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Ena-DATASET-UTSW-08-01-2018-20:28:44:546-61 - samples

ABSTRACT: the dataset contains RNA bam files of Renal Cell Carcinoma patients, which belongs to "An Empirical Approach Leveraging Tumorgrafts to Dissect the Tumor Microenvironment in Renal Cell Carcinoma Identifies Missing Link to Prognostic Inflammatory Factors"

PROVIDER: EGAD00001003895 | EGA |

REPOSITORIES: EGA

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02d8c805-a5f9-4c79-8fc2-af8a7db0fdd6 - samples

Project description:the dataset contains Exome and RNA fastq files of Renal Cell Carcinoma patients, which belongs to "Integrated genomic analysis of tumor thrombus"/

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An Empirical Approach Leveraging Tumorgrafts to Dissect the Tumor Microenvironment in Renal Cell Carcinoma Identifies Missing Link to Prognostic Inflammatory Factors

Project description:We undertook an empirical approach to dissect the tumor microenvironment by developing a bulk tumor dissection algorithm, DisHet, and leveraging RNA-Seq data of tumorgrafts (patient-derived tumors implanted in mice), in which only the tumor cell component expands. We found that approximately 65% of previously defined immune signature genes are not abundantly expressed in the Renal Cell Carcinoma (RCC) microenvironment, and we identified more than two times as many novel immune/stromal transcripts. By using refined immune/stroma-specific genes and genomics, electronic medical record data and imaging data of 1084 RCC patients, we discovered a highly-inflamed pan-RCC subtype enriched for Treg cells, NK cells, Th1 cells, neutrophils, macrophages, B cells, and CD8+ T cells. This inflamed subtype (IS) is enriched for aggressive RCCs, including BAP1-deficient clear-cell and type 2 papillary tumors. Interestingly, IS is correlated with systemic manifestations of inflammation in patients such as thrombocytosis and anemia, whose pathogenesis were poorly defined and have been predictors of poor prognosis in RCC. Indeed, we discovered that IS is a strong predictor of poor survival. Lastly, our analyses show that tumor cells may drive stromal immune response. Overall, these data provide a missing link between the tumor cells, the tumor microenvironment, and systemic factors.

| EGAS00001002786 | EGA

dePillis2013 - Mathematical modeling of regulatory T cell effects on renal cell carcinoma treatment

Project description:Mathematical modeling of regulatory T cell effects on renal cell carcinoma treatment Lisette dePillis 1, , Trevor Caldwell 2, , Elizabeth Sarapata 2, and Heather Williams 2, 1. Department of Mathematics, Harvey Mudd College, Claremont, CA 91711 2. Harvey Mudd College, Claremont, CA 91711, United States, United States, United States Abstract We present a mathematical model to study the effects of the regulatory T cells (Treg) on Renal Cell Carcinoma (RCC) treatment with sunitinib. The drug sunitinib inhibits the natural self-regulation of the immune system, allowing the effector components of the immune system to function for longer periods of time. This mathematical model builds upon our non-linear ODE model by de Pillis et al. (2009) [13] to incorporate sunitinib treatment, regulatory T cell dynamics, and RCC-specific parameters. The model also elucidates the roles of certain RCC-specific parameters in determining key differences between in silico patients whose immune profiles allowed them to respond well to sunitinib treatment, and those whose profiles did not. Simulations from our model are able to produce results that reflect clinical outcomes to sunitinib treatment such as: (1) sunitinib treatments following standard protocols led to improved tumor control (over no treatment) in about 40% of patients; (2) sunitinib treatments at double the standard dose led to a greater response rate in about 15% the patient population; (3) simulations of patient response indicated improved responses to sunitinib treatment when the patient's immune strength scaling and the immune system strength coefficients parameters were low, allowing for a slightly stronger natural immune response. Keywords: Renal cell carcinoma, mathematical modeling., sunitinib, immune system, regulatory T cells.

2024-09-02 | BIOMD0000000908 | BioModels

Exome sequencing identifies frequent mutation of the SWI/SNF complex gene PBRM1 in renal carcinoma - samples

Project description:Exome sequencing identifies frequent mutation of the SWI/SNF complex gene PBRM1 in renal carcinoma

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Expression data in ezrin-knockdown ec109 cells(PSE1) and control(PS)

Project description:Ezrin belongs to the ezrin¨Cradixin¨Cmoesin (ERM) family proteins, which cross-link actin cytoskeleton and plasma membrane, and is actively involved in regulating the growth and metastatic capacity of cancer cells. Esophageal squamous cell carcinoma (ESCC) is one of the most fatal malignancies in the world and the expression of ezrin in ESCC has been described only recently, but its roles and mechanism still remained unclear. we have used the pSUPER RNAi system to stably suppress the expression of the ezrin gene in EC109, an esophageal squamous carcinoma cell line, and then cDNA microarray was performed to explore some changed genes with ezrin silence. Keywords: Gene expression after RNAi

2006-11-09 | GSE6233 | GEO

Mus musculus

Project description:ADAMTSL2, a missing link in Wnt/ß-catenin regulated CNS vascular development

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RNAseq data comparing iKPC cells overexpressing WT-USP21 and ED-USP21

Project description:USP21 belongs to ubiquitin specific protease (USP) family. To dissect the molecular mechanisms that regulated by USP21 overexpression in PDAC cells, we conducted RNA-seq analysis of iKPC PDAC cells overexpressing wild-type USP21 (WT-USP21) and enzyme dead USP21 (ED-USP21).

2019-09-04 | GSE134686 | GEO

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Project description:Renal Cell Carcinoma

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Project description:renal cell carcinoma

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Array-based CGH analysis of ccRCC derived cell lines

Project description:arrayCGH profiling of 10 cell lines derived from clear cell Renal Cell Carcinoma The goal of this study was to make a detailed genomic profile of these cell lines, including aCGH, miRNA expression, and mRNA expression profiles. The miRNA and mRNA expressiion profiles are submitted to GEO separately. One paper is in preparation to link the miRNA expression data with the arayCGH data. For mRNA expression data see GEO Series accession number GSE20491. CGH arrays used to analyse 10 cell lines

2012-11-01 | E-GEOD-34979 | biostudies-arrayexpress

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