Homo sapiens
Ontology highlight
ABSTRACT:
OTHER RELATED OMICS DATASETS IN: GSE47774
PROVIDER: PRJNA264621 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Gene expression microarray has been the primary biomarker platform ubiquitously applied in biomedical research, resulting in enormous data, predictive models and biomarkers accrued. Recently, RNA-seq has looked likely to replace microarrays, but there will be a period where both technologies coexist. This raises two important questions: can microarray-based models and biomarkers be directly applied to RNA-Seq data? Can future RNA-Seq-based predictive models and biomarkers be applied to microarray data to leverage past investment? We systematically evaluated the transferability of predictive models and signature genes between microarray and RNA-seq using two large clinical data sets. The complexity of cross-platform sequence correspondence was considered in the analysis and examined using three human and two rat data sets, and three levels of mapping complexity were revealed. Three algorithms representing different modeling complexity were applied to the three levels of mappings for each of the eight binary endpoints and Cox regression was used to model survival times with expression data. In total, 240,096 predictive models were examined. Signature genes of predictive models are reciprocally transferable between microarray and RNA-seq data for model development, and microarray-based models can accurately predict RNA-seq-profiled samples; while RNA-seq-based models are less accurate in predicting microarray-profiled samples and are affected both by the choice of modeling algorithm and the gene mapping complexity. The results suggest continued usefulness of legacy microarray data and established microarray biomarkers and predictive models in the forthcoming RNA-seq era. Definitions of characteristics: EFS day: number of days for event free survival EFS bin: binary classification of event free survival OS day: number of days for overall survival OS bin: binary classification of overall survival High Risk: Indicating whether a sample belongs to high risk group or not A_EFS_All: binary class label for event free survival for all samples B_OS_All: binary class label for overall survival for all samples C_SEX_All: binary class label for sex D_FAV_All: binary class label for favorable and unfavorable samples E_EFS_HR: binary class label for event free survival of High Risk group F_OS_HR: binary class label for overall survival of High Risk group. The same set of Samples is submitted under GEO accession GSE49711. This Series is a reanalysis of the data. The same set of RNA samples were profiled with microarray and RNA-Seq platforms. We explore the transferability of predictive models and signature genes between microarray and RNA-Seq data
2014-10-22 | E-GEOD-62564 | biostudies-arrayexpress
Project description:Gene expression microarray has been the primary biomarker platform ubiquitously applied in biomedical research, resulting in enormous data, predictive models and biomarkers accrued. Recently, RNA-seq has looked likely to replace microarrays, but there will be a period where both technologies coexist. This raises two important questions: can microarray-based models and biomarkers be directly applied to RNA-Seq data? Can future RNA-Seq-based predictive models and biomarkers be applied to microarray data to leverage past investment? We systematically evaluated the transferability of predictive models and signature genes between microarray and RNA-seq using two large clinical data sets. The complexity of cross-platform sequence correspondence was considered in the analysis and examined using three human and two rat data sets, and three levels of mapping complexity were revealed. Three algorithms representing different modeling complexity were applied to the three levels of mappings for each of the eight binary endpoints and Cox regression was used to model survival times with expression data. In total, 240,096 predictive models were examined. Signature genes of predictive models are reciprocally transferable between microarray and RNA-seq data for model development, and microarray-based models can accurately predict RNA-seq-profiled samples; while RNA-seq-based models are less accurate in predicting microarray-profiled samples and are affected both by the choice of modeling algorithm and the gene mapping complexity. The results suggest continued usefulness of legacy microarray data and established microarray biomarkers and predictive models in the forthcoming RNA-seq era. Definitions of characteristics: EFS day: number of days for event free survival EFS bin: binary classification of event free survival OS day: number of days for overall survival OS bin: binary classification of overall survival High Risk: Indicating whether a sample belongs to high risk group or not A_EFS_All: binary class label for event free survival for all samples B_OS_All: binary class label for overall survival for all samples C_SEX_All: binary class label for sex D_FAV_All: binary class label for favorable and unfavorable samples E_EFS_HR: binary class label for event free survival of High Risk group F_OS_HR: binary class label for overall survival of High Risk group. The same set of Samples is submitted under GEO accession GSE49711. This Series is a reanalysis of the data.
2014-10-22 | GSE62564 | GEO
Project description:Aromatase inhibitors are first-line postmenopausal agents for estrogen receptor alpha (ERa)-positive breast cancer. However, there is considerable response heterogeneity and women frequently relapse. Estrogen deprivation does not completely arrest ERa activity, and transactivation of the unliganded receptor may continue through cross-talk with growth factor pathways. In contrast with aromatase inhibitors, the selective ER downregulator fulvestrant also abrogates ligand-independent ERa activity. The benefit of fulvestrant as an alternative, combination, or sequential therapy to aromatase inhibitor has been reported, but molecular mechanisms underpinning its relative efficacy remain unclear and biomarkers for patient selection are lacking. This study demonstrates, for the first time, that the overall transcriptional response to fulvestrant is of greater magnitude than estrogen deprivation, consistent with its clinical efficacy and more complete blockade of estrogenic signaling. Using a robust integrative approach, we identify a subset of genes differentially affected by fulvestrant that comprises distinct biologic networks, correlates with antiproliferative response, and has potential utility as predictive biomarkers for fulvestrant. Global gene expression profiles from ERα-positive breast carcinomas before and during presurgical treatment with fulvestrant (n = 38) or anastrozole (n = 81), and corresponding in vitro models, were compared. Transcripts responding differently to fulvestrant and estrogen deprivation were identified and integrated using Gene Ontology, pathway and network analyses to evaluate their potential significance. --------------------------------- This represents the data for fulvestrant only
2015-08-07 | E-GEOD-71791 | biostudies-arrayexpress
Project description:Aromatase inhibitors are first-line postmenopausal agents for estrogen receptor alpha (ERa)-positive breast cancer. However, there is considerable response heterogeneity and women frequently relapse. Estrogen deprivation does not completely arrest ERa activity, and transactivation of the unliganded receptor may continue through cross-talk with growth factor pathways. In contrast with aromatase inhibitors, the selective ER downregulator fulvestrant also abrogates ligand-independent ERa activity. The benefit of fulvestrant as an alternative, combination, or sequential therapy to aromatase inhibitor has been reported, but molecular mechanisms underpinning its relative efficacy remain unclear and biomarkers for patient selection are lacking. This study demonstrates, for the first time, that the overall transcriptional response to fulvestrant is of greater magnitude than estrogen deprivation, consistent with its clinical efficacy and more complete blockade of estrogenic signaling. Using a robust integrative approach, we identify a subset of genes differentially affected by fulvestrant that comprises distinct biologic networks, correlates with antiproliferative response, and has potential utility as predictive biomarkers for fulvestrant.
