NanoString Cancer
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ABSTRACT:
PROVIDER: EGAD00010002432 | EGA |
REPOSITORIES: EGA
Similar Datasets
Project description:RCC files of 17 Cartridges' Panel Standards
| EGAD00010002431 | EGA
Project description:Generation of a new library of targeted mass spectrometry assays for accurate protein quantification in malignant and normal kidney tissue. Aliquots of primary tumor tissue lysates from 86 patients with initially localized renal cell carcinoma (RCC), 75 patients with metastatic RCC treated with sunitinib or pazopanib in the first line and 17 adjacent normal tissues treated at Masaryk Memorial Cancer Institute (MMCI) in Brno, Czech Republic, or University Hospital Pilsen (UHP), Czech Republic, were used to generate the spectral library. Two previously published datasets (dataset A and B) and two newly generated RCC datasets (dataset C and D) were analyzed using the newly generated library showing increased number of quantified peptides and proteins, depending on the size of the library and LC-MS/MS instrumentation. This PRIDE project also includes quantitative analysis results for all four datasets and raw files for dataset C and D. Dataset A is characterized in DOI: 10.1038/nm.3807. It consists of 18 samples from 9 RCC patients involving one cancer and non-cancerous sample per patient. Dataset B is characterized in DOI: 10.3390/biomedicines9091145. It consists of 16 tumor samples and 16 adjacent normal tissues from 16 mRCC patients treated at Masaryk Memorial Cancer Institute (MMCI) in Brno, Czech Republic. Dataset C involves only tumor tissues from dataset B. Half of them responded to sunitinib treatment in the first line three months after treatment initiation and half did not. Dataset D involves 16 RCC patients treated at University Hospital Pilsen (UHP), Czech Republic. All were localized at the time of initial diagnosis, half of the tumors developed distant metastasis in five years after the diagnosis.
2022-01-03 | PXD028411 | Pride
Project description:Hi-C of 17 primary samples obtained from human acute leukemias, namely AML, T-ALL and mixed myeloid/lymphoid leukemias with CpG Island Methylator Phenotype (CIMP). As healthy controls, Hi-C of CD34+ HSPCs from 3 healthy donors were used. Due to patient confidentiality considerations, the raw data files for this dataset have been deposited to the EGA controlled-access archive under the accession numbers EGAS00001007094 (study); EGAD00001011051 (dataset).
2023-08-31 | E-MTAB-13122 | biostudies-arrayexpress
Project description:CTCF ChIP-seq of 39 primary samples derived from human acute leukemias, namely AML, T-ALL and mixed myeloid/lymphoid leukemias with CpG Island Methylator Phenotype (CIMP). Due to patient confidentiality considerations, the raw data files for this dataset have been deposited to the EGA controlled-access archive under the accession numbers EGAS00001007094 (study); EGAD00001011059 (dataset).
2023-08-31 | E-MTAB-13117 | biostudies-arrayexpress
Project description:In order to address the progression, metastasis, and clinical heterogeneity of renal cell cancer (RCC), transcriptional profiling with oligonucleotide microarrays (22,283 genes) was done on 49 RCC tumors, 20 non-RCC renal tumors, and 23 normal kidney samples. Samples were clustered based on gene expression profiles and specific gene sets for each renal tumor type were identified. Gene expression was correlated to disease progression and a metastasis gene signature was derived. Gene signatures were identified for each tumor type with 100% accuracy. Differentially expressed genes during early tumor formation and tumor progression to metastatic RCC were found. Subsets of these genes code for secreted proteins and membrane receptors and are both potential therapeutic or diagnostic targets. A gene pattern ("metastatic signature") derived from primary tumors was very accurate in classifying tumors with and without metastases at the time of surgery. A previously described "global" metastatic signature derived by another group from various non-RCC tumors was validated in RCC. Unlike previous studies, we describe highly accurate and externally validated gene signatures for RCC subtypes and other renal tumors. Interestingly, the gene expression of primary tumors provides us information about the metastatic status in the respective patients and has the potential, if prospectively validated, to enrich the armamentarium of diagnostic tests in RCC. We validated in RCC, for the first time, a previously described metastatic signature and further showed the feasibility of applying a gene signature across different microarray platforms. Transcriptional profiling allows a better appreciation of the molecular and clinical heterogeneity in RCC. We used the following tissue samples to obtain transcriptional profiling of kidney tumors using Affymetrix HGU-133A chips: 23 Normal, 32 clear cell RCC (cRCC), 11 papillary RCC (pRCC), 6 chromophobe RCC (chrRCC), 12 Oncocytoma (OC), and 8 transitional cell carcinoma (TCC). The supplementary file 'GSE15641_mas5_data.txt' contains MAS5 signal values for the Samples included in Series GSE15641. This dataset is part of the TransQST collection.
