Filters
Technology
Platform
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
View Samples
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
- 23 Downloadable Samples
- Illumina HumanHT-12 V4.0 expression beadchip
Description
Statins, the 3-hydroxy-3-methyl-glutaryl (HMG)-CoA reductase inhibitors, are widely prescribed for treatment of hypercholesterolemia. Although statins are generally well tolerated, up to ten percent of patients taking statins experience muscle related adverse events. Myalgia, defined as muscle pain without elevated creatinine phosphokinase (CPK) levels, is the most frequent reason for discontinuation of statin therapy. The mechanisms underlying statin-associated myalgia are not clearly understood. To elucidate changes in gene expression associated with statin-induced myalgia, we compared profiles of gene expression in the biopsied skeletal muscle from statin-intolerant patients undergoing statin re-challenge versus those of statin-tolerant controls. A robust separation of statin-intolerant and statin-tolerant cohorts was revealed by Principal Component Analysis of differentially expressed genes (DEGs). To identify putative gene expression and metabolic pathways that may be perturbed in skeletal muscles of statin intolerant patients, we subjected DEGs to Ingenuity Pathways (IPA) and DAVID (Database for Annotation, Visualization and Integrated Discovery) analyses. The most prominent pathways altered by statins included cellular stress, apoptosis, senescence and DNA repair (TP53, BARD1, Mre11 and RAD51); activation of pro-inflammatory immune response (CXCL12, CST5, POU2F1); protein catabolism, cholesterol biosynthesis, protein prenylation and RAS-GTPase activation (FDFT1, LSS, TP53, UBD, ATF2, H-ras). Based on these data we tentatively conclude that persistent myalgia in response to statins may emanate from cellular stress underpinned by mechanisms of post-inflammatory repair and regeneration. We also posit that this subset of individuals are genetically predisposed to eliciting altered statin metabolism and/or increased end-organ susceptibility that lead to a range of statin-induced myopathies. This mechanistic scenario further bolstered by the discovery that a number of single nucleotide polymorphisms (e.g., SLCO1B1, SLCO2B1 and RYR2) associated with statin myopathy were observed with increased frequency among statin-intolerant study subjects.
Publication Title
Patients experiencing statin-induced myalgia exhibit a unique program of skeletal muscle gene expression following statin re-challenge.
Sample Metadata Fields
Specimen part
View Samples- Homo sapiens
- 62 Downloadable Samples
- Affymetrix Human Gene 2.1 ST Array (hugene21st)
Description
The main aim of this study was to assess the changes in blood gene expression in UCB patients and to identify genes serving as biomarkers for UCB diagnosis and progression.
Publication Title
A Specific Blood Signature Reveals Higher Levels of S100A12: A Potential Bladder Cancer Diagnostic Biomarker Along With Urinary Engrailed-2 Protein Detection.
Sample Metadata Fields
Age
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