Transcription Factor Binding Sites by ChIP-seq from ENCODE/LICR
Ontology highlight
ABSTRACT:
ORGANISM(S): Mus musculus
PROVIDER: GSE36027 | GEO | 2012/04/19
REPOSITORIES: GEO
Similar Datasets
Project description:This data was generated by ENCODE. If you have questions about the data, contact the submitting laboratory directly (Yin Shen mailto:y7shen@ucsd.edu). If you have questions about the Genome Browser track associated with this data, contact ENCODE (mailto:genome@soe.ucsc.edu). This track shows a comprehensive survey of cis-regulatory elements in the mouse genome by using ChIP-seq (Robertson et al., 2007) to identify transcription factor binding sites and chromatin modification profiles in many mouse (C57Bl/6) tissues and primary cells, including bone marrow, cerebellum, cortex, heart, kidney, liver, lung, spleen, mouse embryonic fibroblast cells (MEFs) and embryonic stem (ES) cells. In specific, the Ren lab examined RNA polymerase II (PolII), co-activator protein p300, the insulator protein CTCF, and two chromatin modification marks H3K4me3 and H3K4me1 due to their demonstrated utilities in identifying promoters, enhancers and insulator elements (Barski et al., 2007; Blow et al., 2010; Heintzman et al., 2009; Kim et al., 2007; Kim et al., 2005a; Visel et al., 2009). Enrichment of H3K4me3 or PolII signals is a strong indicator of active promoter, while the presence of p300 or H3K4me1 outside of promoter regions has been used as a mark for enhancers. CTCF binding sites are considered as a mark for potential insulator elements. For each transcription factor or chromatin mark in each tissue, ChIP-seq was carried out with at least two biological replicates. Each experiment produced 20-30 million monoclonal, uniquely mapped tags. For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf
2011-07-30 | GSE31039 | GEO
Project description:This data was generated by ENCODE. If you have questions about the data, contact the submitting laboratory directly (Yin Shen mailto:y7shen@ucsd.edu). If you have questions about the Genome Browser track associated with this data, contact ENCODE (mailto:genome@soe.ucsc.edu). This track shows a comprehensive survey of cis-regulatory elements in the mouse genome by using ChIP-seq (Robertson et al., 2007) to identify transcription factor binding sites and chromatin modification profiles in many mouse (C57Bl/6) tissues and primary cells, including bone marrow, cerebellum, cortex, heart, kidney, liver, lung, spleen, mouse embryonic fibroblast cells (MEFs) and embryonic stem (ES) cells. In specific, the Ren lab examined RNA polymerase II (PolII), co-activator protein p300, the insulator protein CTCF, and two chromatin modification marks H3K4me3 and H3K4me1 due to their demonstrated utilities in identifying promoters, enhancers and insulator elements (Barski et al., 2007; Blow et al., 2010; Heintzman et al., 2009; Kim et al., 2007; Kim et al., 2005a; Visel et al., 2009). Enrichment of H3K4me3 or PolII signals is a strong indicator of active promoter, while the presence of p300 or H3K4me1 outside of promoter regions has been used as a mark for enhancers. CTCF binding sites are considered as a mark for potential insulator elements. For each transcription factor or chromatin mark in each tissue, ChIP-seq was carried out with at least two biological replicates. Each experiment produced 20-30 million monoclonal, uniquely mapped tags. For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf Cells were grown according to the approved ENCODE cell culture protocols (http://hgwdev/ENCODE/protocols/cell/mouse). Enrichment and Library Preparation: Chromatin immunoprecipitation was performed according to Ren Lab ChIP Protocol (http://bioinformatics-renlab.ucsd.edu/RenLabChipProtocolV1.pdf). Library construction was performed according to Ren Lab Library Protocol (http://bioinformatics-renlab.ucsd.edu/RenLabLibraryProtocolV1.pdf). Sequencing and Analysis: Samples were sequenced on Illumina Genome Analyzer II Genome Analyzer IIx, and HiSeq 2000 platforms for 36 cycles. Image analysis, base calling and alignment to the mouse genome version mm9 were performed using Illumina's RTA and Genome Analyzer Pipeline software. Alignment to the mouse genome was performed using ELAND or Bowtie (Langmead et al., 2009) with a seed length of 25 and allowing up to two mismatches. Only the sequences that mapped to one location were used for further analysis. Of those sequences, clonal reads, defined as having the same start position on the same strand, were discarded. BED and wig files were created using custom perl scripts.
