11754 results for "cancer rna-seq"

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• Single Cell Genome Sequence for DLP+ library A96109A

  Single Cell Genome Sequence for triple negative breast cancer patient-derived xenograft SA501 passage 2 on DLP+ library A96109A

  Dataset EGAD00001009452

• sWGS data of Pap test smears and tumor tissues

  Shallow whole-genome sequencing data divided into three groups: - sWGS data from Pap smears of patients with confirmed high grade serous ovarian cancer - sWGS data from matched tumor tissue (at diagnosis) from the same patients - sWGS data from Pap tests smears of healthy women

  Dataset EGAD00001010141

• Exome sequencing files for "A single mutant clone populates the pancreatic ductal system to generate coexisting neoplastic lesions"

  Files from whole exome sequencing of eight tumors from eight pancreatic cancer patients along with matched PanIN precursor lesion(s) and a matched normal tissue.

  Dataset EGAD00001004044

• Organoid Derivation Project: TGS (2023-06-22)

  Sequencing of tissue samples and their derived organoids from oesophageal, pancreatic and colorectal cancer patients. . This dataset contains all the data available for this study on 2023-06-22.

  Dataset EGAD00001011089

• Whole genome sequencing data of high-grade serous ovarian cancer samples (set 8a)

  Whole genome sequencing data of 26 high-grade serous carcinoma (HGSC) patients (87 samples) sequenced with MGISEQ-2000 and HiSeq X Ten.

  Dataset EGAD00001011304

• WES data of colorectal cancer patients

  35 samples from individuals with colorectal tumors, exome sequencing

  Dataset EGAD00001007745

• CEBP-Beta/IL-1-Beta/ TNF-alpha Feedback Loop Drives Drug Resistance to BCL2 and MDM2 Inhibitors in Monocytic Leukemia Cells

  To uncover the mechanisms of MDM2 inhibitor resistance that are mediated by IL-1, we performed RNAseq on three primary AML patient cells treated with idasanutlin in the presence or absence of IL-1β. These three leukemia samples were chosen because they exhibit high blast counts and are protected by IL-1β when treated with idasanutlin, as determined by apoptosis assay. We observed that a majority of the major p53 pathway targets remained upregulated in the presence of IL-1β. We next performed differential expressed gene (DEG) analysis following IL-1β treatment with and without idasanutlin. IL-1β treatment upregulated 3295, 1353, and 680 genes with ≥1 Log2FC, in each of the three patient samples, and a significant majority of these genes (82%, 62.4%, and 47.6%) remained upregulated in the presence of idasanutlin. Pathway analysis showed that IL-1β upregulated genes were associated with NF-κB, Interleukin-1 family signaling pathways, immune and inflammatory associated genes and myeloid maturation markers. To identify downstream targets that contribute to the protective effect of IL-1β during idasanutlin treatment, we performed DEG analysis on IL-1β + idasanutlin treatment compared to idasanutlin treatment alone. Interestingly, IL-1β induces its own expression and other pro-survival cytokines including TNF, CSF2 (GMCSF), IL-6, CSF3 (GCSF), as well as their respective receptors and/or coactivators, including IL1RL1 and multiple TNF receptors. We verified that IL-1β and TNF-α induce the upregulation of their own membrane-bound forms, their corresponding receptors, CSF2RB, and monocytic markers, as determined by flow cytometry immunophenotyping. Furthermore, IL-1β upregulated a number of pro-inflammatory chemokines, including CXCL13, CXCL2, etc. These findings highlight a pleiotropic role of IL-1β. The RNA expression of these three samples in the presence and absence of IL-1β and idasanutlin treatment will be deposited in dbGaP.

  Study phs003479

• Gene expression signatures associated with chronic endometritis revealed by RNA sequencing

  Chronic endometritis (CE) is a persistent inflammatory condition of the endometrium characterized by the infiltration of plasma cells. CD138 immunohistochemistry is considered to improve the CE diagnosis rate. Using the number of CD138-positive cells equal or greater than five as a diagnostic criterion for CE, we identified 24 CE and 33 non-CE cases among women with infertility. We conducted RNA-sequencing analysis for these 57 cases in total as an attempt to elucidate the molecular pathogenesis of CE and to search for new biomarkers for CE. By comparing CE and non-CE groups, we identified 20 genes upregulated in the endometria of CE patients, including 12 immunoglobulin-related genes and eight non-immunoglobulin genes as differentially expressed genes. The eight genes were MUC5AC, LTF, CAPN9, MESP1, ACSM1, TVP23A, ALOX15, and MZB1. By analyzing samples in the proliferative and secretory phases of the menstrual cycle separately, we also identified four additional non-immunoglobulin genes upregulated in CE endometria: CCDC13 by comparing the samples in the proliferative phase, and OVGP1, MTUS2, and CLIC6 by comparing the samples in the secretory phase. Although the genes upregulated in CE may serve as novel diagnostic markers of CE, many of them were upregulated only in a limited number of CE cases showing an extremely high number of CD138-positive cells near or over one hundred. Exceptionally, TVP23A was upregulated in the majority of CE cases regardless of the number of CD138-positive cells. The upregulation of TVP23A in the endometria of CE cases may reflect the pathophysiology of a cell-type or cell-types intrinsic to the endometrium rather than the accumulation of plasma cells. Our data, consisting of clinical and transcriptomic information for CE and non-CE cases, helped us identify gene expression signatures associated with CE.

