15165 results for "cancer studies using rna-seq"

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• Circular RNA characterization in functionally distinct brain regions

  RNA extracted from middle temporal gyrus (MG) brain region of healthy elderly controls. Three pairs of samples were generated, each pair consisting of one sample that was enriched for circular RNAs using RNase R, and a second sample that was not enriched (total N=6). Remaining samples in the study from other functionally distinct brain regions are currently under process and will be released soon.

  Dataset EGAD00001004211

• oncogenic gene fusions in primary colon cancers

  This dataset represents RNA-sequencing data from 278 primary colon cancers obtained from fresh-frozen tumor sections. RNA-sequencing was performed using TruSeq library preparation and samples were sequenced on Illumina NextSeq and HiSeq. The data are available as Illumina NextSeq and HiSeq fastq files (_R1.fastq and _R2.fastq for each tumor sample, 556 files in total).

  Dataset EGAD00001003291

• AHA Uganda Study RNAseq Dataset

  This dataset was generated as part of the AHA Uganda Study, an Acute Rheumatic Fever case-control prospective study conducted in Uganda. This dataset consists of raw fastqs from RNA sequencing for 266 samples (226 cases, 40 controls) from 162 individuals over five timepoints. RNA was extracted from Paxgene blood samples using the Sureselect HS2 library preparation kit. Sequencing was performed on Illumina NovaSeq 6000, yielding ~30-40 million raw paired reads per sample.

  Dataset EGAD00001015810

• LuCaP cell line ChIP-seq

  ChIP-seq of 2 LuCaP cell lines with different factors. LuCaP cell line 189.4 was ChIP-sequenced for 6 factors with different conditions as well as the input DNA (n=31 in total). LuCaP 189.4 ChIP-sequence experiments include; 3 H3K27ac/DMSO, 3 H3K27ac/vorinostat, 3 H3K27ac/dexamethasone, 3 H3K27ac/dexamethasone/vorinostat, 3 GR dexamethasone, 3 GR dexamethasone/vorinostat, 3 HDAC3/DMSO, 3 AR, 3 FOXA1, 3 H3K27ac and 1 input sample. LuCaP 35 ChIP-sequence experiments as well as input include (n=10 in total); 3 H3K27ac, 3 AR, 3 FOXA1 and 1 input sample. All 41 samples were sequenced using paired-end reads on Illumina NovaSeq 6000. Each sample has 2 fastq files, one for the forward and reverse read, respectively.

  Dataset EGAD50000001345

• DLK1 Distinguishes Subsets of NF1-Associated Malignant Peripheral Nerve Sheath Tumors with Divergent Molecular Signatures

  Study Design and Aims We aimed to investigate the molecular mechanisms underlying the malignant transformation of pre-existing neurofibromas (PNST) to malignant peripheral nerve sheath tumors (MPNST) in individuals with neurofibromatosis type 1 (NF1). Our primary focus was on the role of DLK1 in this transformation process and its potential as a biomarker for diagnosis, risk assessment, and therapeutic stratification. Population InformationThis study consisted of a case set of peripheral nerve sheath tumor specimens, including plexiform neurofibromas, atypical neurofibromas, and MPNSTs, acquired under IRB approval at our institution. The majority of the subjects had neurofibromatosis type 1. Principal Findings DLK1 Overexpression: The study found that DLK1 is significantly increased in MPNST compared to non-malignant neurofibromas.Early Marker: DLK1 overexpression may precede histological changes indicative of malignancy.Serum Levels: Higher serum levels of DLK1 were observed in both mice and humans with MPNST.Molecular Stratification: Divergent levels of DLK1 expression identified distinct MPNST subsets with unique molecular characteristics and potential therapeutic targets.Clinical Implications: Overexpression of DLK1 was associated with the reactivation of embryonic signatures, an immunosuppressive microenvironment, and poorer overall survival in NF1-MPNST patients. Data Availability Whole exome sequencing, bulk RNA sequencing, and single-cell RNA sequencing data from this study will be made available through the database of Genotypes and Phenotypes (dbGaP) to support further research and validation studies.