2011-07-30 | E-GEOD-31039 | biostudies-arrayexpress
Project description:This data was generated by ENCODE. If you have questions about the data, contact the submitting laboratory directly (Yin Shen mailto:y7shen@ucsd.edu). If you have questions about the Genome Browser track associated with this data, contact ENCODE (mailto:genome@soe.ucsc.edu). This track shows a comprehensive survey of cis-regulatory elements in the mouse genome by using ChIP-seq (Robertson et al., 2007) to identify transcription factor binding sites and chromatin modification profiles in many mouse (C57Bl/6) tissues and primary cells, including bone marrow, cerebellum, cortex, heart, kidney, liver, lung, spleen, mouse embryonic fibroblast cells (MEFs) and embryonic stem (ES) cells. In specific, the Ren lab examined RNA polymerase II (PolII), co-activator protein p300, the insulator protein CTCF, and two chromatin modification marks, H3K4me3 and H3K4me1, due to their demonstrated utilities in identifying promoters, enhancers and insulator elements (Barski et al., 2007; Blow et al., 2010; Heintzman et al., 2009; Kim et al., 2007; Kim et al., 2005a; Visel et al., 2009). Enrichment of H3K4me3 or PolII signals is a strong indicator of an active promoter, while the presence of p300 or H3K4me1 outside of promoter regions has been used as a mark for enhancers. CTCF binding sites are considered as a mark for potential insulator elements. For each transcription factor or chromatin mark in each tissue, ChIP-seq was carried out with at least two biological replicates. Each experiment produced 20-30 million monoclonal, uniquely mapped tags. For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf Cells were grown according to the approved ENCODE cell culture protocols (http://genome.ucsc.edu/ENCODE/protocols/cell/mouse). Enrichment and Library Preparation Chromatin immunoprecipitation was performed according to Ren Lab ChIP Protocol (http://bioinformatics-renlab.ucsd.edu/RenLabChipProtocolV1.pdf). Library construction was performed according to Ren Lab Library Protocol (http://bioinformatics-renlab.ucsd.edu/RenLabLibraryProtocolV1.pdf). Sequencing and Analysis Samples were sequenced on Illumina Genome Analyzer II, Genome Analyzer IIx and HiSeq 2000 platforms for 36 cycles. Image analysis, base calling and alignment to the mouse genome version mm9 were performed using Illumina's RTA and Genome Analyzer Pipeline software. Alignment to the mouse genome was performed using ELAND or Bowtie (Langmead et al., 2009) with a seed length of 25 and allowing up to two mismatches. Only the sequences that mapped to one location were used for further analysis. Of those sequences, clonal reads, defined as having the same start position on the same strand, were discarded. BED and wig files were created using custom perl scripts.
2012-04-19 | E-GEOD-36027 | biostudies-arrayexpress
Project description:This data was generated by ENCODE. If you have questions about the data, contact the submitting laboratory directly (Barbara Wold mailto:woldb@caltech.edu, Georgi K. Marinov mailto:georgi@caltech.edu, Diane Trout mailto:diane@caltech.edu). If you have questions about the Genome Browser track associated with this data, contact ENCODE (mailto:genome@soe.ucsc.edu). Our knowledge of the function of genomic DNA sequences comes from three basic approaches. Genetics uses changes in behavior or structure of a cell or organism in response to changes in DNA sequence to infer function of the altered sequence. Biochemical approaches monitor states of histone modification, binding of specific transcription factors, accessibility to DNases and other epigenetic features along genomic DNA. In general, these are associated with gene activity, but the precise relationships remain to be established. The third approach is evolutionary, using comparisons among homologous DNA sequences to find segments that are evolving more slowly or more rapidly than expected given the local rate of neutral change. These are inferred to be under negative or positive selection, respectively, and we interpret these as DNA sequences needed for a preserved (negative selection) or adaptive (positive selection) function. The ENCODE project aims to discover all the DNA sequences associated with various epigenetic features, with the reasonable expectation that these will also be functional (best tested by genetic methods). However, it is not clear how to relate these results with those from evolutionary analyses. The mouse ENCODE project aims to make this connection explicitly and with a moderate breadth. Assays identical to those being used in the ENCODE project are performed in cell types in mouse that are similar or homologous to those studied in the human project. Thus, we will be able to discover which epigenetic features are conserved between mouse and human, and we can examine the extent to which these overlap with the DNA sequences under negative selection. The contribution of DNA that with a function preserved in mammals versus that with a function in only one species will be discovered. The results will have a significant impact on our understanding of the evolution of gene regulation. Maps of Occupancy by Transcription Factors Genome-wide occupancy maps of transcription factors (TFs) are