Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Molecular analysis of giant cell lesions

ABSTRACT: Data Access Committee EGAC00001000925

PROVIDER: EGAC00001000925 | EGA |

REPOSITORIES: EGA

ACCESS DATA

Json Xml

Similar Datasets

Molecular analysis of giant cell lesions

Project description:Giant cell lesions of the jaws are aggressive proliferative conditions that affect young and adult patients. The genetic profile of the tumour has not been established yet. In this project we performed whole exome sequencing of 18 samples and RNAseq (n=6) of giant cell lesions of the jaws. All the tumours are sporadic and only non-syndromic patients were included.

| EGAS00001002910 | EGA

Joint Addiction, Aging, and Mental Health Data Access Committee General Research Use Datasets

Project description:Joint Addiction, Aging, and Mental Health Data Access Committee General Research Use Datasets

| PRJNA934661 | ENA

Joint Addiction, Aging, and Mental Health Data Access Committee General Research Use Datasets

Project description:Joint Addiction, Aging, and Mental Health Data Access Committee General Research Use Datasets

| PRJNA934662 | ENA

Giant cell lesions of the craniofacial bones.

Project description: Not available

| S-EPMC4245408 | biostudies-literature

Specific molecular signatures underlie response to decitabine in CMML [ERRBS]

Project description:Myelodysplastic syndromes and chronic myelomonocytic leukemia (CMML) are characterized by mutations in epigenetic modifiers and aberrant DNA methylation. DNA methyltransferase inhibitors (DMTis) are used to treat these disorders, but response is highly variable with few means to predict which patients will benefit. To develop a molecular means of predicting response at diagnosis, we examined baseline differences in mutations, DNA methylation, and gene expression in 40 CMML patients responsive and resistant to decitabine (DAC). While somatic mutations did not differentiate responders and non-responders, we were able to identify for the first time 158 differentially methylated regions (DMRs) at baseline between responders and non-responders using next-generation sequencing. These DMRs were primarily localized to non-promoter regions and overlapped with distal regulatory enhancers. Using the methylation profiles, we developed an epigenetic classifier that accurately predicted DAC response at the time of diagnosis. We also found 53 differentially expressed genes between responders and non-responders. Genes up-regulated in responders were enriched in the cell cycle, potentially contributing to effective DAC incorporation. Two chemokines overexpressed in non-responders -- CXCL4 and CXCL7 -- were able to block the effect of DAC on normal CD34+ and primary CMML cells in vitro, suggesting their up-regulation contributes to primary DAC resistance. DNA methylation profiling in bone marrow mononuclear cells (BM MNC) from 39 CMML patients (19 decitabine responders vs. 20 non-responders).

2015-03-31 | E-GEOD-61161 | biostudies-arrayexpress

Joint Addiction, Aging, and Mental Health Data Access Committee General Research Use Datasets (GSR restricted) Collection

Project description:Joint Addiction, Aging, and Mental Health Data Access Committee General Research Use Datasets (GSR restricted) Collection

| PRJNA1018662 | ENA

Joint Addiction, Aging, and Mental Health Data Access Committee General Research Use Datasets (GSR restricted) Collection

Project description:Joint Addiction, Aging, and Mental Health Data Access Committee General Research Use Datasets (GSR restricted) Collection

| PRJNA1018664 | ENA

Giant congenital nevi exome sequencing

Project description:Data Access Committee EGAC00001001659

| EGAC00001001659 | EGA

Flat colorectal cancer precursor lesions: epidemiological aspects and molecular analysis.

Project description:Interventions: 1. Clinical data registration; 2. Molecular analysis of polypoid vs. flat colorectal lesions. Primary outcome(s): 1. Prevalence of flat colorectal lesions in a Dutch population; 2. Clinical characteristics (e.g. location of lesions, percentage of high-grade dysplasia or early cancer); 3. Molecular charcteristics (epigenetic: methylation status and genetic) of flat vs. polypoid colorectal lesions; 4. Prevalence of flat advanced colorectal cancers vs. polypoid advanced colorecal cancers: clinical features of these lesions (e.g. tumor stage); 5. Relation between serrated and adenomatous polyps. Study Design: Non-randomized controlled trial, Open (masking not used), N/A , unknown, Factorial

| 16199 | ecrin-mdr-crc

Distribution of arterial lesions in Takayasu's arteritis and giant cell arteritis.

Project description:To compare patterns of arteriographic lesions of the aorta and primary branches in patients with Takayasu's arteritis (TAK) and giant cell arteritis (GCA).Patients were selected from two North American cohorts of TAK and GCA. The frequency of arteriographic lesions was calculated for 15 large arteries. Cluster analysis was used to derive patterns of arterial disease in TAK versus GCA and in patients categorised by age at disease onset. Using latent class analysis, computer derived classification models based upon patterns of arterial disease were compared with traditional classification.Arteriographic lesions were identified in 145 patients with TAK and 62 patients with GCA. Cluster analysis demonstrated that arterial involvement was contiguous in the aorta and usually symmetric in paired branch vessels for TAK and GCA. There was significantly more left carotid (p=0.03) and mesenteric (p=0.02) artery disease in TAK and more left and right axillary (p<0.01) artery disease in GCA. Subclavian disease clustered asymmetrically in TAK and in patients ?55 years at disease onset and clustered symmetrically in GCA and patients >55 years at disease onset. Computer derived classification models distinguished TAK from GCA in two subgroups, defining 26% and 18% of the study sample; however, 56% of patients were classified into a subgroup that did not strongly differentiate between TAK and GCA.Strong similarities and subtle differences in the distribution of arterial disease were observed between TAK and GCA. These findings suggest that TAK and GCA may exist on a spectrum within the same disease.

| S-EPMC3729734 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data