15020 results for "cancer studies using rna-seq"

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• Genetic control of the transcriptomic response of monocytes to bacterial and viral stimuli assessed by RNA-seq in Africans and Europeans

  Humans differ in the outcome that follows exposure to life-threatening pathogens, yet the extent of population differences in immune responses, and their genetic and evolutionary determinants, remain undefined. Here, we characterized, using RNA-sequencing, the transcriptional response of primary monocytes from Africans and Europeans to bacterial and viral stimuli - ligands activating Toll-like receptors pathways (TLR1/2, TLR4 and TLR7/8) and influenza virus - and mapped expression quantitative trait loci (eQTL). We identify multiple cis- and trans-eQTL that contribute to the marked differences in immune responses detected within and between populations, including a TLR1 master regulator that decreases expression of pro-inflammatory genes in Europeans only. We show that regulatory variants have been privileged targets of natural selection, uncovering evolutionarily advantageous mechanisms, such as attenuated inflammation. Finally, we demonstrate that admixture with Neandertals introduced regulatory variants into European genomes, affecting preferentially responses to viral challenges, and identify archaic haplotypes that contributed to population genetic adaptation.

  Study EGAS00001001895

• Single-cell proteogenomics of MDS upon AZA

  To study the evolution of the clonal molecular and cellular architecture of MDS upon the treatment with AZA, we performed scDNA-seq of an amplicon panel of 53 genes recurrently mutated in myeloid malignancies, as well as typically lost or gain DNA regions, and scProt-seq of 42 cell-surface proteins to provide a simultaneous landscape of the genetic setting and immunophenotype using the Tapestri platform (MissionBio). We sequenced thousands of cells from 14 MDS patients, where paired bone marrow samples were obtained at the timepoint of diagnosis and after AZA treatment.

  Study EGAS00001007427

• PREDICT

  PREDICT/Predicting individual response and resistance to VEGFR/mTOR pathway therapeutic intervention using biomarkers discovered through tumour functional genomics.

  Study EGAS00001000094

• Single-cell RNA-seq of cervical smears and early placental material

  Fastq files of single-cell RNA-sequencing data generated with 10X Genomics of twelve non-invasive cervical samples from pregnant women (7-12 weeks gestational age) and six placental biopsies from patients who had a recurrent miscarriage early during gestation (<12 weeks gestational age).

  Dataset EGAD00001010094

• Integrated analysis of whole genome and RNA sequencing in 22 HBV-associated HCCs

  RIKEN collection WGS and RNA-seq reads for 66 HBV-associated HCC and matched blood or liver samples from 22 donors.

  Dataset EGAD00001001035

• RNA sequencing of BCP-LBL and EM B-ALL patients samples

  In order to perform comprehensive transcriptomics and gene fusion analyses of a cohort of BCP-LBL and extramedullary (EM) manifestations of B-ALL patients, we performed RNA sequencing of 16 tissue samples from BCP-LBL and EM B-ALL patients. Because the material was available as FFPE, and had a relative low quality, we used a capture-based approach, where NGS libraries obtained from total RNA were captured in 4-plex, using a whole exome capture panel.

  Dataset EGAD50000002047

• Bulk RNA-sequencing of rhabdomyosarcoma tumour tissue (2025-09-30)

  This study investigates high-risk rhabdomyosarcoma (RMS) using multiple single-cell and spatial genomic technologies. We generated and analysed single-cell and single-nucleus RNA-sequencing, chromatin accessibility, and spatial transcriptomics data from primary tumours and validation samples. These datasets characterise cellular diversity within rhabdomyosarcoma and identify cell states associated with aggressive disease. The data support research into tumour biology, risk stratification, and therapeutic target discovery. This repository houses the bulk RNA sequencing of RMS tumours data. . This dataset contains all the data available for this study on 2025-09-30.

  Dataset EGAD00001015718

• Bulk RNA-sequencing of rhabdomyosarcoma tumour tissue (2025-09-30)

  This study investigates high-risk rhabdomyosarcoma (RMS) using multiple single-cell and spatial genomic technologies. We generated and analysed single-cell and single-nucleus RNA-sequencing, chromatin accessibility, and spatial transcriptomics data from primary tumours and validation samples. These datasets characterise cellular diversity within rhabdomyosarcoma and identify cell states associated with aggressive disease. The data support research into tumour biology, risk stratification, and therapeutic target discovery. This repository houses the bulk RNA sequencing of RMS tumours data. . This dataset contains all the data available for this study on 2025-09-30.

