12214 results for "cancer rna-seq"

in 38.33 milliseconds.

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0148_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0148_001 for Triple negative breast cancer patient-derived xenograft SA535X4XB02498

  Dataset EGAD00001011221

• 10x Single Cell Gene Expression Library TENX048

  10x Single Cell Gene Expression library TENX048 for Triple negative breast cancer sample SA535X5XB02895

  Dataset EGAD00001011222

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0146_006

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0146_006 for Triple negative breast cancer sample SA535X6XB03099

  Dataset EGAD00001011223

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0175_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0175_001 for Triple negative breast cancer patient-derived xenograft SA535X6XB03101

  Dataset EGAD00001011224

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0182_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0182_001 for Triple negative breast cancer patient-derived xenograft SA535X7XB03304

  Dataset EGAD00001011225

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0189_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0189_001 for Triple negative breast cancer patient-derived xenograft SA535X7XB03448

  Dataset EGAD00001011226

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0184_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0184_001 for Triple negative breast cancer sample SA535X7XB03305

  Dataset EGAD00001011227

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0146_004

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0146_004 for Triple negative breast cancer sample SA535X7XB03305

  Dataset EGAD00001011228

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0189_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0189_002 for Triple negative breast cancer patient-derived xenograft SA535X8XB03663

  Dataset EGAD00001011229

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0182_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0182_002 for Triple negative breast cancer sample SA535X8XB03431

  Dataset EGAD00001011230

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0184_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0184_002 for Triple negative breast cancer patient-derived xenograft SA535X8XB03434

  Dataset EGAD00001011231

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0173_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0173_002 for Triple negative breast cancer patient-derived xenograft SA535X9XB03617

  Dataset EGAD00001011232

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0173_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0173_001 for Triple negative breast cancer patient-derived xenograft SA535X9XB03616

  Dataset EGAD00001011233

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0195_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0195_001 for Triple negative breast cancer patient-derived xenograft SA535X8XB03664

  Dataset EGAD00001011234

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0206_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0206_002 for Triple negative breast cancer patient-derived xenograft SA535X10XB03696

  Dataset EGAD00001011235

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0206_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0206_001 for Triple negative breast cancer sample SA535X10XB03693

  Dataset EGAD00001011236

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0208_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0208_002 for Triple negative breast cancer patient-derived xenograft SA535X9XB03776

  Dataset EGAD00001011237

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0142_002

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0142_002 for Triple negative breast cancer sample SA1035X4XB02879

  Dataset EGAD00001011238

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0071_000

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0071_000 for Triple negative breast cancer sample SA1035X5XB03015

  Dataset EGAD00001011239

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0076_000

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0076_000 for Triple negative breast cancer sample SA1035X5XB03021

  Dataset EGAD00001011240

• 10x Single Cell Gene Expression Library SCRNA10X_SA_CHIP0079_001

  10x Single Cell Gene Expression library SCRNA10X_SA_CHIP0079_001 for Triple negative breast cancer sample SA1035X6XB03216

  Dataset EGAD00001011241

• The Genomic Landscape of Mongolian Hepatocellular Carcinoma

  Background and Hypotheses: Mongolia has the highest reported incidence of - and mortality from - hepatocellular carcinoma (HCC) in the world, which is between three and seven times higher than that observed in other high-incidence populations, such as South Korea, Thailand, and China. Although its causative factors and underlying tumor biology remain unknown, risk factors such as chronic hepatitis B virus and/or hepatitis C virus infection are highly prevalent. Moreover, Mongolia has an extraordinarily high prevalence of hepatitis delta virus (HDV), more than ten times above the global estimate, which we hypothesize as a major driver of Mongolian hepatocarcinogenesis. By performing a comprehensive and integrative genomic characterization, we aimed to identify robust molecular subclasses and driver features informative of the etiology and progression of the disease. This study, the first one of this nature ever to be conducted on a Mongolian cohort, identifies similarities to previous studies performed on other populations (which reflect general characteristics of liver carcinogenesis) as well as unique features of Mongolian HCC. Study Design and Methods: Clinical information and paired tumor/non-tumor liver tissue samples were obtained from 76 HCC patients undergoing surgery at the National Cancer Center of Mongolia. Transcriptome sequencing and whole exome sequencing were performed on all tumor and non-tumor samples, followed by bioinformatics processing and quality control. Consensus clustering and regularized Cox regression analyses were performed on transcriptome data. Driver mutations and mutational signatures were assessed. These results were compared to those from 373 HCC patients of different races and ethnicities and diverse etiologies. Furthermore, molecular subclass gene signatures from multiple previous studies were assessed for validation. Results and Conclusions: Using a transcriptomics-based unsupervised approach, we identified four molecular subclasses among Mongolian HCC cases, which were associated with different patient survival outcomes. These molecular subclasses were mapped to signatures reported in other HCC cohorts, showing significant associations consistent with survival outcomes. A supervised analysis determined risk scores strongly associated with survival, consistent with the four molecular subclasses. Mutational signature analysis using recently published COSMIC and Environmental Agents Compendia identified signatures with distinct prevalence among HDV+ and HVD- groups. Signatures differentially associated with HDV+ include mutational patterns linked to alkylating agents (such as temozolomide), tobacco chewing and exposures to 1,8-Dinitropyrene and furan, whereas HDV- appeared differentially associated with aristolochic acid II. In addition to common driver mutations (TP53, CTNNB1) frequently found in pan-cancer analysis, Mongolian HCC exhibits several unique drivers (most notably GTF2IRD2B, PNRC2, and SPTA1), the latter of which may be linked to HDV infection. These results suggest the existence of new molecular mechanisms at play in Mongolian hepatocarcinogenesis.