  Study phs003835

• RBMX functional retrocopy safeguards brain development

  Retrotransposition has generated thousands of intronless gene copies in mammalian genomes, yet their contribution to brain development and evolution remains largely unexplored. We investigated the role for RBMX retrocopy in shaping neurodevelopment and modulating disease. First, we reported 8 distinct pathogenic variants in RBMX, an X-linked splicing regulator, in 11 males presenting with intellectual disability, microcephaly, and cortical malformations. Then, we combined transcriptomic profiling, protein–protein and protein–RNA interaction studies both in human cellular models and mouse embryonic cortices to assess the functional redundancy between RBMX and its retrocopy RBMXL1. Finally, we use mouse genetics to dissect RBMX function and its compensation by RBMXL1 in corticogenesis. We show that RBMX pathogenic variants disrupt cortical development through both loss- and gain-of-function mechanisms. Surprisingly, despite severe phenotypes in humans, Rbmx-deficient mice display only mild cortical abnormalities, a discrepancy likely due to compensation by Rbmxl1, a retrocopy that arose independently in mice and humans. We demonstrate that RBMX and RBMXL1 share protein and RNA partners and act redundantly in brain development, with RBMXL1 buffering the impact of RBMX deficiency. Together, these findings establish that RBMXL1 functions as a functional paralog of RBMX safeguarding neurodevelopment by buffering deleterious variation. More broadly, these results identify retrocopies as active contributors to neurodevelopmental robustness and suggest that functional retrocopies may have facilitated the evolutionary diversification of the mammalian brain.

  Study EGAS50000001650

• Coding and small non-coding transcriptional landscape of tuberous sclerosis complex cortical tubers: implications for pathophysiology and treatment

  Tuberous Sclerosis Complex (TSC) is a rare genetic disorder that results from a mutation in the TSC1 or TSC2 genes leading to constitutive activation of the mechanistic target of rapamycin complex 1 (mTORC1). TSC is associated with autism, intellectual disability and severe epilepsy. Cortical tubers are believed to represent the neuropathological substrates of these disabling manifestations in TSC. In the presented study we used high-throughput RNA sequencing in combination with systems-based computational approaches to investigate the complexity of the TSC molecular network. Overall we detected 438 differentially expressed genes and 991 differentially expressed small non-coding RNAs in cortical tubers compared to autopsy control brain tissue. We observed increased expression of genes associated with inflammatory, innate and adaptive immune responses. In contrast, we observed a down-regulation of genes associated with neurogenesis and glutamate receptor signaling. MicroRNAs represented the largest class of over-expressed small non-coding RNA species in tubers. In particular, our analysis revealed that the miR-34 family (including miR-34a, miR-34b and miR-34c) was significantly over-expressed. Functional studies demonstrated the ability of miR-34b to modulate neurite outgrowth in mouse primary hippocampal neuronal cultures. This study provides new insights into the TSC transcriptomic network along with the identification of potential new treatment targets.

  Study EGAS00001002485

• Single cell transcriptional characterization of human megakaryocyte lineage commitment and maturation

