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GSE42715
Homo sapiens
9 Downloadable Samples
Affymetrix Human Exon 1.0 ST Array [transcript (gene) version (huex10st)
Description
Diabetes and obesity are widespread diseases with signifciant socioeconomic implications. We used three different types of human adipose tissue (epigastric, visceral, and subcutaneous) in order to determine differences in global gene expression between these adipose depots in severely obese patients.
Publication Title
Gene expression profiling in subcutaneous, visceral and epigastric adipose tissues of patients with extreme obesity.
Sample Metadata Fields
Specimen part, Race
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
- 437 Downloadable Samples
- Affymetrix HT HG-U133+ PM Array Plate (hthgu133pluspm)
Description
Purpose: Severe late normal tissue damage limits radiotherapy treatment regimens. This study aims to validate -H2AX foci decay ratios and induced expression levels of DNA double strand break (DSB) repair genes, found in a retrospective study, as possible predictors for late radiation toxicity. Methods and Materials: Prospectively, decay ratios (initial/residual -H2AX foci numbers) and genome-wide expression profiles were examined in ex vivo irradiated lymphocytes of 198 prostate cancer patients. All patients were followed 2 years after radiotherapy, clinical characteristics were assembled and toxicity was recorded using the Common Terminology Criteria (CTCAE) v4.0. Results: No clinical factors were correlated with late radiation toxicity. Analysis of -H2AX foci uncovered a negative correlation between the foci decay ratio and toxicity grade. Significantly smaller decay ratios were found in grade3 compared to grade 0 patients (p=0.02), indicating less efficient DNA-DSB repair in radio-sensitive patients. Moreover, utilizing a foci decay ratio threshold determined in our previous retrospective study correctly classified 23 of the 28 grade3 patients (sensitivity, 82%) and 9 of the 14 grade 0 patients (specificity, 64%). Grade of toxicity also correlated with a reduced induction of the homologous recombination (HR) repair gene-set. The difference in average fold induction of the HR gene-set was most pronounced between grade 0 and grade3 patients (p=0.008). Conclusions: Reduced responsiveness of HR repair genes to irradiation and inefficient DSB repair correlate with an increased risk of late radiation toxicity. Using a decay ratio classifier, we could correctly classify 82% of the patients with grade3 toxicity. Additional studies are required to further optimize and validate the foci decay assay and to assess its predictive value for late radiation toxicity in patients prostate cancer
Publication Title
Prostate Cancer Patients with Late Radiation Toxicity Exhibit Reduced Expression of Genes Involved in DNA Double-Strand Break Repair and Homologous Recombination.
Sample Metadata Fields
Specimen part, Subject
View Samples- Homo sapiens
- 224 Downloadable Samples
- Affymetrix Human Genome U133A Array (hgu133a)
Description
Objective: We hypothesized that type 1 diabetes (T1D) is accompanied by changes in gene expression in peripheral blood mononuclear cells (PBMCs) due to dysregulation of adaptive and innate immunity, counterregulatory responses to immune dysregulation, insulin deficiency and hyperglycemia. Research Design and Methods: Microarray analysis was performed on PBMCs from 43 patients with newly diagnosed T1D, 12 patients with newly diagnosed type 2 diabetes (T2D) and 24 healthy controls. One and four month follow-up samples were obtained from 20 of the T1D patients.
Publication Title
Gene expression in peripheral blood mononuclear cells from children with diabetes.
Sample Metadata Fields
Sex, Age, Treatment, Race
View Samples- Homo sapiens
- 138 Downloadable Samples
- Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)
Description
Background:
Publication Title
Prediction of recurrence-free survival in postoperative non-small cell lung cancer patients by using an integrated model of clinical information and gene expression.
Sample Metadata Fields
Sex, Age, Specimen part
View Samples- Homo sapiens
- 129 Downloadable Samples
- Affymetrix Human Gene 1.0 ST Array (hugene10st)
Description
This SuperSeries is composed of the SubSeries listed below.
Publication Title
Translational validation of personalized treatment strategy based on genetic characteristics of glioblastoma.
Sample Metadata Fields
Specimen part
View Samples- Homo sapiens
- 103 Downloadable Samples
- Affymetrix HT HG-U133+ PM Array Plate (hthgu133pluspm)
Description
This SuperSeries is composed of the SubSeries listed below.