  Study phs002000

• L1-Seq and Genome-Wide SNP Genotyping in a Multiethnic Asian Population

  Insertions of the human-specific subfamily of LINE-1 (L1) retrotransposon are highly polymorphic across individuals and can critically influence the human transcriptome. We hypothesized that L1 insertions could represent genetic variants determining important human phenotypic traits, and performed an integrated analysis of L1 elements and single nucleotide polymorphisms (SNPs) in several human populations. We found that a large fraction of L1s were in high linkage disequilibrium (LD) with their surrounding genomic regions and that they were well-tagged by SNPs. However, L1 variants were only partially captured by SNPs on standard SNP arrays, so that their potential phenotypic impact would be frequently missed by SNP array-based genome-wide association studies. We next identified potential phenotypic effects of L1s by looking for signatures of natural selection linked to L1 insertions; significant extended haplotype homozygosity (EHH) was detected around several L1 insertions. This suggests that some of these L1 insertions may have been the target of recent positive selection.

  Study phs000732

• scRNA-seq of LN and lymphoma stroma

  The activities of non-haematopoietic cells (NHCs), including mesenchymal stromal cells and endothelial cells, in lymphomas, reportedly underlie lymphomagenesis. However, understanding of lymphoma NHCs has been hampered by unexplained NHC heterogeneity, even in normal human lymph nodes (LNs). Here, we constructed a single-cell transcriptome atlas of >100,000 NHCs collected from 27 human samples, including LNs and various nodal lymphomas, revealing 30 distinct subclusters, including some that were previously unrecognized. Notably, this atlas was highly useful for comparative analysis with lymphoma NHCs, which revealed an unanticipated landscape of subcluster-specific changes in gene expression and interaction with malignant cells in follicular lymphoma NHCs, facilitating our understanding of stromal remodelling in lymphoma and potentially suggesting novel biomarkers. Our study largely updates NHC taxonomy in human LNs and pertains to analysis of disease status, providing a rich resource and deeper insights into LN and lymphoma biology and offering to advance lymphoma management and therapy.

  Study EGAS00001005732

• Germ Cell and Associated Heme Malignancies Evolve from a Common Shared Precursor

  Germ cell tumors (GCTs) are the most common cancer in men between the ages of 15-40. While most patients are cured, those with disease arising in the mediastinum have distinctly poor outcomes. One in every 17 patients with primary mediastinal non-seminomatous GCTs develop an incurable hematologic malignancy and prior data intriguingly suggests a clonal relationship exists between hematologic malignancies and GCTs in these cases. To date however, the precise clonal relationship between GCTs and the diverse additional somatic malignancies arising in such individuals has not been determined. Here, we traced the clonal evolution and characterized the genetic features of each neoplasm from a cohort of fifteen patients with GCTs and associated hematologic malignancies. We discovered that GCTs and hematologic malignancies developing in such individuals evolved from a common shared precursor, nearly all of which harbored allelically imbalanced TP53 and/or RAS pathway mutations. Hematologic malignancies arising in this setting genetically resembled mediastinal GCTs rather than de novo myeloid neoplasms. Our findings argue that this scenario represents a unique clinical syndrome, distinct from de novo GCTs or hematologic malignancies, initiated by an ancestral precursor which gives rise to the parallel evolution of GCTs and blood cancers in these patients. Reprinted from PMID: 32897884, with permission from JCI.

  Study phs002231