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Mus musculus
15 Downloadable Samples
Affymetrix Mouse Expression 430A Array (moe430a)
Description
Unr (upstream of N-ras) is a cytoplasmic RNA-binding protein with cold shock domains, involved in regulation of messenger RNA stability and translation. To address the biological role of Unr, we inactivated the unr gene by homologous recombination in mice and embryonic stem (ES) cells. Embryos deficient for Unr die at mid-gestation, and the main phenotypic defects observed, growth deficiency and absence of neural tube closure, suggest a role of Unr in the balance proliferation/differentiation during early development. Here, we report that in Unr-null ES cell cultures, we observed a greater proportion of partially differentiated colonies, together with dispersed, refractile cells with stellate morphology, reminiscent of primitive endoderm (PrE) cells. DNA microarray, immunostaining, and RNA analyses revealed that Unr-null ES cells express a set of PrE markers, including the GATA6 transcription factor, a key inducer of PrE. Although Unr-deficient cells did not downregulate the pluripotency regulators Oct4, Nanog and Sox2, they grew more slowly than the wild-type lines, and their clonogenicity was lower. Silencing of Unr by RNA interference in ES E14 (129 genetic background) resulted in similar phenotypic and molecular changes as those observed in unr-/- ES cells (C57Bl/6 background). Finally, we show that ectopic expression of Unr in unr-/- ES cells partially reverses the endoderm-specific gene expression and the differentiation phenotype.
Publication Title
The RNA-binding protein Unr prevents mouse embryonic stem cells differentiation toward the primitive endoderm lineage.
Sample Metadata Fields
Specimen part, Treatment
View Samples- Homo sapiens
- 23 Downloadable Samples
- Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)
Description
We have performed gene expression microarray analysis to profile transcriptomic signatures affected by EtOH in human dental pulp stem cells
Publication Title
Genome-wide transcriptomic alterations induced by ethanol treatment in human dental pulp stem cells (DPSCs).
Sample Metadata Fields
Specimen part
View Samples- Homo sapiens
- 16 Downloadable Samples
- Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)
Description
We have performed gene expression microarry analysis to profile molecular alterations in normal human oral keratinocytes that are induced by EtOH and/or nicotine. Our goal is to examine molecular signatures that are dysregulated by EtOH or nicotine and define the effects of co-use of alcohol and nicotine on normal oral epithelial cells and potentially on carcinogenesis.
Publication Title
Gene expression signatures affected by ethanol and/or nicotine in normal human normal oral keratinocytes (NHOKs).
Sample Metadata Fields
Specimen part
View Samples- Homo sapiens
- 18 Downloadable Samples
- Affymetrix Human Exon 1.0 ST Array [transcript (gene) version (huex10st)
Description
Transcriptome analysis of partially degraded and fragmented RNA samples from body fluids
Publication Title
Exon-level expression profiling: a comprehensive transcriptome analysis of oral fluids.
Sample Metadata Fields
No sample metadata fields
View Samples- Homo sapiens
- 10 Downloadable Samples
- Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)
Description
We have performed gene expression microarray analysis to profile transcriptomic signatures affected by EtOH during neural differentiation of human embryonic stem cells
Publication Title
Molecular effect of ethanol during neural differentiation of human embryonic stem cells <i>in vitro.</i>
Sample Metadata Fields
Specimen part
View Samples- Homo sapiens
- 22 Downloadable Samples
- Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)
Description
Pancreatic cancer is the fourth leading cause of cancer death. Lack of early detection technology for pancreatic cancer invariably leads to a typical clinical presentation of incurable disease at initial diagnosis. Oral fluid (saliva) meets the demand for non-invasive, accessible, and highly efficient diagnostic medium. The level of salivary analytes, such as mRNA and microflora, vary upon disease onset; thus possess valuable signatures for early detection and screening. In this study, we evaluated the performance and translational utilities of the salivary transcriptomic and microbial biomarkers for non-invasive detection of early pancreatic cancer. Two biomarker discovery technologies were used to profile transcriptome in saliva supernatant and microflora in saliva pellet. The Affymetrix Human Genome U133 Plus 2.0 Array was used to discover altered gene expression in saliva supernatant. The Human Oral Microbe Identification Microarray (HOMIM) was used to investigate microflora shift in saliva pellet. Biomarkers selected from both studies were subjected to an independent clinical validation using a cohort of 30 early pancreatic cancer, 30 chronic pancreatitis and 30 healthy matched-control saliva samples. Two panels of salivary biomarkers, including eleven mRNA biomarkers and two microbial biomarkers were discovered and validated for pancreatic cancer detection. The logistic regression model with the combination of three mRNA biomarkers (ACRV1, DMXL2 and DPM1) yielded a ROC-plot AUC value of 0.974 (95% CI, 0.896 to 0.997; P < 0.0001) with 93.3% sensitivity and 90% specificity in distinguishing pancreatic cancer patients from healthy subjects. The logistic regression model with the combination of two bacterial biomarkers (Neisseria elongata and Streptococcus mitis) yielded a ROC-plot AUC value of 0.895 (95% CI, 0.784 to 0.961; P < 0.0001) with 96.4% sensitivity and 82.1% specificity in distinguishing pancreatic cancer patients from healthy subjects. Importantly, the logistic regression model with the combination of four biomarkers (mRNA biomarkers, ACRV1, DMXL2 and DPM1; bacterial biomarker, S. mitis) could differentiate pancreatic cancer patients from all non-cancer subjects (chronic pancreatitis and healthy control), yielding a ROC-plot AUC value of 0.949 (95% CI, 0.877 to 0.985; P < 0.0001) with 92.9% sensitivity and 85.5% specificity. This study comprehensively compared the salivary transcriptome and microflora between pancreatic cancer and control subjects. We have discovered and validated eleven mRNA biomarkers and two microbial biomarkers for early detection of pancreatic cancer in saliva. The logistic regression model with four salivary biomarkers can detect pancreatic cancer specifically without the complication of chronic pancreatitis. This is the first report demonstrating the value of multiplex salivary biomarkers for the non-invasive detection of a high impact systemic cancer.
