Ontology highlight
ABSTRACT:
PROVIDER: PRJNA266409 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Renal cells Carcinoma (RCC) Genome variation profiling by genome tiling array with Agilent CGH array
| PRJNA266413 | ENA
Project description:Renal cells Carcinoma (RCC) Genome variation profiling by genome tiling array with Nimblegen CGX6 platform
| PRJNA266412 | ENA
Project description:Renal cells Carcinoma (RCC) Genome variation profiling by genome tiling array with Perkin Elmer Constituionnal Chip platform
| PRJNA266411 | ENA
Project description:In this study, 19 tumor samples from patients with renal cell carcinoma (RCC)-end-stage renal disease (ESRD) were analyzed by array comparative genomic hybridization (array CGH) using the Agilent Whole Human Genome 4× Array.
2012-04-02 | GSE33117 | GEO
Project description:In this study, eighty tumor samples from 63 patients with renal cell carcinoma (RCC)-end-stage renal disease (ESRD) were analyzed by array comparative genomic hybridization (array CGH) using the Agilent Whole Human Genome 4×44K Oligo Micro Array.
2012-03-24 | GSE28601 | GEO
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
Project description:In this study, 19 tumor samples from patients with renal cell carcinoma (RCC)-end-stage renal disease (ESRD) were analyzed by array comparative genomic hybridization (array CGH) using the Agilent Whole Human Genome 4× Array. 19 cystic disease samples from patients with RCC-ESRD
2012-04-01 | E-GEOD-33117 | biostudies-arrayexpress
Project description:In this study, eighty tumor samples from 63 patients with renal cell carcinoma (RCC)-end-stage renal disease (ESRD) were analyzed by array comparative genomic hybridization (array CGH) using the Agilent Whole Human Genome 4×44K Oligo Micro Array. 79 tumor samples from 63 patients with RCC-ESRD
2012-03-24 | E-GEOD-28601 | biostudies-arrayexpress
Project description:The aim of this study was to compare effect of everolimus on growth of different renal cell carcinoma (RCC) populations and develop design for experiments to measure the early response of everolimus in clear cell RCC (ccRCC) cell lines including renal cancer stem cells. Gene expression profiling using microarray was performed to determine the early response to everolimus after 3 days of treatment with optimizied concentration of drug in two ccRCC cell lines 1) parental clear cell renal cell carcinoma ccRCC-PCSC (HKPCSC -human parental kidney cancer stem cells) and 2) ccRCC-CSC - clear cell renal cell carcinoma -cancer stem cells (HKCSC - human kidney cancer stem cells).
2018-05-31 | GSE106819 | GEO
Project description:Progesterone receptor membrane component 1 (PGRMC1) is widely observed at elevated expression levels in multiple human cancers. However, the associations of PGRMC1 with renal cancer are not clear and merit further study. In this report, we report a systematic, integrative biological assessment of PGRMC1 in renal cell carcinoma (RCC), encompassing quantitative proteomics, immunohistochemical profiling, and its clinicopathologic significance. We identified that PGRMC1 is increased to 3.91-fold in RCC tissues when compared with its autologous para-cancerous tissues by a quantitative proteomic analysis. PGRMC1 was widely increased in 63.7% RCC samples (86/135) by immunohistochemical validation. Meanwhile the average expression level of serum PGRMC1 in RCC patients (n=18) was significantly increased to 1.67-fold compared with the healthy persons. Moreover PGRMC1 upregulation is correlated with tumor malignancy level and a poor overall survival for RCC. And PGRMC1 overexpression promotes cell proliferation for renal cancer cells in vitro. Our findings demonstrate that PGRMC1 is a novel potential biomarker and therapeutic target for renal cancer due to PGRMC1 roles in promoting RCC-associated phenotypes in vitro and in vivo.
2017-01-24 | PXD004595 | Pride