2015-08-07 | GSE71791 | GEO
Project description:This pilot metabolomic study will evaluate brain specimens from an established mouse model of AD, the tq2576 mouse model of cerebral amyloid overexpression (APP), in comparison to their non-transgenic (NTG) littermates. These animals were either on a CR or ad libitum (AL) diet, and specimens were collected at two time points (5 and 15 months of age). Tissue from this cohorts of mice have already undergone microbiome analysis, and await coordinated brain and peripheral tissue assessments. Future analysis will include metabolomics, RNA-seq, and microarray data to assess the gut-brain microbiome system in neurodegenerative disorders.
2016-09-07 | ST000462 | MetabolomicsWorkbench
Project description:This pilot metabolomic study will evaluate cecal specimens from an established mouse model of AD, the tq2576 mouse model of cerebral amyloid overexpression, in comparison to their non-transgenic (ntg) littermates. These animals were either on a CR or ad libitum (AL) diet, and specimens were collected at two time points (5 and 15 months of age). Tissue from this cohorts of mice have already undergone microbiome analysis, and await coordinated brain and peripheral tissue assessments. Future analysis will include metabolomics, RNA-seq, and microarray data to assess the gut-brain microbiome system in neurodegenerative disorders.
2016-09-07 | ST000463 | MetabolomicsWorkbench
Project description:An updated representation of S. meliloti metabolism that was manually-curated and encompasses information from 240 literature sources, which includes transposon-sequencing (Tn-seq) data and Phenotype MicroArray data for wild-type and mutant strains.
2020-06-01 | MODEL2003240001 | BioModels
Project description:Estrogen Receptor alpha (ERa) is the main driver of luminal breast cancer development and progression, and represents the main drug target in patient care. ERa chromatin binding has been extensively studied in breast cancer cell lines and a number of human tumors, often focused on differential binding patterns between groups or conditions. However, little is known about the inter-tumor heterogeneity of ERa chromatin action. Here, we use a large set of ERa ChIP-seq data from 70 ERa+ breast cancers (40 women & 30 men) to explore general inter-patient heterogeneity in ERa DNA binding in breast cancers. We found a total universe of 84,565 and 101,653 ERa sites in females and males respectively, with merely 1.2% and 5% of sites shared in at least half of the tumors analyzed, reflecting a high level of inter-patient heterogeneity. This heterogeneity was found to be most variable at putative enhancers as opposed to promoter regions, potentially reflecting a level of functional redundancy in enhancer action. Interestingly, commonly shared ERa sites showed the highest estrogen-driven enhancer activity, as determined using a massive parallel reporter assay, and were most-engaged in long-range chromatin interactions. In addition, the most-commonly shared ERa-occupied enhancers were found enriched for breast cancer risk SNP loci. We experimentally illustrate such SNVs can impact chromatin binding potential for ERa and its pioneer factor FOXA1. Finally, in the TCGA breast cancer cohort, we could confirm these variations to associate with differences in expression for the target gene. Cumulatively, our data reveal a natural hierarchy of ERa-chromatin interactions in breast cancers within a highly heterogeneous inter-tumor ERa landscape, with the most-common shared regions being most active and affected by germline functional risk SNPs for breast cancer development.
2024-05-22 | PXD045526 | Pride
Project description:Data analysis is a critical part of quantitative proteomics studies in interpreting biological questions. Numerous computational tools including protein quantification, imputation, and differential expression (DE) analysis were generated in the past decade. However, searching optimized tools is still an unsolved issue. Moreover, due to the rapid development of RNA-Seq technology, a vast number of DE analysis methods are created. Applying these newly developed RNA-Seq-oriented tools to proteomics data is still a question that needs to be addressed. In order to benchmark these analysis methods, a proteomics dataset constituted the proteins derived from human, yeast, and drosophila with different ratios were generated. Based on this dataset, DE analysis tools (including array-based and RNA-Seq based), imputation algorithms, and protein quantification methods were compared and benchmarked. This study provided useful information on analyzing quantitative proteomics datasets. All the methods used in this study were integrated into Perseus which are available at https://www.maxquant.org/perseus.
2022-11-16 | PXD029891 | JPOST Repository
Project description:Aged STAT1-/- female mice spontaneously develop ERa+ PR+ mammary tumors that exhibit strikingly similar hormone-sensitivity and -dependency as human ERa+ luminal breast cancers. We used microarray data to compare the genetic relationships between the STAT1-/- mammary tumors and human breast cancers.
2011-12-15 | GSE31942 | GEO