15059 results for "cancer studies using rna-seq"

in 112.28 milliseconds.

• RNA-seq of Liver Cancer

  The present series corresponds to 161 RNA-seq samples from tumors with matched WES or WGS. Hepatocellular carcinoma (HCC) accounts for more than 90% of liver cancers, and is a major health problem. It is the 3rd cause of cancer-related mortality. Advances in genomic analyses have formed a comprehensive understanding of different underlying pathobiological layers resulting in hepatocarcinogenesis. Thus, the development of next-generation sequencing technologies has made it possible to generate more comprehensive catalogues of somatic alteration events (single nucleotide substitutions, structural variations, and epigenetic changes) in liver cancer genome than ever before.

  Dataset EGAD00001003993

• RNA-seq data from 70 non-small cell lung cancer (NSCLC)

  Dataset EGAD00001006145

• RNA-seq of high grade serous ovarian tumours

  Somatic RNA for 37 samples was extracted using the Qiagen Qiasymphony RNA protcol (cat no 931636). The tissue was initially homogenised using a Qiagen Bioruptor, followed by the manufacturers recommended protocol (including DNase digestion). The resulting RNA the underwent quality control as follows: firstly, A260 and A280nm were measured on a Denovix DS-11 Fx to qualitatively illustrate A260/280nm and A260/230nm ratios as measures of RNA purity. A260/280 had to be 2.0 and A260/230 had to be 2.0-2.2. Then RNA was quantified using LifeTechnologies Qubit RNA BR kit (cat no Q10210). RNAseq was carried out by the Edinburgh Clinical Research Facility on an Illumina NExtSeq500. Total RNA samples were assessed on the Agilent Bioanalyser (Agilent Technologies, #G2939AA) with the RNA 6000 Nano Kit (#5067-1512) for quality and integrity of total RNA, and then quantified using the Qubit 2.0 Fluorometer (Thermo Fisher Scientific Inc, #Q32866) and the Qubit RNA HS assay kit (#Q32855). Libraries were prepared from total-RNA sample using the NEBNext Ultra 2 Directional RNA library prep kit for Illumina (#E7760S) with the NEBNext rRNA Depletion kit (#E6310) according to the provided protocol. 400ng of totalRNA was then added to the ribosomal RNA (rRNA) depletion reaction using the NEBNext rRNA depletion kit (Human/mouse/rat) (#E6310). This step uses specific probes that bind to the rRNA in order to cleave it. rRNA-depleted RNA was then DNase treated and purified using Agencourt RNAClean XP beads (Beckman Coulter Inc, #66514). RNA was then fragmented using random primers before undergoing first strand and second strand synthesis to create cDNA. cDNA was end repaired before ligation of sequencing adapters, and libraries were enriched by PCR using the NEBNext Multiplex oligos for Illumina set 1 and 2 (#E7500). Final libraries had an average peak size of 271bp. Libraries were quantified by fluorometry using the Qubit dsDNA HS assay and assessed for quality and fragment size using the Agilent Bioanalyser with the DNA HS Kit (#5067-4626). Sequencing was performed using the NextSeq 500/550 High-Output v2 (150 cycle) Kit (# FC- 404-2002) on the NextSeq 550 platform (Illumina Inc, #SY-415-1002). Libraries were combined in an equimolar pool based on the library quantification results and run across 5 High-Output Flow Cell v2.5.

