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Dataset Information

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Omics score: 144

Normal and Renal Cell Carcinoma Kidney Tissue, Human

ABSTRACT: Each total RNA sample is hybridized to two different arrays: Affymetrix U133A (GPL96) and U133B (GPL97). For most of the normal tissue samples there is a renal clear cell carcinoma sample from the same patient. There is no matching tumor sample for normal sample N1. For most of the renal clear cell carcinoma samples there is a corresponding adjacent normal tissue sample from the same patient. There are no matching normal tissue samples for C011 or C032. Keywords = kidney Keywords = renal Keywords = RCC Keywords = carcinoma Keywords = cancer Keywords: parallel sample

OTHER RELATED OMICS DATASETS IN: PRJNA87743

ORGANISM(S): Homo Sapiens

SUBMITTER: Herbert T Cohen Norman P Gerry Louis S Liou Michael F Christman Marc E. Lenburg Garrett M Frampton Marc E Lenburg

PROVIDER: E-GEOD-781 | ArrayExpress | 2003-11-25

SECONDARY ACCESSION(S): GDS505GDS507GSE781PRJNA87743

REPOSITORIES: GEO, ArrayExpress

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			Action	DRS
	E-GEOD-781.idf.txt	Idf
	E-GEOD-781.processed.1.zip	Processed
	E-GEOD-781.raw.1.zip	Raw
	E-GEOD-781.sdrf.txt	Txt

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Publications

Previously unidentified changes in renal cell carcinoma gene expression identified by parametric analysis of microarray data.

Lenburg Marc E ME Liou Louis S LS Gerry Norman P NP Frampton Garrett M GM Cohen Herbert T HT Christman Michael F MF

BMC cancer 20031127

<h4>Background</h4>Renal cell carcinoma is a common malignancy that often presents as a metastatic-disease for which there are no effective treatments. To gain insights into the mechanism of renal cell carcinogenesis, a number of genome-wide expression profiling studies have been performed. Surprisingly, there is very poor agreement among these studies as to which genes are differentially regulated. To better understand this lack of agreement we profiled renal cell tumor gene expression using ge ...[more]

PMID: 14641932

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Normal and Renal Cell Carcinoma Kidney Tissue, Human

Project description:Each total RNA sample is hybridized to two different arrays: Affymetrix U133A (GPL96) and U133B (GPL97). For most of the normal tissue samples there is a renal clear cell carcinoma sample from the same patient. There is no matching tumor sample for normal sample N1. For most of the renal clear cell carcinoma samples there is a corresponding adjacent normal tissue sample from the same patient. There are no matching normal tissue samples for C011 or C032. Keywords = kidney Keywords = renal Keywords = RCC Keywords = carcinoma Keywords = cancer Keywords: parallel sample

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Next Generation Sequencing of small non-coding RNAs in the tissue of patients with renal cell carcinoma

Project description:Non-coding RNAs play an important role in the pathogenesis of human malignancies. So far, microRNAs have been investigated in detail in clear cell renal cell carcinoma, but the knowledge about other small non-coding RNAs like snoRNA, tRNA and piRNA remains small. There is increasing evidence that these non-coding RNAs are also involved in regulation of gene expression, and we therefore performed small RNA sequencing in a cohort of 18 corresponding normal and malignant tissue samples from patients with clear cell renal cell carcinoma. We observed differential expression of microRNAs, but also some dysregulated tRNA and snoRNA in clear cell renal cell carcinoma tissue Overall design: small RNA expression profiling in clear cell renal cell carcinoma and corresponding normal renal tissues samples Please note that each 'ccRCC' sample is followed by the corresponding normal renal tissue sample. For example, the '2014_001_002' is the corresponding normal tissue for '2014_001_001' ccRCC sample, except the last two samples 2014_001_028 and 2014_001_029 are listed in an inverse order.

2016-10-31 | GSE73342 | GEO

Loss of 5-hydroxymethylcytosine is linked to gene body hypermethylation in kidney cancer

Project description:Aberrant DNA methylation is common in cancer. To associate DNA methylation with gene function, we performed RNAseq upon tumor tissue and matched normal tissues of two ccRCC (clear cell renal cell carcinoma) patients. To quantify 5mC and 5hmC level in each CG site at genome-wide level, we performed BS-seq and TAB-seq upon tumor tissue and matched normal tissues of two ccRCC (clear cell renal cell carcinoma) patients, respectively. mRNA profiles of tumor and matched normal tissues from two ccRCC patients were generated by deep sequencing, using Hiseq 2000. Single-nucleotide-resolution, whole-genome, 5mC and 5hmC profiles of tumor and matched normal tissues from two ccRCC (clear cell renal cell carcinoma) patients were generated by deep sequencing, using Hiseq 2000.

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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.

2020-01-06 | BIOMD0000000908 | BioModels

Genome-wide gene expression profiles of clear cell renal cell carcinoma; Identification of molecular targets for treatment of renal cell carcinoma

Project description:In order to clarify the molecular mechanism involved in renal carcinogenesis, and identify molecular targets for diagnosis and treatment, we analyzed genome-wide gene expression profiles of 15 surgical specimens of clear cell renal cell carcinoma (RCC), compared to normal renal cortex, using a combination of laser microbeam microdissection (LMM) with a cDNA microarray representing 27,648 genes. Tissue samples of surgically-resected clear cell renal cell carcinoma (ccRCC) and their corresponding clinical information were obtained from patients with written informed consent. The total of 15 cancer patients (6 women and 9 men; median age, 66; range, 36-75 years) that had been confirmed histologically as ccRCC were selected for this study. Two to three pieces of cancer tissue had been taken from each patient at the time of radical nephrectomy. Normal tissue had been obtained from the distant region from cancer area in the resected kidney tissue. These samples were immediately embedded in TissueTek OCT compound (Sakura, Tokyo, Japan), frozen, and stored at -80°C. The frozen tissues were sliced into 8-μm sections using a cryostat (Sakura) and then stained with H&E for histological examination. We used LMM technology to collect pure populations of ccRCC cells as well as non-cancerous renal cortex. A mixture of normal renal cortex cells in kidney tissues from 11 patients was prepared as a universal control. Experiments were performed using 6 sets of slides (slide set 1-6 corresponding to ID_REF 1-27648).

| E-GEOD-39364 | BioStudies

Decreased capillary density in renal cell carcinoma: Evidence from a case report with micro-computerized tomography.

Project description:RATIONALE:Conventional computerized tomography (CT) examination can differentiate renal cortical tumor from urothelial carcinoma on the basis of the highly contrast-enhanced vessels in renal cortical tumors. However, the capillary distribution of renal cell carcinoma (RCC) has been under-investigated. Here, we present a micro-CT image of tumor tissue in a patient with RCC. PATIENT CONCERNS:The patient was a 72-year-old woman with a past history of diabetes mellitus and hypertension. She did not have tumor-related symptoms. DIAGNOSIS AND INTERVENTIONS:The tumor was diagnosed using abdominal CT during her yearly routine health check. After radical nephrectomy, the tumor was subjected to pathological examination and micro-CT imaging. Pathological analysis confirmed a clear cell renal carcinoma. The capillary distribution of the tumor was significantly lesser than that of the normal cortex on micro-CT image. LESSONS:Microvessels of RCC can be detected by micro-CT. We also found that the distribution of microvessels was uneven and lower than that in the normal cortex in this case. For a more general diagnosis, more micro-CT images of RCC tumors are needed.

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Napsin A Expression in Human Tumors and Normal Tissues.

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Impact of Ischemia and Procurement Conditions on Gene Expression in Renal Cell Carcinoma

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