Similar Datasets
Project description:Raw .RCC files for NanoString. nCounter PanCancer IO 360 Panel was used
| EGAD00010002612 | EGA
Project description:NanoString raw data for a noeadjuvant combination PD-L1 plus CTLA-4 blockade trial on patients with cisplatin-ineligible operable urothelial carcinoma. All samples were FFPE tumor samples. Raw probe count data (.RCC files) were generated from nCounter Digital Analyzer (4.0.0.3).
| EGAD00010001852 | EGA
Project description:Characterization of ~68 cell lines derived from human sarcoma and 5 normal counterpart cells, including drug sensitivity testing, gene expression profiling and microRNA expression profiling have been completed. Data and tools for searching these data will be made publicly available through the NCI Developmental Therapeutics Program. The raw data (RCC files) are provided through the GEO website. Sarcoma represents a variety of cancers at arise from cells of mesenchymal origin and have seen limited treatment advances in the last decade. Drug sensitivity data coupled with the transcription and microRNA profiles of a cohort of sarcoma cell lines may help define novel treatment paradigms. For each cell line, microRNA expression was measured on nCounter miRNA Expression Arrays (Nanostring Technologies), providing multiplexed, digital detection and counting of 800 human microRNA's. Please note that there are 2 replicates included in the study: A-204-rep1 and A-204-rep2, ES-4-rep1 and ES-4-rep2 resulting total 77 samples.
2015-08-01 | E-GEOD-69470 | biostudies-arrayexpress
Project description:Renal cell carcinoma (RCC) with sarcomatoid transformation features a biphasic tumour with both sarcomatoid and carcinomatous components. Clear cell RCC (ccRCC) is the most common RCC subtype, frequently exhibits sarcomatoid transformation. The pathogenesis of sarcomatoid ccRCC remains unclear. This study aimed to identify the genes and pathways involved in sarcomatoid ccRCC using gene expression profiling. We analysed three distinct regions including sarcomatoid component, clear cell component and matched normal kidney tissue from formalin-fixed, paraffin-embedded (FFPE) tissue samples of four patients. RNA was extracted and gene expression profiles were analysed using the NanoString nCounter® PanCancer Pathways Panel on the NanoString nCounter® Analysis System. Data analysis was performed using NanoString nSolver™ Analysis Software. Significant gene expression differences were defined with a threshold of fold change ˃ 4 and P-value ˂ 0.05. Compared to normal kidney tissue, 17 genes were significantly different in the clear cell component, with 5 were upregulated and 12 downregulated. The most significantly upregulated and downregulated genes were GDF6 and SFRP1 respectively. In the sarcomatoid component, 85 genes showed differences, with 38 upregulated and 47 downregulated. COL11A1 and LRP2 were the most significantly overexpressed and underexpressed genes respectively. When comparing sarcomatoid component with clear cell component, there were 53 significantly dysregulated genes, including epithelial-mesenchymal transition markers such as MMP9 and FN1. Pathway analysis indicated that PI3K was the most frequently deregulated pathway in the sarcomatoid component, suggesting its role in sarcomatoid transformation. This study provides insights into the gene expression patterns in ccRCC with sarcomatoid transformation.
2025-01-21 | GSE285848 | GEO
Project description:Adapter trimmed fastq files, non-redundant identical reads and the results of their alignment using the TIGER pipeline.
