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GSE53454
Homo sapiens
86 Downloadable Samples
Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)
Description
In the context of T1 Diabetes, pro-inflammatory cytokines IL-1 and IFN- are known to contribute to -cell apoptosis;
Publication Title
Temporal profiling of cytokine-induced genes in pancreatic β-cells by meta-analysis and network inference.
Sample Metadata Fields
Specimen part, Treatment, Time
View Samples- Rattus norvegicus
- 40 Downloadable Samples
- Affymetrix Rat Gene 1.0 ST Array (ragene10st)
Description
In the context of T1 Diabetes, pro-inflammatory cytokines IL-1 and IFN- are known to contribute to -cell apoptosis;
Publication Title
Temporal profiling of cytokine-induced genes in pancreatic β-cells by meta-analysis and network inference.
Sample Metadata Fields
Cell line, Treatment, Time
View Samples- Homo sapiens
- 737 Downloadable Samples
- Affymetrix Human Genome U133A Array (hgu133a)
Description
Purpose: A number of microarray studies have reported distinct molecular profiles of breast cancers (BC): basal-like, ErbB2-like and two to three luminal-like subtypes. These were associated with different clinical outcomes. However, although the basal and the ErbB2 subtypes are repeatedly recognized, identification of estrogen receptor (ER)-positive subtypes has been inconsistent. Refinement of their molecular definition is therefore needed.
Publication Title
Definition of clinically distinct molecular subtypes in estrogen receptor-positive breast carcinomas through genomic grade.
Sample Metadata Fields
Age, Disease stage
View Samples- Homo sapiens
- 1 Downloadable Sample
- Affymetrix Human Gene 2.0 ST Array (hugene20st)
Description
Comparison between ex vivo immature, mature and stimulated T cells and in vitro generated counterparts. The T cells generated in vitro were cultured on OP9-DL1 stroma supplied with growth factors.
Publication Title
In vitro generation of mature, naive antigen-specific CD8(+) T cells with a single T-cell receptor by agonist selection.
Sample Metadata Fields
Specimen part
View Samples- Homo sapiens
- 802 Downloadable Samples
- Affymetrix Human Genome U219 Array (hgu219)
Description
The host response in critically ill patients with sepsis, septic shock remains poorly defined. Considerable research has been conducted to accurately distinguish patients with sepsis from those with non-infectious causes of disease. Technological innovations have positioned systems biology at the forefront of biomarker discovery. Analysis of the whole-blood leukocyte transcriptome enables the assessment of thousands of molecular signals beyond simply measuring several proteins in plasma, which for use as biomarkers is important since combinations of biomarkers likely provide more diagnostic accuracy than the measurement of single ones or a few. Evidence suggests that genome-wide transcriptional profiling of blood leukocytes can assist in differentiating between infection and non-infectious causes of severe disease. Of importance, RNA biomarkers have the potential advantage that they can be measured reliably in rapid quantitative reverse transcriptase polymerase chain reaction (qRT-PCR)-based point of care tests.
Publication Title
A molecular biomarker to diagnose community-acquired pneumonia on intensive care unit admission.
Sample Metadata Fields
Sex, Age
View Samples- Homo sapiens
- 77 Downloadable Samples
- Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)
Description
Background: Estrogen receptor positive (ER+) breast cancers (BC) are heterogeneous with regard to their clinical behavior and response to therapies. The ER is currently the best predictor of response to the anti-estrogen agent tamoxifen, yet up to 30-40% of ER+BC will relapse despite tamoxifen treatment. New prognostic biomarkers and further biological understanding of tamoxifen resistance are required. We used gene expression profiling to develop an outcome-based predictor using a training set of 255 ER+ BC samples from women treated with adjuvant tamoxifen monotherapy. We used clusters of highly correlated genes to develop our predictor to facilitate both signature stability and biological interpretation. Independent validation was performed using 362 tamoxifen-treated ER+ BC samples obtained from multiple institutions and treated with tamoxifen only in the adjuvant and metastatic settings.
Publication Title
Predicting prognosis using molecular profiling in estrogen receptor-positive breast cancer treated with tamoxifen.
