Renal cells Carcinoma (RCC) Genome variation profiling by genome tiling array
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE63003 | GEO | 2014/11/06
SECONDARY ACCESSION(S): PRJNA266409
REPOSITORIES: GEO
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Project description:Papillary renal cell carcinoma (pRCC) is the second most frequent renal cell carcinomas (RCC) after clear cell RCC. In contrast to clear cell RCC, there is no consensual protocol using targeted therapy for metastatic pRCC. Moreover, diagnosis of some pRCC, especially pRCC of type 2 (pRCC2) may be challenging. Our aim was to identify molecular biomarkers that could be helpful for the diagnosis and treatment of pRCC.
2014-11-06 | GSE63000 | GEO
Project description:Papillary renal cell carcinoma (pRCC) is the second most frequent renal cell carcinomas (RCC) after clear cell RCC. In contrast to clear cell RCC, there is no consensual protocol using targeted therapy for metastatic pRCC. Moreover, diagnosis of some pRCC, especially pRCC of type 2 (pRCC2) may be challenging. Our aim was to identify molecular biomarkers that could be helpful for the diagnosis and treatment of pRCC.
2014-11-06 | GSE63001 | GEO
Project description:Papillary renal cell carcinoma (pRCC) is the second most frequent renal cell carcinomas (RCC) after clear cell RCC. In contrast to clear cell RCC, there is no consensual protocol using targeted therapy for metastatic pRCC. Moreover, diagnosis of some pRCC, especially pRCC of type 2 (pRCC2) may be challenging. Our aim was to identify molecular biomarkers that could be helpful for the diagnosis and treatment of pRCC.
2014-11-06 | GSE63002 | GEO
Project description:Papillary renal cell carcinoma (pRCC) is the second most frequent renal cell carcinomas (RCC) after clear cell RCC. In contrast to clear cell RCC, there is no consensual protocol using targeted therapy for metastatic pRCC. Moreover, diagnosis of some pRCC, especially pRCC of type 2 (pRCC2) may be challenging. Our aim was to identify molecular biomarkers that could be helpful for the diagnosis and treatment of pRCC. 9 samples was analysed by arrayCGH method using Agilen recommendations. Sample was labeled in Cy5 and co-hybridized with control DNA cy3 labeled.
2014-11-06 | E-GEOD-63000 | biostudies-arrayexpress
Project description:Retinoblastoma is the most common intraocular cancer of infancy and childhood, with an incidence of one case per 15,000 - 20,000 live births. An early event in retinoblastoma genesis is a functional loss of both alleles of the RB1 gene. However, other genes are likely to be involved in the development of this cancer. In this study we sought to build a comprehensive molecular portrait of this cancer by performing transcriptomic, methylomic, genomic profiling of primary retinoblastoma samples. Most of the patients whose tumors were studied had received no treatment prior to surgical enucleation.
2021-07-06 | GSE58784 | GEO
Project description:BackgroundAnaplasma phagocytophilum (Ap) is an obligate intracellular bacterium and the agent of human granulocytic anaplasmosis, an emerging tick-borne disease. Ap alternately infects ticks and mammals and a variety of cell types within each. Understanding the biology behind such versatile cellular parasitism may be derived through the use of tiling microarrays to establish high resolution, genome-wide transcription profiles of the organism as it infects cell lines representative of its life cycle (tick; ISE6) and pathogenesis (human; HL-60 and HMEC-1).ResultsDetailed, host cell specific transcriptional behavior was revealed. There was extensive differential Ap gene transcription between the tick (ISE6) and the human (HL-60 and HMEC-1) cell lines, with far fewer differentially transcribed genes between the human cell lines, and all disproportionately represented by membrane or surface proteins. There were Ap genes exclusively transcribed in each cell line, apparent human- and tick-specific operons and paralogs, and anti-sense transcripts that suggest novel expression regulation processes. Seven virB2 paralogs (of the bacterial type IV secretion system) showed human or tick cell dependent transcription. Previously unrecognized genes and coding sequences were identified, as were the expressed p44/msp2 (major surface proteins) paralogs (of 114 total), through elevated signal produced to the unique hypervariable region of each - 2/114 in HL-60, 3/114 in HMEC-1, and none in ISE6.ConclusionUsing these methods, whole genome transcription profiles can likely be generated for Ap, as well as other obligate intracellular organisms, in any host cells and for all stages of the cell infection process. Visual representation of comprehensive transcription data alongside an annotated map of the genome renders complex transcription into discernable patterns.