  Single cell RNA-sequencing of sternal bone marrow reciding Hematopoietic Stem Cells (HSCs) and Megakaryocytes (MKs) from individuals undergoing elective open heart valve replacement. HSCs were defined as Lineage-, CD34+, CD38-, CD45RA-, CD90+, CD49f+ cells. MKs where CD41a+, CD42b+ and ploidy was determined with Hoechst. A sternal bone marrow scraping was taken directly following median sternotomy using a Volkmann’s spoon. The sample was collected into an EDTA Vacutainer tube containing 1.8mg/ml EDTA. 4mL of Dulbecco’s phosphate buffered saline (PBS, Sigma) containing 10% human serum albumin (HSA, Gemini Bio Products) was added and the whole volume was resuspended by pipetting 2-3 times. The sample was then put on metallic thermal beads (ThermoFisher Scientific) at a temperature between 0-4°C and transported to the University of Cambridge for further processing. For HSC isolation the cells were stained with the following antibody cocktail: PECy5 conjugated anti-lineage specific antibodies: CD2 (BD), CD3 (BD), CD10 (BD), CD11b (BD), CD11c (BD), CD19 (BD), CD20 (BD), CD56 (BD), biotinylated CD42b (Pab5, NHS Blood and Transplant, International Blood Group Reference Laboratory [IBGRL]), biotinylated GP6 (Pab5, NHS Blood and Transplant, International Blood Group Reference Laboratory [IBGRL]) used in combination with PECy5 conjugated streptavidin (Biolegend). Alexa Fluor 700 conjugated anti-CD34 (BD), PerCP-Cy5.5 conjugated anti-CD38 (BD), Pacific Blue conjugated anti-CD45RA (Invitrogen), PECy7 conjugated anti-CD90 (BD),PE conjugated anti-CD49f (BD). After staining cells were kept at 4°C before sorting using a FACS Aria Fusion flow sorter (BD). Single HSCs defined as Lineage-, CD34+, CD38-, CD45RA-, CD90+, CD49f+ cells were sorted by FACS directly into individual wells of a 96-well plate. Index sort data was collected for each single cell. For MK isolation the cells were stained for surface MK markers with mouse anti-human CD41a APC conjugated antibody (BD) and mouse anti-human CD42b PE conjugated antibody (BD) and for ploidy analysis with 1ug/ml Hoechst 33342 (Invitrogen). After incubation at 37°C for 30 minutes, the cells were kept at 4°C before sorting using a FACS Aria Fusion flow sorter (BD). Single cells and MK pools of 20 cells were sorted by FACS according to ploidy level using a 100uM nozzle directly into individual wells of a 96-well plate. cDNA synthesis and poly(A) enrichment was performed following the G&T-seq protocol (Macaulay et al. 2015), a variation of the Smart-seq2 protocol1. ERCC spike-in RNA (Ambion) was added to the lysis buffer in a dilution of 1:4,000,000.

  Dataset EGAD00001006677

• MutWP1: CRUK Grand Challenge Mutographs of Cancer: Kidney (1)

  The Mutographs project aims to advance our understanding of the causes of cancer through studies of mutational signatures. Led by Mike Stratton, together with Paul Brennan, Ludmil Alexandrov, Allan Balmain, David Phillips and Peter Campbell, this large-scale international research endeavour was awarded a Cancer Research UK Grand Challenge. Different patterns of somatic mutation are generated by the different environmental, lifestyle and genetic factors that cause cancer, many of them are still unknown. Within Mutographs, the International Agency for Research on Cancer is coordinating the recruitment of 5000 individuals with cancer (colorectal, renal, pancreatic, oesophageal adenocarcinoma or oesophageal squamous cancers) across 5 continents to explore whether different mutational signatures explain marked variation in incidence. In brief, through an international network of collaborators around the world, biological materials are collected, along with demographic, histological, clinical and questionnaire data. Whole genome sequences of tumour-germline DNA pairs are generated at the Wellcome Trust Sanger Institute. Somatic mutational signatures are subsequently extracted by non-negative matrix factorisation methods and correlated with risk factors data. Through an enhanced understanding of cancer aetiology, Mutographs unprecedented effort is anticipated to outline modifiable risk factors, lead to new approaches to prevent cancer, and provide opportunities to empower early detection, refine high-risk groups and contribute to further therapeutic development.

  Dataset EGAD00001006427

• Log2-CPM (voom)-normalised read-count matrix of the RNAseq Datasets of CLUSTER JIA pre-MTX cohort

  Samples were sequenced with NovaSeq6000 and the sequencing files were processed using the RSSnextflow workflow (https://gitlab.com/b8307038/rssnextflow). The raw read counts from all batches were combined. Analysis-ready batch-corrected read counts were generated using ComBat-seq. Read counts normalisation for DGE analysis was performed with limma-voom function (log2 CPM) as seen in the pipeline.

  Dataset EGAD50000002170