Sample Metadata Fields
Age, Disease stage, Treatment
View Samples- Homo sapiens
- 105 Downloadable Samples
- Affymetrix Human Exon 1.0 ST Array [transcript (gene) version (huex10st)
Description
Background: Systemic inflammation is a whole body reaction that can have an infection-positive (i.e. sepsis) or infection-negative origin. It is important to distinguish between septic and non-septic presentations early and reliably, because this has significant therapeutic implications for critically ill patients. We hypothesized that a molecular classifier based on a small number of RNAs expressed in peripheral blood could be discovered that would: 1) determine which patients with systemic inflammation had sepsis; 2) be robust across independent patient cohorts; 3) be insensitive to disease severity; and 4) provide diagnostic utility. The overall goal of this study was to identify and validate such a molecular classifier. Methods and Findings: We conducted an observational, non-interventional study of adult patients recruited from tertiary intensive care units (ICU). Biomarker discovery was conducted with an Australian cohort (n = 105) consisting of sepsis patients and post -surgical patients with infection-negative systemic inflammation. Using this cohort, a four-gene classifier consisting of a combination of CEACAM4, LAMP1, PLA2G7 and PLAC8 RNA biomarkers was identified. This classifier, designated SeptiCyte Lab, was externally validated using RT-qPCR and receiver operating characteristic (ROC) curve analysis in five cohorts (n = 345) from the Netherlands. Cohort 1 (n=59) consisted of unambiguous septic cases and infection-negative systemic inflammation controls; SeptiCyte Lab gave an area under curve (AUC) of 0.96 (95% CI: 0.91-1.00). ROC analysis of a more heterogeneous group of patients (Cohorts 2-5; 249 patients after excluding 37 patients with infection likelihood possible) gave an AUC of 0.89 (95% CI: 0.85-0.93). Disease severity, as measured by Sequential Organ Failure Assessment (SOFA) score or the Acute Physiology and Chronic Health Evaluation (APACHE) IV score, was not a significant confounding variable. The diagnostic utility o f SeptiCyte Lab was evaluated by comparison to various clinical and laboratory parameters that would be available to a clinician within 24 hours of ICU admission. SeptiCyte Lab was significantly better at differentiating sepsis from infection-negative systemic inflammation than all tested parameters, both singly and in various logistic combinations. SeptiCyte Lab more than halved the diagnostic error rate compared to PCT in all tested cohorts or cohort combinations. Conclusions: SeptiCyte Lab is a rapid molecular assay that may be clinically useful in the management of ICU patients with systemic inflammation.
Publication Title
A Molecular Host Response Assay to Discriminate Between Sepsis and Infection-Negative Systemic Inflammation in Critically Ill Patients: Discovery and Validation in Independent Cohorts.
Sample Metadata Fields
No sample metadata fields
View Samples- Homo sapiens
- 195 Downloadable Samples
- Affymetrix Human Genome U219 Array (hgu219)
Description
This SuperSeries is composed of the SubSeries listed below.
Publication Title
Combination of Gene Expression Signature and Model for End-Stage Liver Disease Score Predicts Survival of Patients With Severe Alcoholic Hepatitis.
Sample Metadata Fields
Specimen part, Disease
View Samples- Homo sapiens
- 86 Downloadable Samples
- Affymetrix Human Genome U219 Array (hgu219)
Description
Corticosteroids are the current standard of care to improve short-term mortality in severe alcoholic hepatitis (AH), although nearly 40% of the patients do not respond and accurate pre-treatment predictors are lacking. We developed 123-gene prognostic score based on molecular and clinical variables before initiation of corticosteroids. Furthermore, The gene signature was implemented in an FDA-approved platform (NanoString), and verified for technical validity and prognostic capability. Here we demonstrated that a Nanostring-based gene expressoin risk classification is useful to predict mortality in patients with severe alcoholic hepatitis who were treated by corticosteroid
Publication Title
Combination of Gene Expression Signature and Model for End-Stage Liver Disease Score Predicts Survival of Patients With Severe Alcoholic Hepatitis.
Sample Metadata Fields
Specimen part, Disease
View Samples- Homo sapiens
- 71 Downloadable Samples
- Affymetrix Human Genome U219 Array (hgu219)
Description
Corticosteroids are the current standard of care to improve short-term mortality in severe alcoholic hepatitis (AH), although nearly 40% of the patients do not respond and accurate pre-treatment predictors are lacking. We developed 123-gene prognostic score based on molecular and clinical variables before initiation of corticosteroids. Furthermore, The gene signature was implemented in an FDA-approved platform (NanoString), and verified for technical validity and prognostic capability. Here we demonstrated that a Nanostring-based gene expressoin risk classificatoin is useful to predict mortality in patients with severe alcoholic hepatitis who were treated by corticosteroid
Publication Title
Combination of Gene Expression Signature and Model for End-Stage Liver Disease Score Predicts Survival of Patients With Severe Alcoholic Hepatitis.
Sample Metadata Fields
No sample metadata fields
View Samples