Transcription profiling of clear cell renal carcinomas and normal kidney cortical tissues
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ABSTRACT:
ORGANISM(S): Homo sapiens
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PROVIDER: S-DIXA-D-1186 | biostudies-other |
REPOSITORIES: biostudies-other
Similar Datasets
Project description:We performed a differential gene expression analysis comparing a collection of clear cell renal carcinoma tissue samples to normal cortical tissues. The Affymetrix GeneChip HG-U133 Plus 2.0 arrays were used.
2008-01-16 | E-TABM-282 | biostudies-arrayexpress
Project description:BackgroundUpper tract urothelial carcinomas (UT-UC) can invade the pelvicalyceal system making differential diagnosis of the various histologically distinct renal cell carcinoma (RCC) subtypes and UT-UC, difficult. Correct diagnosis is critical for determining appropriate surgery and post-surgical treatments. We aimed to identify microRNA (miRNA) signatures that can accurately distinguish the most prevalent RCC subtypes and UT-UC form the normal kidney.Methods and findingsmiRNA profiling was performed on FFPE tissue sections from RCC and UT-UC and normal kidney and 434 miRNAs were significantly deregulated in cancerous vs. the normal tissue. Hierarchical clustering distinguished UT-UCs from RCCs and classified the various RCC subtypes among them. qRT-PCR validated the deregulated expression profile for the majority of the miRNAs and ROC analysis revealed their capability to discriminate between tumour and normal kidney. An independent cohort of freshly frozen RCC and UT-UC samples was used to validate the deregulated miRNAs with the best discriminatory ability (AUC>0.8, p<0.001). Many of them were located within cytogenetic regions that were previously reported to be significantly aberrated. miRNA targets were predicted using the miRWalk algorithm and ingenuity pathway analysis identified the canonical pathways and curated networks of the deregulated miRNAs. Using the miRWalk algorithm, we further identified the top anti-correlated mRNA/miRNA pairs, between the deregulated miRNAs from our study and the top co-deregulated mRNAs among 5 independent ccRCC GEO datasets. The AB8/13 undifferentiated podocyte cells were used for functional assays using luciferase reporter constructs and the developmental transcription factor TFCP2L1 was proved to be a true target of miR-489, which was the second most upregulated miRNA in ccRCC.ConclusionsWe identified novel miRNAs specific for each RCC subtype and UT-UC, we investigated their putative targets, the networks and pathways in which they participate and we functionally verified the true targets of the top deregulated miRNAs.
| S-EPMC3951427 | biostudies-literature
Project description:Monocyte Chemoattractant protein-induced protein 1 (MCPIP1), also known as Regnase-1, is encoded by the ZC3H12a gene, and it mediates inflammatory processes by regulating the stability of transcripts coding for proinflammatory cytokines and controlling activity of transcription factors, such as NF-?B and AP1. We found that MCPIP1 transcript and protein levels are strongly downregulated in clear cell renal cell carcinoma (ccRCC) samples, which were derived from patients surgically treated for renal cancer compared to surrounded normal tissues. Using Caki-1 cells as a model, we analyzed the role of MCPIP1 in cancer development. We showed that MCPIP1 expression depends on the proteasome activity; however, hypoxia and hypoxia inducible factor 2 alfa (HIF2?) are key factors lowering MCPIP1 expression. Furthermore, we found that MCPIP1 negatively regulates HIF1? and HIF2? levels and in the case of the last one, the mechanism is based on the regulation of the half time of transcript coding for HIF2?. Enhanced expression of MCPIP1 in Caki-1 cells results in a downregulation of transcripts encoding VEGFA, GLUT1, and IL-6. Furthermore, MCPIP1 decreases the activity of mTOR and protein kinase B (Akt) in normoxic conditions. Taken together, MCPIP1 contributes to the ccRCC development.
| S-EPMC5511613 | biostudies-literature
Project description:Clear cell renal cell carcinoma (ccRCC) is the most common malignancy of the adult kidney and displays heterogeneity in clinical outcomes. Through comprehensive gene expression profiling, we have identified previously a set of transcripts that predict survival following nephrectomy independent of tumor stage, grade, and performance status. These transcripts, designated as the SPC (supervised principal components) gene set, show no apparent biological or genetic features that provide insight into renal carcinogenesis or tumor progression. We explored the relationship of this gene list to a set of genes expressed in different anatomical segments of the normal kidney including the cortex (cortex gene set) and the glomerulus (glomerulus gene set), and a gene set expressed after serum stimulation of quiescent fibroblasts (the core serum response or CSR gene set). Interestingly, the normal cortex, glomerulus (part of the normal renal cortex), and CSR gene sets captured more than 1/5 of the genes in the highly prognostic SPC gene set. Based on gene expression patterns alone, the SPC gene set could be used to sort samples from normal adult kidneys by the anatomical regions from which they were dissected. Tumors whose gene expression profiles most resembled the normal renal cortex or glomerulus showed better survival than those that did not, and those with expression features more similar to CSR showed poorer survival. While the cortex, glomerulus, and CSR signatures predicted survival independent of traditional clinical parameters, they were not independent of the SPC gene list. Our findings suggest that critical biological features of lethal ccRCC include loss of normal cortical differentiation and activation of programs associated with wound healing.
| S-EPMC2698218 | biostudies-literature
Project description:Whole genome expression of 39 clear-cell renal cell carcinomas tumors
2011-05-28 | GSE29609 | GEO
Project description:We analyzed 84 tumor/normal pairs (177 total) using standard shotgun proteomic techniques in order to characterize the molecular landscape of clear cell renal cell carcinoma (ccRCC) and interrogated changes in protein abundance and biological pathways with ccRCC grade. These tissues were distributed across stage 1 (n = 34), 2 (n = 40), 3 (n = 42), and 4 (n = 52), with 9 pairs also including samples from metastasized tumor. These data can be (and were) combined with a previously published transcriptomic data set from the same sample cohort (NCBI GEO: GSE53757).