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Project description:This SuperSeries is composed of the SubSeries listed below. Refer to individual Series
2015-06-19 | E-GEOD-66885 | biostudies-arrayexpress
Project description:A single expressing copy of the human protamine domain was randomly inserted into an intron of Cyp2c38. The transgenic locus was shown to recapitulate the level of expression observed in normal human testis while not perturbing endogenous protamine expression. The development of an interspecies tiling array was pursued to enable direct comparison of the orthologous protamine domains in a single experiment. Probe design was adapted to generate species-specific high resolution probe sets that would tolerate repetitive elements. Results from competitive hybridizations demonstrate that interspecies tiling arrays are a valuable tool for parallel analysis of highly similar DNA sequences. This approach provides a rapid and reliable means of interrogating samples prior to deep sequencing analysis. These arrays should readily compliment most DNA isolation and analysis techniques such as ChIP, nuclease sensitivity and nuclear matrix association assays.
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Project description:PurposeTo investigate associations between imaging features and mutational status of clear cell renal cell carcinoma (ccRCC).Materials and methodsThis multi-institutional, multi-reader study included 103 patients (77 men; median age 59 years, range 34-79) with ccRCC examined with CT in 81 patients, MRI in 19, and both CT and MRI in three; images were downloaded from The Cancer Imaging Archive, an NCI-funded project for genome-mapping and analyses. Imaging features [size (mm), margin (well-defined or ill-defined), composition (solid or cystic), necrosis (for solid tumors: 0%, 1%-33%, 34%-66% or >66%), growth pattern (endophytic, <50% exophytic, or ≥50% exophytic), and calcification (present, absent, or indeterminate)] were reviewed independently by three readers blinded to mutational data. The association of imaging features with mutational status (VHL, BAP1, PBRM1, SETD2, KDM5C, and MUC4) was assessed.ResultsMedian tumor size was 49 mm (range 14-162 mm), 73 (71%) tumors had well-defined margins, 98 (95%) tumors were solid, 95 (92%) showed presence of necrosis, 46 (45%) had ≥50% exophytic component, and 18 (19.8%) had calcification. VHL (n = 52) and PBRM1 (n = 24) were the most common mutations. BAP1 mutation was associated with ill-defined margin and presence of calcification (p = 0.02 and 0.002, respectively, Pearson's χ (2) test); MUC4 mutation was associated with an exophytic growth pattern (p = 0.002, Mann-Whitney U test).ConclusionsBAP1 mutation was associated with ill-defined tumor margins and presence of calcification; MUC4 mutation was associated with exophytic growth. Given the known prognostic implications of BAP1 and MUC4 mutations, these results support using radiogenomics to aid in prognostication and management.
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Project description:Renal cell carcinoma (RCC) is the second most lethal urinary cancer. RCC is frequently asymptomatic and it is already metastatic at diagnosis. There is an urgent necessity for RCC specific biomarkers selection for diagnostic and prognostic purposes. In present study, we applied liquid chromatography-mass spectrometry (LC-MS) based metabolomics to analyze urine samples of 100 RCC, 34 benign kidney tumors and 129 healthy controls. Differential metabolites were analyzed to investigate if urine metabolites could differentiate RCC from non-RCC. A panel consisting of 9 metabolites showed the best predictive ability for RCC from the health controls with an area under the curve (AUC) values of 0.905 for the training dataset and 0.885 for the validation dataset. Separation was observed between the RCC and benign samples with an AUC of 0.816. RCC clinical stages (T1 and T2 vs. T3 and T4) could be separated using a panel of urine metabolites with an AUC of 0.813. One metabolite, N-formylkynurenine, was discovered to have potential value for RCC diagnosis from non-RCC subjects with an AUC of 0.808. Pathway enrichment analysis indicated that tryptophan metabolism was an important pathway in RCC. Our data concluded that urine metabolomics could be used for RCC diagnosis and would provide candidates for further targeted metabolomics analysis of RCC.
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