Project description:We aim to identify profiling of circRNAs in renal tissue from renal cell carcinoma patients. In this study, seven paired frozen carcinoma tissues as well as normal tissues from patients with renal cell carcinoma were used for circRNA profiling by second generation of RNA sequencing.
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.
Project description:We aim to identify profiling of circRNAs in renal biopsies from lupus nephritis (LN) patients. In this study, seven frozen renal biopsies from patients with LN class IV were used for circRNA profiling by second generation of RNA sequencing. Three kidney tissue 5 cm far from renal tumors were used as normal control .
Project description:The proteome of clinical tissue samples diagnosed with clear cell renal cell carcinoma (ccRCC) and papillary renal cell carcinoma (pRCC) were evaluated analyzed along with the dataset identifier PXD022018 to establish a potential discriminative biomarker panel of proteins for these tumors subtypes.
Project description:Transcriptional profiling of human renal clear-cell carcinoma cells comparing control unexpressing MUC1 cells (82-F7 and 82-65 samples) with MUC1 overexpressing cells (83-2 and 83-5 samples)
Project description:Arraystar Human circRNA Microarray is designed for the global profiling of human circRNAs. In this study, we applied a circRNA microarray to screen the potential biomarker for HCC. 20 samples extracted from plasma samples including HCC group before operation, and after operation, CH group and control group. Each group contained five samples.
Project description:Transcriptional profiling of human renal clear-cell carcinoma cells comparing control unexpressing MUC1 cells (82-F7 and 82-65 samples) with MUC1 overexpressing cells (83-2 and 83-5 samples) Goal was to determine the effect of MUC1 expression on global gene expression