Publication Title
Salivary transcriptomic biomarkers for detection of resectable pancreatic cancer.
Sample Metadata Fields
No sample metadata fields
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GSE13364- Mus musculus
- 6 Downloadable Samples
- Affymetrix Mouse Genome 430 2.0 Array (mouse4302)
Description
Microarray analysis was performed on BWF1 mice spleenocyte cells in control and pCONS treated mice.
Publication Title
Distinct gene signature revealed in white blood cells, CD4(+) and CD8(+) T cells in (NZBx NZW) F1 lupus mice after tolerization with anti-DNA Ig peptide.
Sample Metadata Fields
No sample metadata fields
View Samples- Homo sapiens
- 19 Downloadable Samples
- Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)
Description
A sensitive assay to identify biomarkers that can accurately diagnose the onset of breast cancer using non-invasively collected clinical specimens is ideal for early detection. In this study, we have conducted a prospective sample collection and retrospective blinded validation (PRoBE design) to evaluate the performance and translational utilities of salivary transcriptomic and proteomic biomarkers for the non-invasive detection of breast cancer. The Affymetrix HG U133 Plus 2.0 Array and 2D-DIGE were used to profile transcriptomes and proteomes in saliva supernatants respectively. Significant variations of salivary transcriptomic and proteomic profiles were observed between breast cancer patients and healthy controls. Eleven transcriptomic biomarker candidates and two proteomic biomarker candidates were selected for a preclinical validation using an independent sample set. Transcriptomic biomarkers were validated by RT-qPCR and proteomic biomarkers were validated by quantitative protein immunoblot. Eight mRNA biomarkers and one protein biomarker have been validated for breast cancer detection, yielding ROC-plot AUC values between 0.665 and 0.959. This report provides proof of concept of salivary biomarkers for the non-invasive detection of breast cancer. The salivary biomarkers discriminatory power paves the way for a PRoBE-design definitive validation study.
Publication Title
Discovery and preclinical validation of salivary transcriptomic and proteomic biomarkers for the non-invasive detection of breast cancer.
Sample Metadata Fields
Disease
View Samples- Homo sapiens
- 18 Downloadable Samples
- Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)
Description
10 saliva samples from patients with primary Sojgren's syndrome and 10 saliva samples from control subjects
Publication Title
Salivary proteomic and genomic biomarkers for primary Sjögren's syndrome.
Sample Metadata Fields
Sex
View Samples- Homo sapiens
- 678 Downloadable Samples
- Affymetrix Human Gene 1.0 ST Array (hugene10st)
Description
BACKGROUND: In patients with suspicious pulmonary lesions, bronchoscopy is frequently non-diagnostic. This often results in additional invasive testing, including surgical biopsy, although many patients have benign disease. We sought to validate an airway gene-expression classifier for lung cancer in patients undergoing diagnostic bronchoscopy. METHODS: Two multicenter prospective studies (AEGIS 1 and 2) enrolled 1357 current or former smokers undergoing bronchoscopy for suspected lung cancer. Bronchial epithelial cells were collected from normal appearing mucosa in the mainstem bronchus during bronchoscopy. Patients without a definitive diagnosis from bronchoscopy were followed for 12 months. A gene-expression classifier was used to assess the risk of lung cancer, and its performance was evaluated. RESULTS: A total of 298 patients from AEGIS 1 and 341 from AEGIS 2 met criteria for analysis. Bronchoscopy was non-diagnostic for cancer in 272 of 639 patients (43%; 95%CI, 39-46%). The gene expression classifier correctly identified 431 of 487 patients with cancer (89% sensitivity; 95%CI, 85-91%), and 72 of 152 patients without cancer (47% specificity; 95%CI, 40-55%). The combination of the classifier and bronchoscopy had a sensitivity of 97% (95%CI, 95-98%), which was independent of size, location, stage, and histological subtype of lung cancer. In patients with an intermediate pre-test risk (10-60%) of lung cancer, the NPV of the classifier was 91% (95%CI 75-98%). CONCLUSIONS: In patients with an intermediate risk of lung cancer and a non-diagnostic bronchoscopy, a gene-expression classification of low-risk warrants consideration of a more conservative diagnostic approach that could reduce unnecessary invasive testing in patients with benign disease.
Publication Title
A Bronchial Genomic Classifier for the Diagnostic Evaluation of Lung Cancer.
Sample Metadata Fields
Sex, Specimen part
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