generated by ChIP-seq. A ChIP-Seq experiment combines a chromatin immunoprecipitation (ChIP) experiment that enriches genomic DNA for the segments bound by specific proteins (the antigens recognized by the antibody) with high-throughput short read sequencing of the enriched DNA fragments (Wold & Myers, 2008). Proteins are crosslinked to DNA (usually with formaldehyde), chromatin is sheared and immunoprecipitated with the antibody of interest. The immunoprecipitated material is turned into a sequencing library and sequenced. The sequencing reads are then aligned to the genome. A control sample consisting of sonicated chromatin that has not been immunoprecipitated or immunoprecipitated with a non-specific immunoglobulin is also sequenced. The ChIP and the control datasets are analyzed with a variety of software packages to identify regions occupied by the target protein. The sequencing data, alignments and analysis files for these experiments are available for download. In specific, the Ren lab examined RNA polymerase II (PolII), co-activator protein p300, the insulator protein CTCF, and two chromatin modification marks, H3K4me3 and H3K4me1, due to their demonstrated utilities in identifying promoters, enhancers and insulator elements (Barski et al., 2007; Blow et al., 2010; Heintzman et al., 2009; Kim et al., 2007; Kim et al., 2005a; Visel et al., 2009). Enrichment of H3K4me3 or PolII signals is a strong indicator of an active promoter, while the presence of p300 or H3K4me1 outside of promoter regions has been used as a mark for enhancers. CTCF binding sites are considered as a mark for potential insulator elements. For each transcription factor or chromatin mark in each tissue, ChIP-seq was carried out with at least two biological replicates. Each experiment produced 20-30 million monoclonal, uniquely mapped tags. Our knowledge of the function of genomic DNA sequences comes from three basic approaches. Genetics uses changes in behavior or structure of a cell or organism in response to changes in DNA sequence to infer function of the altered sequence. Biochemical approaches monitor states of histone modification, binding of specific transcription factors, accessibility to DNases and other epigenetic features along genomic DNA. In general, these are associated with gene activity, but the precise relationships remain to be established. The third approach is evolutionary, using comparisons among homologous DNA sequences to find segments that are evolving more slowly or more rapidly than expected given the local rate of neutral change. These are inferred to be under negative or positive selection, respectively, and we interpret these as DNA sequences needed for a preserved (negative selection) or adaptive (positive selection) function. The ENCODE project aims to discover all the DNA sequences associated with various epigenetic features, with the reasonable expectation that these will also be functional (best tested by genetic methods). However, it is not clear how to relate these results with those from evolutionary analyses. The mouse ENCODE project aims to make this connection explicitly and with a moderate breadth. Assays identical to those being used in the ENCODE project are performed in cell types in mouse that are similar or homologous to those studied in the human project. Thus we will be able to discover which epigenetic features are conserved between mouse and human, and we can examine the extent to which these overlap with the DNA sequences under negative selection. The contribution of DNA that with a function preserved in mammals versus that with a function in only one species will be discovered. The results will have a significant impact on our understanding of the evolution of gene regulation. Maps of histone modifications Levels of three histone modifications are being determined. H3K4me1 (monomethylation of lysine 4 of histone H3) is a mark for active chromatin and in the absence of H3K4me3, it is one indicator of an enhancer. H3K4me3 (trimethylation of lysine 4 of histone H3) is highly enriched at active promoters. One repressive (Polycomb) mark, H3K27me3, is associated with some silenced genes. Maps of genomic DNA in chromatin with these histone modifications are generated by ChIP-seq. This consists of two basic steps: chromatin immunoprecipitation (ChIP) is used to highly enrich genomic DNA for the segments bound by specific proteins (the antigens recognized by the antibodies) followed by massively parallel short read sequencing to tag the enriched DNA segments. Sequencing is done on the Illumina GAIIx and HiSeq. The sequence tags are mapped back to the mouse genome (Langmead et al. 2009), and a graph of the enrichment for histone modifications are displayed as the "Signal" track (essentially the counts of mapped reads per interval) and the deduced probable binding sites from the MACS program (Zhang et al. 2008) are shown in the "Peaks" track. Each experiment is associated with an input signal, which represents the control condition where immunoprecipitation with non-specific immunoglobulin was performed in the same cell type. The sequence reads, quality scores, and alignment coordinates from these experiments are available for download. Our knowledge of the function of genomic DNA