whole genome sequencing (WGS ) data, we are going to explore genetic changes in intronic regions also. In fact, we sequenced 10 AML samples which are known to have specific genetic rearrangements. Analysis of these WGS, WES and WTS data are under the way. If we have a chance to access WES, array-CGH and WTS data of other cancer samples with specific genetic rearrangements, we would be able to compare additional genetic changes required for oncogenesis in cancer cells with various genetic rearrangements. We hope this result will enlighten our understanding of cancer genetics further.

  Study EGAS00001000673

• scRNAseq data of CAP

  Fastq files from scRNAseq data of cancer associated pericytes-like from 3 patients. CAP were isolated from a total of 3 primary BC (surgical residues prior to any treatment) by using BDFACS ARIA III sorter (BD Biosciences). BC were collected directly from the operating room after surgical specimen macroscopic examination and selection of areas of interest by a pathologist. Samples were cut into small pieces (around 1 mm3) and digested in CO2-independent medium (Gibco #18045-054) supplemented with 150 μg/mL liberase (Roche #05401020001) and Dnase I (Roche #11284932001) for 40 min at 37°C with shaking (180 rpm). After digestion, cells were processed and stained as described above (#Flow Cytometry analysis of BC samples). CAP fibroblasts were then gated on the Live/Dead negative fraction and defined as EPCAM- CD45- CD31- CD235a- FAPMed CD29High. CAP scRNA-seq: Upon isolation, CAP cells were directly collected into RNase-free tubes (Thermo Fisher Scientific, #AM12450) precoated with DMEM (GE Life Sciences, #SH30243.01) supplemented with 10% FBS (Biosera, #1003/500). Single-cell capture, lysis, and cDNA library construction were performed using Chromium system from 10X Genomics, with the following kits: Chromium Single Cell 3′ Library & Gel Bead Kit v2 kit (10X Genomics, #120237) and Chromium Single Cell A Chip Kits (10X Genomics, #1000009). Generation of gel beads in Emulsion (GEM), barcoding, post GEM-reverse transcription cleanup and cDNA amplification were performed according to the manufacturer’s instructions. Cells were loaded accordingly on the Chromium Single cell A chips, and 12 cycles were performed for cDNA amplification. cDNA quality and quantity were checked on Agilent 2100 Bioanalyzer using Agilent High Sensitivity DNA Kit (Agilent, #5067-4626) and library construction followed according to 10X Genomics protocol. Libraries were next run on the Illumina HiSeq (for patients P1) and NovaSeq (for patients P2–3) with a depth of sequencing of 50,000 reads per cell.

  Dataset EGAD50000000321

• Multi-omics bulk and single-cell profiling of epithelioid sarcoma

  Epithelioid sarcoma (EpS) is an aggressive sarcoma, characterized by the loss of SMARCB1 expression. EpS is traditionally classified as distal or proximal according to clinicopathological features, but its molecular characteristics remain largely unknown. To establish an EpS molecular classification and uncover determinants of inter- and intra-patient heterogeneity in EpS, we used multi-omics profiling and integrated the genomic, transcriptional and methylome landscapes with single-cell RNA sequencing on fresh samples as well as spatial transcriptomics. We identified two molecular subtypes of EpS: “distal-like” and “proximal-like”, which were distinct from the histological subtypes. Distal-like tumors include all morphological distal subtypes and a subset of proximal and hybrid EpS. They express a specific molecular single-cell derived epithelial-to-mesenchymal transition signature, which associates with improved patient survival and includes Desmoglein 2 (DSG2), which we identify as a potential routine immunohistochemical biomarker to improve diagnosis. Distal-like EpS also display increased peri-tumoral CD8+ T cell infiltrates and specific tumor-immune cell interactions. Conversely, proximal-like EpS harbor a higher inter-tumoral molecular heterogeneity with some cases resembling other SMARCB1-deficient tumors by DNA methylation profiling, and increased intra-tumoral pro-tumoral macrophage infiltrates. Our study introduces a novel molecular classification of EpS with prognostic value, associated diagnostic biomarkers and distinct tumor immune microenvironments, which extends beyond traditional clinicopathological classification. This paves the way for precision medicine-based therapeutic strategies for patients with EpS.

  Study EGAS50000000973