2016-04-18 | PXD003271 | Pride
Project description:Twenty to 40% localised RCC patients may experience recurrence after curative surgery. Limited miRNA predictors have been identified for ccRCC recurrence.Through a multi-phase study design, we analysed miRNAs in tissues obtained from 203 ccRCC patients. Paired t-test was used for tumour-normal comparisons and Cox regression model was performed to compute hazard ratios (HRs) and corresponding 95% CIs.A 17-miRNA signature was identified that can concordantly classify >95% of tumour/adjacent normal samples. Significant enrichment was found as 6 out of 17 miRNAs were associated with obesity (binomial probability=0.001). Decreased levels of miR-204-5p and miR-139-5p were each associated with an approximately three-fold increased risk of recurrence (P<0.01). Risk score was generated based on expressions of miR-204-5p and miR-139-5p, and the trend test was significant in both discovery and validation sets (Pfor trend<0.05). Striking MST reduction was observed for patients with a high-risk score (high vs low: discovery, 9.4 vs >97.7 months; validation, 20.8 vs >70.3 months). Expressions of miR-204-5p were also associated with body mass index (?=5.64, P<0.001). Significant inverse correlations were observed and validated between miR-204-5p and 13 obesity-related genes (r<0, P<0.01).We identified 17 miRNAs dysregulated in ccRCC tissues and showed that low expressions of miR-204-5p and miR-139-5p were associated with the higher risk of recurrence. The link between miR-204-5p and ccRCC recurrence may be partially mediated by regulating the expression of targeted obesity-related genes.
| S-EPMC5220154 | biostudies-literature
Project description:Whole genome expression of 39 clear-cell renal cell carcinomas tumors 39 frozen tissue from recent nephrectomies for clear-cell renal cell carcinoma; association with clinical characteristics
2011-05-28 | E-GEOD-29609 | biostudies-arrayexpress
Project description:WNT7A (wingless-type MMTV integration site family, member 7A) is a known tumor suppressor gene of non-small cell lung carcinomas (NSCLC) and is frequently inactivated due to CpG-island hypermethylation in human cancers. The members of WNT family are involved in cell signaling and play crucial roles in cancer development. In the present work hypermethylation of the WNT7A gene was detected in 66% (29/44) of analyzed clear cell renal cell carcinomas (RCCs) using methyl-specific PCR (MSP). Moreover, bisulfite sequencing confirmed intensive hypermethylation of the 5'-CpG island of the WNT7A gene. Methylation analysis revealed positive correlations between tumor stage, Fuhrman nuclear grade and WNT7A hypermethylation. Additionally, restoration of WNT7A gene expression in the A498 cell line by 5-aza-2'-deoxycytidine treatment confirmed a direct contribution of hypermethylation in silencing of the WNT7A gene. High frequency of loss of heterozygosity (LOH) was demonstrated on chromosome 3p25 in regions surrounding the WNT7A gene. The frequent down-regulation of WNT7A gene expression was detected in 88% (15/17) of clear cell RCCs. We have also shown that the WNT7A gene possesses tumor suppression function by colony-formation and cell proliferation assays in RCC cell lines. In summary, the WNT7A gene is inactivated by genetic/epigenetic alterations in clear cell RCC and demonstrates tumor suppressor properties.
| S-EPMC3466251 | biostudies-literature
Project description:Precise diagnosis and subtyping of kidney tumors are imperative to optimize and personalize treatment decision for patients. Patients with the most common benign renal tumor, renal oncocytomas, may be overtreated with surgical resection because of limited preoperative diagnostic methods that can accurately identify the benign condition with certainty. In this study, desorption electrospray ionization (DESI)-mass spectrometry (MS) imaging was applied to study the metabolic and lipid profiles of various types of renal tissues, including normal kidney, renal oncocytoma, and renal cell carcinomas (RCC). A total of 73,992 mass spectra from 71 patient samples were obtained and used to build predictive models using the least absolute shrinkage and selection operator (Lasso). Overall accuracies of 99.47% per pixel and 100% per patient for prediction of the three tissue types were achieved. In particular, renal oncocytoma and chromophobe RCC, which present the most significant morphologic overlap and are sometimes indistinguishable using histology alone, were also investigated and the predictive models built yielded 100% accuracy in discriminating these tumor types. Discrimination of three subtypes of RCC was also achieved on the basis of DESI-MS imaging data. Importantly, several small metabolites and lipids species were identified as characteristic of individual tissue types and chemically characterized using tandem MS and high mass accuracy measurements. Collectively, our study shows that the metabolic data acquired by DESI-MS imaging in conjunction with statistical modeling allows discrimination of renal tumors and thus has the potential to be used in the clinical setting to improve treatment of patients with kidney tumor. SIGNIFICANCE: Metabolic data acquired by mass spectrometry imaging in conjunction with statistical modeling allows discrimination of renal tumors and has the potential to be used in the clinic to improve treatment of patients.
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