sequences comes from three basic approaches. Genetics uses changes in behavior or structure of a cell or organism in response to changes in DNA sequence to infer function of the altered sequence. Biochemical approaches monitor states of histone modification, binding of specific transcription factors, accessibility to DNases and other epigenetic features along genomic DNA. In general, these are associated with gene activity, but the precise relationships remain to be established. The third approach is evolutionary, using comparisons among homologous DNA sequences to find segments that are evolving more slowly or more rapidly than expected given the local rate of neutral change. These are inferred to be under negative or positive selection, respectively, and we interpret these as DNA sequences needed for a preserved (negative selection) or adaptive (positive selection) function. The ENCODE project aims to discover all the DNA sequences associated with various epigenetic features, with the reasonable expectation that these will also be functional (best tested by genetic methods). However, it is not clear how to relate these results with those from evolutionary analyses. The mouse ENCODE project aims to make this connection explicitly and with a moderate breadth. Assays identical to those being used in the ENCODE project are performed in cell types in mouse that are similar or homologous to those studied in the human project. Thus we will be able to discover which epigenetic features are conserved between mouse and human, and we can examine the extent to which these overlap with the DNA sequences under negative selection. The contribution of DNA that with a function preserved in mammals versus that with a function in only one species will be discovered. The results will have a significant impact on our understanding of the evolution of gene regulation. Maps of Occupancy by Transcription Factors Maps of occupancy of genomic DNA by transcription factors (TFs) are determined by ChIP-seq. This consists of two basic steps: chromatin immunoprecipitation (ChIP) is used to highly enrich genomic DNA for the segments bound by specific proteins (the antigens recognized by the antibodies) followed by massively parallel short read sequencing to tag the enriched DNA segments. Sequencing is done on the Illumina GAIIx and HiSeq. The sequence tags are mapped back to the mouse genome (Langmead et al. 2009), and a graph of the enrichment for TF binding are displayed as the "Signal" track (essentially the counts of mapped reads per interval) and the deduced probable binding sites from the MACS program (Zhang et al. 2008) are shown in the "Peaks" track. Each experiment is associated with an input signal, which represents the control condition where immunoprecipitation with non-specific immunoglobulin was performed in the same cell type. The sequence reads, quality scores, and alignment coordinates from these experiments are available for download. The sequence reads, quality scores, and alignment coordinates from these experiments are available for download. For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf
2012-04-19 | GSE36023 | GEO
Project description:CD47 is a transmembrane glycoprotein that is ubiquitously expressed in different organs and tissues (Barclay and Van den Berg 2014; Liu, et al. 2017). In the human immune system, CD47 interacts with some integrins, two counter-receptor signal regulator protein (SIRP) family members, and the secreted thrombospondin-1 (TSP1) (Barclay and Van den Berg 2014; Gao, et al. 2016; Kaur, et al. 2013; Oldenborg, et al. 2000). CD47 has two established roles in the immune system. The CD47-SIRPα interaction was identified as a critical innate immune checkpoint, which delivers an antiphagocytic signal to macrophages and inhibits neutrophil cytotoxicity (Martínez- Sanz, et al. 2021). Its interaction with inhibitory SIRPα is a physiological anti-phagocytic “don’t eat me” signal on circulating red blood cells that is co-opted by cancer cells (Matlung, et al. 2017). Many malignant cells overexpress CD47 (Betancur, et al. 2017; Chao, et al. 2011; Jaiswal, et al. 2009; Majeti, et al. 2009; Oronsky, et al. 2020; Petrova, et al. 2017). CD47/SIRPα-targeted therapeutics have been developed to overcome this immune checkpoint for cancer treatment (Kaur, et al. 2020; Matlung, et al. 2017). Secondly, engagement of CD47 on T cells by TSP1 regulates their differentiation and survival (Grimbert, et al. 2006; Lamy, et al. 2007) and inhibits T cell receptor signaling and antigen presentation by dendritic cells (DCs) (Kaur, et al. 2014; Li, et al. 2002; Liu, et al. 2015; Miller, et al. 2013; Soto-Pantoja, et al. 2014; Weng, et al. 2014). TSP1/CD47 signaling has similar inhibitory functions to limit NK cell activation (Kim, et al. 2008; Nath, et al. 2018; Nath, et al. 2019; Schwartz, et al. 2019) and IL1β production by macrophages (Stein, et al. 2016). CD47 is therefore a checkpoint that regulates both innate and adaptive immunity. The recent understanding of CD47 antagonism associated with increased antigen presentation by DCs (Liu, et al. 2016) and natural killer cell cytotoxicity (Nath, et al. 2019) contributes to the heightened interest in CD47 as a therapeutic target (Kaur, et al. 2020).