Publication Title
Consensus molecular subtypes of colorectal cancer are recapitulated in in vitro and in vivo models.
Sample Metadata Fields
Specimen part, Disease, Disease stage, Cell line, Subject
View Samples- Homo sapiens
- 104 Downloadable Samples
- Affymetrix Human Gene 1.0 ST Array (hugene10st)
Description
Glioblastoma (GBM) heterogeneity in the genomic and phenotypic properties has potentiated personalized approach against specific therapeutic targets of each GBM patient. The Cancer Genome Atlas (TCGA) Research Network has been established the comprehensive genomic abnormalities of GBM, which sub-classified GBMs into 4 different molecular subtypes. The molecular subtypes could be utilized to develop personalized treatment strategy for each subtype. We applied a classifying method, NTP (Nearest Template Prediction) method to determine molecular subtype of each GBM patient and corresponding orthotopic xenograft animal model. The models were derived from GBM cells dissociated from patient's surgical sample. Specific drug candidates for each subtype were selected using an integrated pharmacological network database (PharmDB), which link drugs with subtype specific genes. Treatment effects of the drug candidates were determined by in vitro limiting dilution assay using patient-derived GBM cells primarily cultured from orthotopic xenograft tumors. The consistent identification of molecular subtype by the NTP method was validated using TCGA database. When subtypes were determined by the NTP method, orthotopic xenograft animal models faithfully maintained the molecular subtypes of parental tumors. Subtype specific drugs not only showed significant inhibition effects on the in vitro clonogenicity of patient-derived GBM cells but also synergistically reversed temozolomide resistance of MGMT-unmethylated patient-derived GBM cells. However, inhibitory effects on the clonogenicity were not totally subtype-specific. Personalized treatment approach based on genetic characteristics of each GBM could make better treatment outcomes of GBMs, although more sophisticated classifying techniques and subtype specific drugs need to be further elucidated.
Publication Title
Translational validation of personalized treatment strategy based on genetic characteristics of glioblastoma.
Sample Metadata Fields
Specimen part
View Samples- Mus musculus
- 7 Downloadable Samples
Illumina HiSeq 2000, Affymetrix Mouse Gene 2.0 ST Array (mogene20st)
Description
This SuperSeries is composed of the SubSeries listed below.
Publication Title
Regulation of transcriptional elongation in pluripotency and cell differentiation by the PHD-finger protein Phf5a.
Sample Metadata Fields
Specimen part, Cell line
View Samples- Homo sapiens
- 90 Downloadable Samples
- Affymetrix Human Genome U133A Array (hgu133a)
Description
Background: The ability to predict the spatial frequency of relapses in multiple sclerosis (MS) would enable treating physicians to decide when to intervene more aggressively and to plan clinical trials more accurately. Methods: In the current study our objective was to determine if subsets of genes can predict the time to the next acute relapse in patients with MS. Data-mining and predictive modeling tools were utilized to analyze a gene-expression dataset of 94 non-treated patients; 62 patients with definite MS and 32 patients with clinically isolated syndrome (CIS). The dataset included the expression levels of 10,594 genes and annotated sequences corresponding to 22,215 gene-transcripts that appear in the microarray. Results: We designed a two stage predictor. The first stage predictor was based on the expression level of 10 genes, and predicted the time to next relapse with a resolution of 500 days (error rate 0.079, p< 0.001). If the predicted relapse was to occur in less than 500 days, a second stage predictor based on an additional different set of 9 genes was used, resulting in a prediction with a resolution of 50 days as to the timing of the next relapse. The error rate of this predictor was 2.3 fold lower than the error rate of random predictions (error rate = 0.35, p<0.001). The predictors were further evaluated and found effective not only in untreated patients but were also valid for MS patients which subsequently received immunomodulatory treatments after the initial testing (the error rate of the first level predictor was < 0.18 with p<0.001 for all the patient groups). Conclusions: We conclude that gene expression analysis is a valuable tool that can be used in clinical practice to predict future MS disease activity. Similar approach can be also useful for dealing with other autoimmune diseases that characterized by relapsing-remitting nature
Publication Title
Prediction of acute multiple sclerosis relapses by transcription levels of peripheral blood cells.
Sample Metadata Fields
Sex, Age, Specimen part, Disease, Disease stage
View Samples