  Study JGAS000621

• Single cell genomic variation induced by mutational processes in cancer

  How cell-to-cell copy number alterations that underpin genomic instability in human cancers drive genomic and phenotypic variation, and consequently cancer evolution, remains understudied. Here, applying scaled single cell whole genome sequencing to wildtype, TP53-deficient and TP53/BRCA1/2-deficient mammary epithelial cells (n=13,818 genomes) and primary triple negative breast (TNBC) and high grade serous ovarian cancers (HGSC) (n=22,057 genomes), we identified three distinct ‘foreground’ mutational patterns defined by cell-to-cell copy number and structural variation properties occurring in the ‘background’ of cancer-associated genomic instability. Cell and clone-specific high level amplifications (HLAMPs), parallel allele-specific copy number alterations and copy number segment length variation (serrate structural variations) exhibited measurable phenotypic and evolutionary consequences. Clone-specific HLAMPs in known oncogenes were highly prevalent in fold back inversion (FBI)-bearing tumors and associated with increased clone-to-clone phenotypic variation in gene expression inferred from matched single cell RNASeq. Parallel allele-specific alterations were linked with phylogenetically determined evolutionary diversity and clone-specific mono-allelic expression. Serrate patterns present across all clones were increased in FBI tumors, most prominently on a tetraploid background and were highly correlated with increased genomic diversity of cellular populations. Taken together, we show that cell-to-cell structural-copy number variation significantly impacts the phenotypes and evolutionary diversity of TNBC and HGSC, revealing previously hidden genomic states of cancer cells.

  Study EGAS00001006343

• Jackson Heart Study (JHS) Cohort

  The Jackson Heart Study (JHS) is a large, community-based, observational study whose participants were recruited from urban and rural areas of the three counties (Hinds, Madison and Rankin) that make up the Jackson, MS metropolitan statistical area (MSA). Participants were enrolled from each of 4 recruitment pools: (1) random, 17%, (2) volunteer, 30%, (3) currently enrolled in the Atherosclerosis Risk in Communities (ARIC) Study, 31%, and (4) secondary family members, 22%. The final cohort of 5,306 participants included 6.59% of all African American Jackson MSA residents, aged 35-84, during the baseline exam (N-76,426, US Census 2000). Among these, approximately 3,600 gave consent that allows genetic research and deposition of data into dbGaP. Major components of three clinic examinations (see study history) include medical history, physical examination, blood/urine analytes, and interview questions on areas such as physical activity, stress, coping and spirituality, racism and discrimination, socioeconomic position, and access to health care. Extensive clinical phenotyping includes anthropometrics, electrocardiography, carotid ultrasound, ankle-brachial blood pressure index, echocardiography, CT chest and abdomen for coronary and aortic calcification, liver fat, and subcutaneous and visceral fat measurements, and cardiac MRI. At 12-month intervals after the baseline clinic visit (Exam 1), participants have been contacted by telephone to update information, confirm vital statistics, document interim medical events, hospitalizations and functional status, and obtain additional sociocultural information. Questions about medical events, symptoms of cardiovascular disease and functional status are repeated annually. Ongoing cohort surveillance includes abstraction of medical records and death certificates for relevant International Classification of Diseases (ICD) codes and adjudication of nonfatal events and deaths. CMS data are currently being incorporated into the dataset. Note: Regarding date variables warehoused in the Jackson Heart Study (JHS) Cohort on dbGaP, the coordinating center has developed an algorithm that will systematically review and de-identify any of the (nearly 100) date-related variable types stored in the data package. To simultaneously minimize (i) de-identifiability of the data and (ii) impact on analyses utilizing sensitive data elements, a participant-level random number was generated to avoid the necessity of sharing any potentially sensitive data. The coordinating center maintains an archived linkage of these data in their raw form and regularly reviews ad hoc requests to utilize the raw data on a project-by-project basis. The JHS Cohort is utilized in the following dbGaP substudies. To view genotypes, other molecular data, and derived variables collected in these substudies, please click on the following substudies below or in the "Substudies" section of this top-level study page phs000286 JHS Cohort. phs000402 ESP Heart GO JHS phs000498 JHS Allelic Spectrum Seq phs000499 JHS CARephs001069 MI Gen JHS phs001098 T2D GENES Exome Seq phs001356 Exome Chip phs002256 Cardiometabolic Renal Proteomics phs002369 Metabolomics Insulin Resistance

  Study phs000286