2022-09-21 | GSE198820 | GEO
Project description:This data was generated by ENCODE. If you have questions about the data, contact the submitting laboratory directly (Georgi K. Marinov mailto:georgi@caltech.edu (data coordination/informatics/experimental), Diane Trout mailto:diane@caltech.edu (informatics) Brian Williams mailto:bawilli_91125@yahoo.com (experimental)). If you have questions about the Genome Browser track associated with this data, contact ENCODE (mailto:genome@soe.ucsc.edu). RNA-seq is a method for mapping and quantifying the transcriptome of any organism that has a genomic DNA sequence assembly (Mortazavi et al., 2008). RNA-seq is performed by reverse-transcribing an RNA sample into cDNA, followed by high-throughput DNA sequencing, which was done here on the Illumina HiSeq sequencer. The transcriptome measurements shown on these tracks were performed on polyA selected RNA (http://hgwdev.cse.ucsc.edu/cgi-bin/hgEncodeVocab?term=longPolyA&type=rnaExtract) from total cellular RNA (http://hgwdev.cse.ucsc.edu/cgi-bin/hgEncodeVocab?term=cell&type=localization). PolyA-selected RNA was fragmented by magnesium-catalyzed hydrolysis and then converted into cDNA by random priming and amplified. Paired-end 2x100 bp reads were obtained from each end of a cDNA fragment. Reads were aligned to the mm9 human reference genome using TopHat (Trapnell et al., 2009), a program specifically designed to align RNA-seq reads and discover splice junctions de novo. All sequence and alignments files are available at http://hgwdev.cse.ucsc.edu/cgi-bin/hgFileUi?db=mm9&g=wgEncodeCaltechRnaSeq.
2012-05-10 | GSE37909 | GEO
Project description:This data was generated by ENCODE. If you have questions about the data, contact the submitting laboratory directly (Yin Shen mailto:y7shen@ucsd.edu). If you have questions about the Genome Browser track associated with this data, contact ENCODE (mailto:genome@soe.ucsc.edu). Using RNA-Seq (Mortazavi et al., 2008), high-resolution genome-wide maps of the mouse transcriptome across multiple mouse (C57Bl/6) tissues and primary cells were generated.