  Dataset EGAD00001010139

• RNA-seq of high grade serous ovarian tumours

  Somatic RNA for 40 samples matched to the WGS was extracted using the Qiagen Qiasymphony RNA protcol (cat no 931636). The tissue was initially homogenised using a Qiagen Bioruptor, followed by the manufacturers recommended protocol (including DNase digestion). The resulting RNA the underwent quality control as follows: firstly, A260 and A280nm were measured on a Denovix DS-11 Fx to qualitatively illustrate A260/280nm and A260/230nm ratios as measures of RNA purity. A260/280 had to be 2.0 and A260/230 had to be 2.0-2.2. Then RNA was quantified using LifeTechnologies Qubit RNA BR kit (cat no Q10210). RNAseq was carried out by the Edinburgh Clinical Research Facility on an Illumina NExtSeq500. Total RNA samples were assessed on the Agilent Bioanalyser (Agilent Technologies, #G2939AA) with the RNA 6000 Nano Kit (#5067-1512) for quality and integrity of total RNA, and then quantified using the Qubit 2.0 Fluorometer (Thermo Fisher Scientific Inc, #Q32866) and the Qubit RNA HS assay kit (#Q32855). Libraries were prepared from total-RNA sample using the NEBNext Ultra 2 Directional RNA library prep kit for Illumina (#E7760S) with the NEBNext rRNA Depletion kit (#E6310) according to the provided protocol. 400ng of totalRNA was then added to the ribosomal RNA (rRNA) depletion reaction using the NEBNext rRNA depletion kit (Human/mouse/rat) (#E6310). This step uses specific probes that bind to the rRNA in order to cleave it. rRNA-depleted RNA was then DNase treated and purified using Agencourt RNAClean XP beads (Beckman Coulter Inc, #66514). RNA was then fragmented using random primers before undergoing first strand and second strand synthesis to create cDNA. cDNA was end repaired before ligation of sequencing adapters, and libraries were enriched by PCR using the NEBNext Multiplex oligos for Illumina set 1 and 2 (#E7500). Final libraries had an average peak size of 271bp. Libraries were quantified by fluorometry using the Qubit dsDNA HS assay and assessed for quality and fragment size using the Agilent Bioanalyser with the DNA HS Kit (#5067-4626). Sequencing was performed using the NextSeq 500/550 High-Output v2 (150 cycle) Kit (# FC- 404-2002) on the NextSeq 550 platform (Illumina Inc, #SY-415-1002). Libraries were combined in an equimolar pool based on the library quantification results and run across 5 High-Output Flow Cell v2.5.

  Dataset EGAD00001009048

• Temporal stability of circulating microRNAs in human serum

  Circulating microRNA biomarkers for disease have been subject to extensive research. However, insufficient consideration of variability in the healthy population has led to few markers achieving the performance necessary in independent follow-up studies to be taken forward to clinical practice. To investigate the natural variation of circulating microRNA over time, we performed small RNA sequencing in a longitudinal study of 66 women with no history of cancer, and determined the distribution and dynamics (via intraclass correlation coefficients, ICCs) of the miRNA profile over 3 time points sampled across 2-5 years. We also investigate abundances in circulation, as well as the impact of normalization, age, BMI, and other sample collection factors on microRNA measurements from small RNA sequencing.

  Study EGAS00001003221

• Characterization of Human Transcriptome by Computational and HTS Approaches

  We have deeply sequenced total RNA and whole exome of six different post-mortem human snap-frozen tissues (brain, liver, lung, striated muscle, kidney, heart) from three unrelated healthy Caucasian individuals (males, aged 47-54) with the aim to characterize post-transcriptional events due to alternative splicing and RNA editing. In addition, to facilitate the detection of RNA editing sites we have also resequenced the entire genome of each individual. Data are also used to investigate tissue-specific abundance of mitochondrial DNA from exomes and its correlation with mitochondrial transcription, mass and respiratory activity. Tissues were obtained from Cureline (South San Francisco, CA, USA). DNA and RNA were purified using the DNeasy Blood and Tissue Kit (Qiagen, Hilden, Germany) and the RNeasy Plus Mini Kit (Qiagen, Hilden, Germany), respectively. Exome capture was performed using the TruSeq Exome Enrichment Kit (Illumina, San Diego, CA) and the sequencing was carried out at IGA Technology Services in Udine (Italy) on Illumina HiSeq 2000 (generating for each tissue approximately 40 millions of 100bp paired-end reads). Strand-oriented RNA reads (2x100bp) were generated using the TruSeq Stranded Total RNA Sample Prep Kit (Illumina, San Diego, CA, USA) and sequenced at IGA Technology Services in Udine (Italy) on Illumina HiSeq 2000 platform. Whole genome resequencing was performed at Personal Genomics in Italy on an Illumina HiSeq 2000 platform (2x100bp) with a mean coverage of 30X. MiRNA-Seq was instead performed at IBBE Institute (Italy) using the TruSeq Small RNA Sample Prep Kit on Illumina MiSeq platform. Preliminary bioinformatics analyses have been performed to check quality using FASTQC software and inconsistent read regions have been trimmed using trim_galore. All reads were mapped onto the human reference genome (version hg19) using GSNAP.

  Study phs000870

• Whole-exome sequencing and RNA-seq of paired normal-tumour samples from MMR-proficient early-onset colorectal cancer patients

  This dataset contains fastq files obtained from paired samples of normal colonic mucosa and tumour tissue, collected from 19 patients diagnosed with MMR-proficient colorectal cancer at an age of 50 years or younger. It includes both whole-exome sequencing and RNA-seq data, generated on the Illumina HiSeq 3000 platform. Additionally, the dataset includes phenotypic information from the patients.

