Ena-DATASET-MUG-25-01-2020-09:52:56:047-167 - samples
Ontology highlight
ABSTRACT:
PROVIDER: EGAD00001005813 | EGA |
REPOSITORIES: EGA
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Project description:We performed genome-wide 5hmC Methylated DNA Capture (5hMethylCap-seq) on one pooled RCC tissue sample (n=3) and the corresponding matched normal kidney tissue (NAT) (n=3), and we also performed 5hMethylCap-seq on one pooled urine sample obtained from RCC patients (n=52) along with another pooled urine sample obtained from control patients without malignancy (n=65). Global 5hmC levels were dramatically reduced in RCC tissues compared to matched normal adjacent kidney tissues, and although we detected low levels of 5hmC in urine samples, we also observed reduction of 5hmC in urine samples compared to tissue samples. Through assessing histone marked regions we found that 5hmC levels were enriched in H3K9me3 marked repressive genomic regions of normal adjacent kidney compared to RCC tissue tissues. Given the lower 5hmC signal in other genomic regions in cancer tissues, this upregulated 5hmC levels in H3K9me3 marked regions were also clearly identified comparing urine samples from RCC patients to control patients without RCC. We used Caki1 and Caki2 RCC cells to established stable cells with low H3K9me3 expression by knocking down the SUV39H1 gene. We found that low global H3K9me3 causes major upregulation of 5hmC at H3K9me3 marked regions and minor downregulation of 5hmC at genebody regions without change global 5mC and 5hmC levels.
2019-12-31 | GSE118922 | GEO
Project description:Vibrio ordalii FF-167 Genome sequencing and assembly
| PRJNA164835 | ENA
Project description:Paired end shallow whole genome sequencing (sWGS) data of cell-free DNA from plasma and urine from RCC patients (DAIMOND cohort)
| EGAD00001005814 | EGA
Project description:Cell-free tumour-derived DNA (ctDNA) allows non-invasive monitoring of various cancers but its utility in renal cell cancer (RCC) has not been well established. A combination of untargeted and targeted sequencing methods, applied to two independent cohorts of patients (n=90) with various renal tumour subtypes, were used to determine ctDNA content in plasma and urine. Our data revealed lower plasma ctDNA levels in RCC relative to other cancers, with untargeted detection of ~33% for both cohorts. A highly sensitive personalised approach, applied to plasma and urine from select patients (n=22), improved detection to ~50%, including in patients with early stage and even benign lesions. A machine-learning based model, applied to untargeted data, predicted this detection, potentially offering a means of triaging patient samples for personalised analysis. We observed that plasma, and for the first time, urine ctDNA may better represent tumour heterogeneity than a single tissue biopsy. Longitudinal sampling of >200 plasma samples revealed that ctDNA can track disease course. These data highlight low ctDNA levels in RCC but indicate potential clinical utility provided improvement in isolation and detection approaches.
| EGAS00001003530 | EGA
Project description:Highlighting the gene expression characteristics of a large panel of lung cancer cell lines. Microarrays were used to explore and detail the global gene expression profile of 56 lung cancer cell lines Gene expression was measured and recorded on HG-U133A chips in a large panel of lung cancer cell lines under ATCC recommended growth conditions
2013-05-24 | E-GEOD-47206 | biostudies-arrayexpress
Project description:We established a large panel of preclinical models of human RCC directly from patients, faithfully reproducing the biological features of the original tumor. RCC tissues were collected for 8 years from 336 patients undergoing surgery, xenografted subcutaneously in nude mice, and serially passaged into new mice up to 13 passages. Tissue samples from the primary tumor and tumors grown in mice through passages were analyzed at the histology, genetic and for of them at the molecular levels for biological tissue stability. We established a large panel of 30 RCC models and 5 them of the clear cell type (clear cell renal cell carcinoma, CCC) were characterized at the mRNA expression level.
2016-06-29 | GSE83820 | GEO
Project description:Fifty patient urine samples diagnosed as high-grade urothelial carcinoma (HGUC) or benign were evaluated for bladder cancer via urine cytology. RNA was isolated and analyzed by microarray to identify a panel of biomarkers differentially expressed in HGUC and benign.
2018-04-17 | GSE68020 | GEO
Project description:Highlighting the gene expression characteristics of a large panel of lung cancer cell lines. Microarrays were used to explore and detail the global gene expression profile of 56 lung cancer cell lines
2013-05-24 | GSE47206 | GEO
Project description:Eleven TC-RCC cases were selected on the basis of the criteria defined by the 2012 Vancouver Classification of Renal Neoplasia and WHO 2016 classification criteria, and re-reviewed by a panel of expert renal pathologists. Two of these cases, TC-RCC#9 and #10, had a mixed tubulocystic and papillary histology and were macro-dissected into separate PRCC and TC-RCC components. The molecular basis of TC-RCC, and miRNA expression profile, is unknown. We therefore used Affymetrix miRNA v.2. arrays to elucidate the expression of these samples
2017-07-19 | GSE101563 | GEO
Project description:Intronic and intergenic long noncoding RNAs (lncRNAs) are emerging gene expression regulators. The molecular pathogenesis of renal cell carcinoma (RCC) is still poorly understood, and in particular, limited studies are available for intronic lncRNAs expressed in RCC. Microarray experiments were performed with two different custom-designed arrays enriched with probes for lncRNAs mapping to intronic genomic regions. Samples from 18 primary clear cell RCC tumors and 11 nontumor adjacent matched tissues were analyzed with 4k-probes microarrays. Oligoarrays with 44k-probes were used to interrogate 17 RCC samples (14 clear cell, 2 papillary, 1 chromophobe subtypes) split into four pools. Meta-analyses were performed by taking the genomic coordinates of the RCC-expressed lncRNAs, and cross-referencing them with microarray expression data from three additional human tissues (normal liver, prostate tumor and kidney nontumor samples), and with large-scale public data for epigenetic regulatory marks and for evolutionarily conserved sequences. A signature of 29 intronic lncRNAs differentially expressed between RCC and nontumor samples was obtained (false discovery rate (FDR) <5%). An additional signature of 26 intronic lncRNAs significantly correlated with the RCC five-year patient survival outcome was identified (FDR <5%, p-value ≤0.01). We identified 4303 intronic antisense lncRNAs expressed in RCC, of which 25% were cis correlated (r >|0.6|) with the expression of the mRNA in the same locus across three human tissues. Gene Ontology (GO) analysis of those loci pointed to ‘regulation of biological processes’ as the main enriched category. A module map analysis of all expressed protein-coding genes in RCC that had a significant (r ≥|0.8|) trans correlation with the 20% most abundant lncRNAs identified 35 relevant (p <0.05) GO sets. In addition, we determined that 60% of these lncRNAs are evolutionarily conserved. At the genomic loci containing the intronic RCC-expressed lncRNAs, a strong association (p <0.001) was found between their transcription start sites and genomic marks such as CpG islands and histones methylation and acetylation. Intronic antisense lncRNAs are widely expressed in RCC tumors. Some of them are significantly altered in RCC in comparison with nontumor samples. The majority of these lncRNAs is evolutionarily conserved and possibly modulated by epigenetic modifications. Our data suggest that these RCC lncRNAs may contribute to the complex network of regulatory RNAs playing a role in renal cell malignant transformation.
2014-01-21 | GSE40911 | GEO