2021-11-01 | GSE160616 | GEO
Project description:To address the need to study frozen clinical specimens using next-generation RNA, DNA, chromatin immunoprecipitation (ChIP) sequencing and protein analyses, we developed a biobank work flow to prospectively collect biospecimens from patients with renal cell carcinoma (RCC). We describe our standard operating procedures and work flow to annotate pathologic results and clinical outcomes. We report quality control outcomes, nucleic acid yields of our RCC submissions (N=16) to The Cancer Genome Atlas (TCGA) project, as well as newer discovery platforms by describing mass spectrometry analysis of albumin oxidation in plasma and 6 ChIP sequencing libraries generated from nephrectomy specimens after histone H3 lysine 36 trimethylation (H3K36me3) immunoprecipitation. From June 1, 2010, through January 1, 2013, we enrolled 328 patients with RCC. Our mean (SD) TCGA RNA integrity numbers (RINs) were 8.1 (0.8) for papillary RCC, with a 12.5% overall rate of sample disqualification for RIN <7. Banked plasma had significantly less albumin oxidation (by mass spectrometry analysis) than plasma kept at 25°C (P<.001). For ChIP sequencing, the FastQC score for average read quality was at least 30 for 91-95% of paired-end reads. In parallel, we analyzed frozen tissue by RNA sequencing and after genome alignments, only 0.2-0.4% of total reads failed the default quality check steps of Bowtie2, which was comparable to the disqualification ratio (0.1%) of the 786-O RCC cell line, prepared under optimal RNA isolation conditions. The overall correlation coefficients for gene expression between the Mayo Clinic vs. TCGA tissues ranged from 0.75 to 0.82. These data support the generation of high-quality nucleic acids for genomic analyses from banked RCC. Importantly, the protocol does not interfere with routine clinical care. Collections over defined time points during disease treatment further enhance collaborative efforts to integrate genomic information with outcomes. Examination of H3K36me3 in ccRCC
2015-07-02 | E-GEOD-69196 | biostudies-arrayexpress
Project description:To address the need to study frozen clinical specimens using next-generation RNA, DNA, chromatin immunoprecipitation (ChIP) sequencing and protein analyses, we developed a biobank work flow to prospectively collect biospecimens from patients with renal cell carcinoma (RCC). We describe our standard operating procedures and work flow to annotate pathologic results and clinical outcomes. We report quality control outcomes, nucleic acid yields of our RCC submissions (N=16) to The Cancer Genome Atlas (TCGA) project, as well as newer discovery platforms by describing mass spectrometry analysis of albumin oxidation in plasma and 6 ChIP sequencing libraries generated from nephrectomy specimens after histone H3 lysine 36 trimethylation (H3K36me3) immunoprecipitation. From June 1, 2010, through January 1, 2013, we enrolled 328 patients with RCC. Our mean (SD) TCGA RNA integrity numbers (RINs) were 8.1 (0.8) for papillary RCC, with a 12.5% overall rate of sample disqualification for RIN <7. Banked plasma had significantly less albumin oxidation (by mass spectrometry analysis) than plasma kept at 25°C (P<.001). For ChIP sequencing, the FastQC score for average read quality was at least 30 for 91-95% of paired-end reads. In parallel, we analyzed frozen tissue by RNA sequencing and after genome alignments, only 0.2-0.4% of total reads failed the default quality check steps of Bowtie2, which was comparable to the disqualification ratio (0.1%) of the 786-O RCC cell line, prepared under optimal RNA isolation conditions. The overall correlation coefficients for gene expression between the Mayo Clinic vs. TCGA tissues ranged from 0.75 to 0.82. These data support the generation of high-quality nucleic acids for genomic analyses from banked RCC. Importantly, the protocol does not interfere with routine clinical care. Collections over defined time points during disease treatment further enhance collaborative efforts to integrate genomic information with outcomes. Examination of RNA expression in ccRCC
2015-07-02 | E-GEOD-69197 | biostudies-arrayexpress
Project description:We report the immediate effects of estrogen signaling on the transcriptome of breast cancer cells using Global Run-On and sequencing (GRO-seq). We found that estrogen signaling directly regulates a strikingly large fraction of the transcriptome in a rapid, robust, and unexpectedly transient manner. In addition to protein-coding genes, estrogen regulates the distribution and activity of all three RNA polymerases, and virtually every class of non-coding RNA that has been described to date. This data submission covers >95% of mapped reads comprising nearly all transcript classes described. Reads mapping to intergenic and enhancer transcripts were removed from this data submission and will be reported separately (manuscripts in preparation). Bed files are tab-separated text files in which columns represent: chrom, chromStart (5' End of the read), chromEnd (chromStart+1), name (unused always 'n'), score (the number of mismatches), and strand. Note that because of the inclusion of reads mapping to the rRNA chromosome, bed files cannot be uploaded to the UCSC genome browser directly. Instead, use the wiggle files (coming soon!) for this purpose.
2011-05-06 | GSE27463 | GEO
Project description:This Series reports data from a CTCF ChIP-Seq experiment performed in F1-hybrid mouse trophoblast stem cells (TSCs). The data are part of a larger study examining inactive X gene expression and chromatin states, reported as GEO Series GSE39406. Included for this dataset are FASTQ files, BED alignments and WIG files with coordinates relative to UCSC genome build mm9, and _snp files that report the location of all SNP-overlapping reads
2012-09-08 | GSE40667 | GEO