2012-05-10 | GSE36026 | GEO
Project description:This data was generated by ENCODE. If you have questions about the data, contact the submitting laboratory directly (Philip Cayting mailto:pcayting@stanford.edu). If you have questions about the Genome Browser track associated with this data, contact ENCODE (mailto:genome@soe.ucsc.edu). This track shows probable binding sites of the specified transcription factors (TFs) in the given cell types as determined by chromatin immunoprecipitation followed by high throughput sequencing (ChIP-Seq). Included for each cell type is the input signal, which represents the control condition where no antibody targeting was performed. For each experiment (cell type vs. antibody) this track shows a graph of enrichment for TF binding (Signal), along with sites that have the greatest evidence of transcription factor binding (Peaks). For data usage terms and conditions, please refer to http://www.genome.gov/27528022 and http://www.genome.gov/Pages/Research/ENCODE/ENCODEDataReleasePolicyFinal2008.pdf Cells were grown according to the approved ENCODE cell culture protocols. Further preparations were similar to those previously published (Euskirchen et al., 2007) with the exceptions that the cells were unstimulated and sodium orthovanadate was omitted from the buffers. For details on the chromatin immunoprecipitation protocol used, see Euskirchen et al. (2007) and Rozowsky et al. (2009). DNA recovered from the precipitated chromatin was sequenced on the Illumina (Solexa) sequencing platform and mapped to the genome using the Eland alignment program. ChIP-seq data was scored based on sequence reads (length ~30 bps) that align uniquely to the human genome. From the mapped tags a signal map of ChIP DNA fragments (average fragment length ~ 200 bp) was constructed where the signal height is the number of overlapping fragments at each nucleotide position in the genome. For each 1 Mb segment of each chromosome a peak height threshold was determined by requiring a false discovery rate <= 0.05 when comparing the number of peaks above threshold as compared the number obtained from multiple simulations of a random null background with the same number of mapped reads (also accounting for the fraction of mapable bases for sequence tags in that 1 Mb segment). The number of mapped tags in a putative binding region is compared to the normalized (normalized by correlating tag counts in genomic 10 kb windows) number of mapped tags in the same region from an input DNA control. Using a binomial test, only regions that have a p-value <= 0.05 are considered to be significantly enriched compared to the input DNA control.
2011-08-30 | E-GEOD-31477 | biostudies-arrayexpress
Project description:Canonical auxin signalling starts with auxin binding to the receptor complex, followed by modulation of gene transcription and protein abundance (Tan et al., 2007; Chapman and Estelle, 2009; Slade et al., 2017). However, recent studies also showed an alternative mechanism in roots involving intra-cellular auxin perception, but not transcriptional reprogramming (Fendrych et al., 2018). Despite knowledge on effects of auxin on Arabidopsis root growth at the protein and phosphorylation level is increasing (Zhang et al., 2013; Mattei et al., 2013; Slade et al., 2017), it still remains incomplete. To address this gap in our knowledge, we explored the impact of auxin on the root tip proteome and phosphoproteome.
2021-09-09 | PXD021267 | Pride
Project description:Hepatocellular adenomas (HCA) are rare benign tumors mainly developed in women after 2 years of oral contraceptive use (Rooks et al., 1979). HCA are also related to other risk factors (obesity, vascular diseases, androgen and alcohol intake) or to different genetic diseases (Mac Cune Albright syndrome, glycogen storage diseases type 1a and MODY3 diabetes caused by HNF1A germline mutation) (Calderaro et al., 2013; Nault et al., 2013a). Bleeding and malignant transformation to hepatocellular carcinoma (HCC) can occur as severe complications observed respectively in 30-50% and 5% of the cases. In the past 10 years, we identified 4 major molecular subgroups of HCA defined by (1) mutations inactivating HNF1A (H-HCA, 35% of the HCA) (Bacq et al., 2003; Bluteau et al., 2002; Jeannot et al., 2010), (2) activation of ß-catenin by mutations in exon 3 (bHCA, 15%) (Chen et al., 2002), (3) inflammatory phenotype with STAT3 activation (IHCA, 50%) (Bioulac-Sage et al., 2009; Zucman-Rossi et al., 2006), (4) a tumor subgroup of HCA with Sonic Hedgehog pathway activation due to recurrent focal deletions (shHCA) (Nault et al. ,in preparation) and (5) the remaining unclassified tumors (UHCA, 10%) (Bioulac-Sage et al., 2009). Among bHCA, half displayed both inflammatory and ß-catenin activated phenotypes (bIHCA). Inflammatory adenomas (IHCA) are caused by IL6ST somatic mutation activating gp130 in 60% of the cases (Rebouissou et al., 2009) whereas other IHCA are mutated for STAT3 itself (Pilati et al., 2011) or GNAS (Nault et al., 2012) but in the remaining 30% cases no mutation (NM) were identified yet. This molecular classification is currently accepted in clinical practice using either immunohistochemical markers (Bioulac-Sage et al., 2009; Bioulac-Sage et al., 2007) or in radiology at MRI (Laumonier et al., 2008) and it has dramatically improved the diagnosis and prognostic assessment of HCA. HCC derived from HCA malignant transformation (HCC on HCA, 5%)
2019-10-01 | GSE88839 | GEO