  Dataset EGAD50000001844

• National Cancer Institute (NCI) Adding Hispanics to Ongoing GWAS in Colorectal Cancer

  Genome-wide association studies (GWAS) of colorectal cancer (CRC) have been instrumental in identifying a number of common susceptibility loci in Non Hispanic (NH)-White populations, and a NCI priority is to extend GWAS findings to other populations to address racial/ethnic disparities in cancer susceptibility. Currently, GWA studies of CRC in NH-Whites, Japanese and African-Americans are ongoing. We propose a complementary study to address this critical research area in Hispanics. Hispanics represent the fastest growing ethnic population in the U.S. and have been largely understudied in terms of genetic susceptibility to cancer. There are noted differences in incidence, survival and mortality in CRC by ethnic/racial groups. Hispanics often present with CRC at a younger age and have a significantly greater incidence of stage IV tumors or metastatic disease compared to NH-Whites. We propose to conduct a large, cost-efficient, population-based GWAS in Hispanics by building upon existing NIH-funded resources, the Colon Cancer Family Registry (Colon CFR) and the Multiethnic Cohort Study (MEC). We plan to recruit 2,500 Hispanic men and women diagnosed with CRC between 01/2008 to present using cancer registries in California, physican referrals and familial referrals. Risk factor/diet questionnaires, pathology reports, Oragene saliva samples (for genotyping), optional blood samples (for genotyping and biometric analysis) and tumor blocks (for MSI testing) will be collected using methodologies developed in the Colon CFR/MEC. Cases of CRC in the MEC (currently 473; anticipated 600 at end) will also be included. Population-based Hispanic individuals without a diagnosis of CRC participating in other GWA studies in the MEC (n=3,900, U01HG004726, Haiman) will be used as controls. We will genotype all 3,100 cases using the Illumina 1M array and use available genotype and epidemiologic data collected on 3,900 controls. Our statistical analyses will include: single-SNP and haplotype effects, gene-environment interactions and heterogeneity by MSI, tumor subtype and family history of CRC. We will replicate findings in a second-stage using CRC cases and controls from Mexico (1,000 cases and 1,000 controls, EU FP7 funding, CHIBCHA, Carvajal-Carmona/Tomlinson). We will also examine heterogeneity of the risk estimates by ethnicity/race by leveraging GWA data on NH-Whites (2,142 cases, 1,909 controls, U01 CA122839, Casey), (4,000 cases, 6,000 NH-White controls, UK-CHIBCHA, Tomlinson), Colombians (2,000 cases and 2,000 controls, CHIBCHA), Japanese (1,000 cases and 1,000 controls) and African-Americans (1,500 cases and 1,500 controls, R01CA126895, Le Marchand). We will genotype replicated significant SNPs in our main and combined analysis in several Hispanic populations (note: studies funded by EU or NIH for data collection but not GWAS), including 800 Puerto Ricans, 2,000 Brazilians, 2,000 Argentineans and 3,000 Spanish/Portuguese, to assess generalizability of findings. We will examine the differences in inflammatory gene transcription dynamics in leukocytes (from blood sample collection) by fatigue level (as assessed from study questionnaire data). This study will have a high impact by addressing the key question of racial/ethnic disparities related to genetic susceptibility to CRC, will provide translational guidelines on biological mechanisms during the cancer survivorship period to increase quality of life among cancer survivors, and will enable further growth and investment into research among Hispanics by providing a resource of genetic data and biospecimens, which is lacking.

  Study phs001193

• Single-cell RNA-seq of immune cell subsets isolated from human tumors (Kaptein et al., 2024)

  Single-cell RNA-seq profiling of effector (KLRG1+ PD1-), transitional (KLRG1+ PD1-intermediate), dysfunctional (CD39+ PD1+) and memory (IL7Rα+) CD8 T cells isolated from 4 human tumors (1 melanoma, 1 renal carcinoma and 2 ovarian carcinoma). Respective cell populations were identified and isolated using FACS. FASTQ files contain gene expression data, feature barcode antibody capture or TCR data on individual CD8 T cell subsets across the 4 different samples.

  Dataset EGAD50000000376

• Single-cell RNA-seq of matched primary GBM tumours, patient-derived organoids, and gliomasphere lines

  Single-cell RNA-seq of primary GBM tumours and matched patient-derived organoids and gliomasphere lines. Obtained using the 10X Genomics single-cell 3' expression solution (v2 chemistry). Primary samples and PDOs from 12 tumours from 10 patients (10 primary, two recurrent), and gliomasphere lines from a subset of five tumours. Samples were obtained from two spatially distinct regions of each tumour.

  Dataset EGAD00001007936