2009-04-28 | E-GEOD-15641 | biostudies-arrayexpress
Project description:Sunitinib is a TKI inhibitor used for managing metastatic renal cell carcinoma (RCC). However, chronic sunitinib treatment in RCC usually results in the development of drug resistance via alternating phosphorylation dynamics. On the other hand, 17-beta-estradiol, or estrogen, has been demonstrated to repress RCC growth partly through regulating cell signallings. To investigate how estrogen can repress sunibitinib-resistant RCC growth and its the possible mechanism of action related protein phosphorylation, a label-free quantitative phosphoproteomics study is performed.
2020-08-07 | PXD011020 | Pride
Project description:H3K27ac ChIP-seq of 79 primary samples derived from human acute leukemias, namely AML, T-ALL and mixed myeloid/lymphoid leukemias with CpG Island Methylator Phenotype (CIMP). In addition, 4 samples derived from CD34+ cord blood cells of healthy donors were included. Due to patient confidentiality considerations, the raw data files for this dataset have been deposited to the EGA controlled-access archive under the accession numbers EGAS00001007094 (study); EGAD00001011060 (dataset).
2023-08-31 | E-MTAB-13119 | biostudies-arrayexpress
Project description:Raw .RCC files for NanoString. nCounter PanCancer IO 360 Panel was used
| EGAD00010002612 | EGA
Project description:This study investigates the molecular signatures that drive Renal Cell Carcinoma (RCC) metastatic conversion using the metastatic (LM2) and non-metastatic (SN12C) RCC cell lines.
2010-08-17 | GSE23633 | GEO
Project description:In order to address the progression, metastasis, and clinical heterogeneity of renal cell cancer (RCC), transcriptional profiling with oligonucleotide microarrays (22,283 genes) was done on 49 RCC tumors, 20 non-RCC renal tumors, and 23 normal kidney samples. Samples were clustered based on gene expression profiles and specific gene sets for each renal tumor type were identified. Gene expression was correlated to disease progression and a metastasis gene signature was derived. Gene signatures were identified for each tumor type with 100% accuracy. Differentially expressed genes during early tumor formation and tumor progression to metastatic RCC were found. Subsets of these genes code for secreted proteins and membrane receptors and are both potential therapeutic or diagnostic targets. A gene pattern ("metastatic signature") derived from primary tumors was very accurate in classifying tumors with and without metastases at the time of surgery. A previously described "global" metastatic signature derived by another group from various non-RCC tumors was validated in RCC. Unlike previous studies, we describe highly accurate and externally validated gene signatures for RCC subtypes and other renal tumors. Interestingly, the gene expression of primary tumors provides us information about the metastatic status in the respective patients and has the potential, if prospectively validated, to enrich the armamentarium of diagnostic tests in RCC. We validated in RCC, for the first time, a previously described metastatic signature and further showed the feasibility of applying a gene signature across different microarray platforms. Transcriptional profiling allows a better appreciation of the molecular and clinical heterogeneity in RCC.
2009-04-28 | GSE15641 | GEO