  Dataset EGAD00001015714

• Single-cell RNA-sequencing of rhabdomyosarcoma tumouroids (2025-09-30)

  This study investigates high-risk rhabdomyosarcoma (RMS) using multiple single-cell and spatial genomic technologies. We generated and analysed single-cell and single-nucleus RNA-sequencing, chromatin accessibility, and spatial transcriptomics data from primary tumours and validation samples. These datasets characterise cellular diversity within rhabdomyosarcoma and identify cell states associated with aggressive disease. The data support research into tumour biology, risk stratification, and therapeutic target discovery. This repository houses the single-cell RNA sequencing of RMS tumouroids data. . This dataset contains all the data available for this study on 2025-09-30.

  Dataset EGAD00001015708

• Integrative Gene Regulatory Network Analysis Discloses Key Driver Genes of Fibromuscular Dysplasia

  Fibromuscular dysplasia (FMD) is a poorly understood vascular disease that can lead to hypertension, stroke, myocardial infarction and even death. In 2013 we initiated the DEFINE study (ClinicalTrials.gov Identifier: NCT01967511), which has now recruited > 440 subjects and which aims to unravel the pathobiological mechanisms of this disease. Here, by integrating DNA genotype and RNA sequence data from primary fibroblasts of 83 FMD patients and 71 matched healthy controls from the DEFINE study, and its precursor the CAUSE study, we inferred 18 gene regulatory co-expression networks, four of which were found to act together as an FMD-associated super network in the arterial wall. Molecular studies indicated that this super network, termed SN-A and with the top key driver gene UBR4, governs multiple aspects of vascular cell physiology and functionality, including collagen/matrix production. The effects of UBR4 knockdown were also investigated in commercially available aortic smooth muscle and fibroblast cell lines by RNA sequencing, and these results are included in these datasets.Note, that of the 154 subjects in this analysis, 62 did not provide consent to have their genomic data made publicly available. Therefore the human subject data available here is from the 92 study participants who provided written informed consent to share their deidentified genomic data publicly.

  Study phs003674

• IMAGINE_EBV studies

  Dac EGAC00001000790

• Whole scRNA-seq from pre frontal cortex patient biopsy

  The single- cell RNA-seq prefrontal cortex sample was selected from a cohort of patients with hydrocephalus ≥18 years of age planning to undergo CSF diversion surgery either with a ventriculoperitoneal shunt placement (VP) or ventriculocisternostomy (VCS). The tissue was dissociated followed by red cell and debris removal and counting of the cells to be sequenced with (scRNA-seq; 10x Genomics kit v2). After dissociation only non neuronal cells were retrieved.

  Dataset EGAD50000001542

• EM-seq data from plasma cfDNA samples of ALS patients, control and C9-carriers

  This dataset contains targeted EM-seq data from 61 plasma cfDNA samples spanning 20 sporadic ALS, 10 C9orf72-ALS, 10 asymptomatic C9orf72 expansion carriers, and 21 non-disease controls. Libraries were prepared with the NEBNext Enzymatic Methyl-seq (EM-seq) kit, captured using the Twist Human Methylome Panel (3.98 M CpGs; 123 Mb), and sequenced on Illumina NovaSeq 6000 (150 bp paired-end; ~45× on-target depth) with unmethylated lambda DNA spike-in and a 20% PhiX balance library. The dataset includes per-sample gzipped FASTQ files (R1/R2) and minimal metadata.

  Dataset EGAD50000001808

• DNA methylation database for gynecological cancer detection, classification and assay development

  MeD-seq data (fastq files) from gynecological cancers and associated healthy tissues. In total 292 fastq files generated by MeD-seq are deposited consisting of: healthy tissues (vulva n=11, cervix n=15, endometrium n=13, fallopian tube n=18 and ovary n=13), precursor lesions of cancer (vulva n=23 and cervix n=46) and cancer (vulva n=21, cervix n=45, endometrium n=26, fallopian tube n=8 and ovary n=33)

  Dataset EGAD50000000611