Genome-wide gene expression profiles of clear cell renal cell carcinoma; Identification of molecular targets for treatment of renal cell carcinoma
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE39364 | GEO | 2012/07/16
SECONDARY ACCESSION(S): PRJNA170718
REPOSITORIES: GEO
Similar Datasets
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).
2012-07-15 | E-GEOD-39364 | biostudies-arrayexpress
Project description:Renal cell carcinoma (RCC) is the most common neoplasm of the adult kidney. Currently, there are no biomarkers for the diagnostic, prognostic, or predictive applications in RCC. MicroRNAs (miRNAs) are short non protein-coding RNAs that negatively regulate gene expression and have been shown to be involved in cancer. We analyzed a total of 70 matched pairs of clear cell RCC (ccRCC) and normal kidney tissues from the same patients by microarray analysis and validated our results by quantitative real time PCR. We identified 166 miRNAs significantly dysregulated in ccRCC. MiR-122, miR-155 and miR-210 had the highest fold changes of overexpression while miR-200c, miR-335, and miR-218 were the most downregulated. We performed extensive bioinformatics analysis including a combinatorial analysis of previously reported miRNAs dysregulated in RCC and extensive target prediction analysis. Many miRNAs were predicted to target a number of genes involved in RCC pathogenesis. Our results showed that miRNA dysregulation in RCC can be attributed in part, to chromosomal aberrations, the co-regulation of miRNA clusters, and co-expression with host genes. We also correlated miRNA expression with clinical characteristics and found miR-155 expression was correlated with ccRCC tumor size. In conclusion, our analysis showed that a number of miRNAs are dysregulated in ccRCC and may contribute to kidney cancer pathogenesis by targeting more than one key molecule. We identified mechanisms that may contribute to miRNA dysregulation in ccRCC. Dysregulated miRNAs represent potential biomarkers for kidney cancer. We preformed a miRNA microarray on 20 pairs of matched primary clear cell renal cell carcinoma (ccRCC) and normal kidney tissue from the same patient (St. Michael's Hospital, Toronto, Canada). One matched pair was used per array for a total of 20 arrays. Microarray results were validated by quantitative real time PCR using an independent set of 50 matched pairs of ccRCC and normal kidney tissue from the same patient.
2010-07-22 | E-GEOD-23085 | biostudies-arrayexpress
Project description:Renal cell carcinoma (RCC) is the most common neoplasm of the adult kidney. Currently, there are no biomarkers for the diagnostic, prognostic, or predictive applications in RCC. MicroRNAs (miRNAs) are short non protein-coding RNAs that negatively regulate gene expression and have been shown to be involved in cancer. We analyzed a total of 70 matched pairs of clear cell RCC (ccRCC) and normal kidney tissues from the same patients by microarray analysis and validated our results by quantitative real time PCR. We identified 166 miRNAs significantly dysregulated in ccRCC. MiR-122, miR-155 and miR-210 had the highest fold changes of overexpression while miR-200c, miR-335, and miR-218 were the most downregulated. We performed extensive bioinformatics analysis including a combinatorial analysis of previously reported miRNAs dysregulated in RCC and extensive target prediction analysis. Many miRNAs were predicted to target a number of genes involved in RCC pathogenesis. Our results showed that miRNA dysregulation in RCC can be attributed in part, to chromosomal aberrations, the co-regulation of miRNA clusters, and co-expression with host genes. We also correlated miRNA expression with clinical characteristics and found miR-155 expression was correlated with ccRCC tumor size. In conclusion, our analysis showed that a number of miRNAs are dysregulated in ccRCC and may contribute to kidney cancer pathogenesis by targeting more than one key molecule. We identified mechanisms that may contribute to miRNA dysregulation in ccRCC. Dysregulated miRNAs represent potential biomarkers for kidney cancer.
2010-07-22 | GSE23085 | GEO
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:Renal cell carcinoma is the most common neoplasm of the adult kidney. A few subtypes of RCC include papillary RCC (pRCC), chromophobe RCC (chRCC) and the benign oncocytoma tumor. In some cases, distinguishing between the RCC subyptes is difficult. We performed a mircroRNA (miRNA) microarray to determine differential miRNA expression between pRCC, chRCC, and oncocytoma. We performed a miRNA microarray on 10 tumor samples of each papillary renal cell carcinoma (pRCC), chromophobe renal cell carcinoma (chRCC), and oncocytoma.
