Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1145626 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR30170100_1.fastq.gz | Fastqsanger.gz | |||
| SRR30170100_2.fastq.gz | Fastqsanger.gz | |||
| SRR30170101_1.fastq.gz | Fastqsanger.gz | |||
| SRR30170101_2.fastq.gz | Fastqsanger.gz | |||
| SRR30170102_1.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 5 of 152 |
Similar Datasets
Project description:Although multiple gene and protein expression have been extensively profiled in human pulmonary arterial hypertension (PAH), the mechanism for the development and progression of pulmonary hypertension remains elusive. Analysis of the global metabolomic heterogeneity within the pulmonary vascular system leads to a better understanding of disease progression. Using a combination of high-throughput liquid-and-gas-chromatography-based mass spectrometry, we showed unbiased metabolomic profiles of disrupted glycolysis, increased TCA cycle, and fatty acid metabolites with altered oxidation pathways in the severe human PAH lung. The results suggest that PAH has specific metabolic pathways contributing to increased ATP synthesis for the vascular remodeling process in severe pulmonary hypertension. These identified metabolites may serve as potential biomarkers for the diagnosis of severe PAH. By profiling metabolomic alterations of the PAH lung, we reveal new pathogenic mechanisms of PAH in its later stage, which may differ from the earlier stage of PAH, opening an avenue of exploration for therapeutics that target metabolic pathway alterations in the progression of PAH. Global profiles were determined in human lung tissue and compared across 11 normal and 12 severe pulmonary arterial hypertension patients. Using a combination of microarray and high-throughput liquid-and-gas-chromatography-based mass spectrometry, we showed unbiased metabolomic profiles of disrupted glycolysis, increased TCA cycle, and fatty acid metabolites with altered oxidation pathways in the severe human PAH lung.
2014-11-14 | E-GEOD-53408 | biostudies-arrayexpress
Project description:Metabolic syndrome is a common and complicated metabolic disorder and defined as a clustering of metabolic risk factors such as insulin resistance or diabetes, obesity, hypertension, and hyperlipidemia. The identification of accurate and effective biomarkers is beneficial to the early diagnosis and treatment of metabolic syndrome. Our study firstly detected the plasma miRNA expression profile of MetS patients compared with control group by high-throughput sequencing and integrated bioinformatics approaches. To our best knowledge, our study firstly perform high-throughput sequencing to obtain the circulating microRNA expression data in MetS plasma, and identified several potential plasma biomarkers for MetS.
2023-07-24 | GSE155096 | GEO
Project description:Although multiple gene and protein expression have been extensively profiled in human pulmonary arterial hypertension (PAH), the mechanism for the development and progression of pulmonary hypertension remains elusive. Analysis of the global metabolomic heterogeneity within the pulmonary vascular system leads to a better understanding of disease progression. Using a combination of high-throughput liquid-and-gas-chromatography-based mass spectrometry, we showed unbiased metabolomic profiles of disrupted glycolysis, increased TCA cycle, and fatty acid metabolites with altered oxidation pathways in the severe human PAH lung. The results suggest that PAH has specific metabolic pathways contributing to increased ATP synthesis for the vascular remodeling process in severe pulmonary hypertension. These identified metabolites may serve as potential biomarkers for the diagnosis of severe PAH. By profiling metabolomic alterations of the PAH lung, we reveal new pathogenic mechanisms of PAH in its later stage, which may differ from the earlier stage of PAH, opening an avenue of exploration for therapeutics that target metabolic pathway alterations in the progression of PAH.
2014-11-14 | GSE53408 | GEO
Project description:Differentially expressed noncoding RNAs as potential biomarkers for DIPG diagnosis and prognosis
| PRJNA575738 | ENA
Project description:A DNA hypermethylation profile reveals new potential biomarkers for prostate cancer diagnosis and prognosis
| PRJNA197502 | ENA
Project description:Gastric cancer is an important health problem because it is difficult to diagnose and treat in advanced stage. This makes that the prognosis of gastric cancer patients remains scarce. Currently it is known that the cause of gastric cancer is attributed to chronic infection with Helicobacter pylori. Its persistent infection leads to development of chronic atrophic gastritis that is considered as a predecessor stage of intestinal-type gastric cancer. The understanding of the alteration of molecular mechanisms during the early stages of the development of gastric cancer, and the identification of their potential biomarkers can allow a rapid diagnosis that leads to an improvement diagnosis and increase the patient’s prognosis. We analyzed gene expression profiles of patients with chronic atrophic gastritis and gastric cancer through microarray analysis, functional enrichment analysis and validation of gene expression by quantitative PCR. Gene expression profiles in patients with chronic atrophic gastritis showed molecular changes of the gastric mucosa, which leads to intestinal metaplasia and subsequently, gastric cancer. In gastric cancer the gene expression profile showed the stage of tumor progression, the product of these genes are potential biomarkers of early stages of cancer that can be potential therapeutic targets. Accordingly, the transcriptome analysis revealed several gene groups are related to development of chronic atrophic gastritis, some of which were inhibited in gastric cancer patients. The increased expression of CLDN1, CLDN7, OLFM4, c-Myc and MMP-9 genes in chronic atrophic gastritis and gastric cancer point outs to their use as promising biomarkers for the early diagnosis of gastric cancer.
2019-12-18 | GSE116312 | GEO
Project description:Clinical practice currently faces a significant shortfall in specific biomarkers needed for diagnosing right ventricular (RV) remodeling in patients with pulmonary hypertension (PH). MiRNAs are crucial regulators of cardiovascular development, influencing processes like angiogenesis, cardiomyocyte differentiation, cell cycle, cardiac electrical activity, and cardiomyocyte growth. Furthermore, circulating miRNAs are cell-or tissue-specific, relatively stable in serum, and resistant to degradation factors. These properties make serum miRNAs possible promising biomarkers for predictive diagnosis of cardiovascular disease (CVD). This study systematically screened and identified candidate serum miRNAs as potential diagnostic biomarkers for RV remodeling in PH patients.
2025-01-18 | GSE287146 | GEO
Project 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. Keywords: Salivary biomarker, pancreatic cancer, early detection, salivary transcriptome, salivary microflora
2008-12-31 | GSE14245 | GEO