2012-08-13 | E-GEOD-39790 | biostudies-arrayexpress
Project description:The clear cell subtype of renal cell carcinoma (RCC) is the most lethal and prevalent cancer of the urinary system. To investigate the molecular changes associated with malignant transformation in clear cell RCC, the gene expression profiles of matched samples of tumor and adjacent non-neoplastic tissue were obtained from 6 patients. A custom-built cDNA microarray platform was used, comprising 2,292 probes that map to exons of genes and 822 probes for noncoding RNAs mapping to intronic regions. Intronic transcription was detected in all normal and neoplastic renal tissues. A subset of 55 transcripts was significantly down-regulated in clear cell RCC relative to the matched non-tumor tissue as determined by a combination of two statistical tests and leave-one-out patient cross-validation. Among the down-regulated transcripts, 49 mapped to untranslated or coding exons and 6 were intronic relative to known exons of protein-coding genes. Lower levels of expression of SIN3B, TRIP3, SYNJ2BP and NDE1 (p < 0.02), and of intronic transcripts derived from SND1 and ACTN4 loci (p < 0.05), were confirmed in clear cell RCC by Real-time RT-PCR. A subset of 25 transcripts was deregulated in additional 6 non-clear cell RCC samples, pointing to common transcriptional alterations in RCC irrespective of the histological subtype or differentiation state of the tumor. Our results indicate a novel set of tumor suppressor gene candidates, including noncoding intronic RNAs, which may play a significant role in malignant transformations of normal renal cells. Fluorescently labeled cDNA targets derived from tumor and non-tumor tissue from each patient were combined and hybridized to spotted cDNA microarrays. For each patient, a replicate hybridization with dye-swap of labeled targets was performed. For each tissue (tumor and adjacent non-tumor tissue), there are 4 replicate mesurements for each probe.
2010-06-25 | E-GEOD-7367 | biostudies-arrayexpress
Project description:The clear cell subtype of renal cell carcinoma (RCC) is the most lethal and prevalent cancer of the urinary system. To investigate the molecular changes associated with malignant transformation in clear cell RCC, the gene expression profiles of matched samples of tumor and adjacent non-neoplastic tissue were obtained from 6 patients. A custom-built cDNA microarray platform was used, comprising 2,292 probes that map to exons of genes and 822 probes for noncoding RNAs mapping to intronic regions. Intronic transcription was detected in all normal and neoplastic renal tissues. A subset of 55 transcripts was significantly down-regulated in clear cell RCC relative to the matched non-tumor tissue as determined by a combination of two statistical tests and leave-one-out patient cross-validation. Among the down-regulated transcripts, 49 mapped to untranslated or coding exons and 6 were intronic relative to known exons of protein-coding genes. Lower levels of expression of SIN3B, TRIP3, SYNJ2BP and NDE1 (p < 0.02), and of intronic transcripts derived from SND1 and ACTN4 loci (p < 0.05), were confirmed in clear cell RCC by Real-time RT-PCR. A subset of 25 transcripts was deregulated in additional 6 non-clear cell RCC samples, pointing to common transcriptional alterations in RCC irrespective of the histological subtype or differentiation state of the tumor. Our results indicate a novel set of tumor suppressor gene candidates, including noncoding intronic RNAs, which may play a significant role in malignant transformations of normal renal cells. Keywords: Disease state analysis
2008-03-25 | GSE7367 | GEO
Project description:We performed a microRNA (miRNA) microarray on 10 metastatic RCC tumors and compared differential miRNA expresison to 19 primary clear cell renal cell carcinomas (ccRCC). We found there were 65 significantly dysregulated miRNAs; 9 miRNAs were significantly upregulated and 56 miRNAs were significantly downregulated in metastatic RCC when compared to primary clear cell renal cell carcinoma. miRNA microarray was performed on 10 metastatic RCC tumors
2012-08-13 | E-GEOD-39440 | biostudies-arrayexpress
Project description:Renal cell carcinoma is the most common neoplasm of the adult kidney. A few subtypes of RCC include papillary RCC (pRCC), chromophobe RCC (chRCC) and the benign oncocytoma tumor. In some cases, distinguishing between the RCC subyptes is difficult. We performed a mircroRNA (miRNA) microarray to determine differential miRNA expression between pRCC, chRCC, and oncocytoma.
2012-08-14 | GSE39790 | 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