Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Methylation Profiling of Medulloblastoma in a Clinical Setting Permits Sub-Classification and Reveals New Outcome Predictions

ABSTRACT: In this study, we report the first examination of Medulloblastoma (MB) methylation patterns in Arab world, using an extensive primary tumor cohort in tertiray care center. We assessed DNA methylation patterns to sub-classify clinical MB cases and their amenability to clinical applications from archival biopsy material (FPPE). We herein establish methylation events as clinically useful biomarkers and demonstrate how their incorporation into current risk-stratification schemes could significantly improve the accuracy of survival predictions in clinical setting. This holds potential for future precision therapeutic approaches aimed at improving the outcomes of afflicted MB patients.

ORGANISM(S): Homo sapiens

PROVIDER: GSE142627 | GEO | 2019/12/27

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Similar Datasets

Methylation Profiling of Medulloblastoma in a Clinical Setting Permits Sub-Classification and Reveals New Outcome Predictions

Project description:Methylation Profiling of Medulloblastoma in a Clinical Setting Permits Sub-Classification and Reveals New Outcome Predictions

| PRJNA597772 | ENA

Methylation Profiling of Medulloblastoma in a Clinical Setting Permits Sub-classification and Reveals New Outcome Predictions.

Project description: Not available

| S-EPMC7100767 | biostudies-literature

Novel epigenetically dysregulated genes in Oral Submucous Fibrosis

Project description:DNA methylation data of normal buccal mucosa and Oral Submucous Fibrosis (OSF) afflicted mucosa.

2021-07-24 | GSE156669 | GEO

Methylation profiling data from medulloblastoma tumor samples

Project description:Smoothened (SMO)-inhibitors recently entered clinical trials for sonic-hedgehog driven medulloblastoma (SHH-MB). Clinical response appears highly variable. To understand the mechanism(s) of primary resistance and to identify pathways co-operating with aberrant SHH-signaling, we sequenced a large cohort of SHH-MBs across all age groups by sequencing, DNA methylation and expression profiling. Our data show that most adults but only half of the pediatric patients with SHH-MB will respond to SMO inhibition as predicted by molecular analysis of the primary tumor and tested in the SHH-xenografts, demonstrating that the next generation of SMO-inhibitor trials should be based on these predictive biomarkers. To further dissect the biological differences between the different age groups within SHH medulloblastomas, we looked at the DNA methylation profiles of SHH medulloblastoma samples. We investigated the DNA methylation profiles of 46 SHH medulloblastomas across all age groups using the Illumina 450k methylation array.

2014-09-02 | E-GEOD-49377 | biostudies-arrayexpress

Methylation data from medulloblastoma tumor samples

Project description:Smoothened (SMO)-inhibitors recently entered clinical trials for sonic-hedgehog driven medulloblastoma (SHH-MB). Clinical response appears highly variable. To understand the mechanism(s) of primary resistance and to identify pathways co-operating with aberrant SHH-signaling, we sequenced a large cohort of SHH-MBs across all age groups by sequencing, DNA methylation and expression profiling. Our data show that most adults but only half of the pediatric patients with SHH-MB will respond to SMO inhibition as predicted by molecular analysis of the primary tumor and tested in the SHH-xenografts, demonstrating that the next generation of SMO-inhibitor trials should be based on these predictive biomarkers. To further dissect the biological differences between the different age groups within SHH medulloblastomas, we looked at the DNA methylation profiles of SHH medulloblastoma samples. We investigated the DNA methylation profiles of 83 SHH medulloblastomas across all age groups using the Illumina 450k methylation array.

2014-09-02 | E-GEOD-49576 | biostudies-arrayexpress

Proteomic analysis of human Medulloblastoma reveals distinct activated pathways between subgroups

Project description:Deregulations in fundamental signaling pathways are key events in pathogenesis of cancer. One intriguing illustration that still holds blind spots is the pediatric brain tumor arising from the developing cerebellum: medulloblastoma (MB). Extensive high-throughput sequencing led to the characterization of four MB subgroups (WNT, SHH, Group 3 and Group 4) delineated with distinct molecular signatures and clinical outcomes. However, up-to-date these analyses have not attained the global comprehension of their dynamic network complexity. Wishing to uncover a comprehensive view of all MB subgroups we employed a proteomic analysis to integrate accurate protein expression and activity that should ultimately give rise to a realistic picture of how MB cancer cells are regulated. In this study we present the first analysis regrouping methylation status, whole-transcriptome sequencing and quantitative proteomics (proteome and phosphoproteome) using a super SILAC / spiked in strategy across 38 flash frozen primary human MBs (5 WNT, 10 SHH, 10 Group 3 and 13 Group 4). First, our data pinpointed that proteomic analysis could reveal MB subgroup identity. Second, analysis of proteome and phosphoproteome highlighted disregulated signaling pathways that have not been predicted by transcriptomic analysis. Altogether, combined multi-scale analyses of MB have allowed us to identify unrevealed pathways involved in human MB genesis and progression.

2020-07-23 | PXD006607 | Pride

Genetic Association study of Autism in Saudi Females using SNP array

Project description:Multiple variations in olfactory receptor genes are contributing to ASD in females of Arab origin

2022-12-21 | GSE221098 | GEO

Pan-cancer predictions of transcription factors mediating aberrant DNA methylation

Project description:Aberrant DNA methylation is a hallmark of cancer cells. However, the mechanisms underlying changes in DNA methylation remain elusive. Transcription factors initially thought to be repressed from binding by DNA methylation, have recently emerged as being able to shape DNA methylation patterns. Here, we integrated the massive amount of data available from The Cancer Genome Atlas to predict transcription factors drivingAQ1 aberrant DNA methylation in 13 cancer types. We identified differentially methylated regions between cancer and matching healthy samples, searched for transcription factor motifs enriched in those regions and selected transcription factors with corresponding changes in gene expression. We predict transcription factors known to be involved in cancer as well as novel candidates to drive hypo-methylated regions such as FOXA1 and GATA3 in breast cancer, FOXA1 and TWIST1 in prostate cancer and NFE2L2 in lung cancer. We also predict transcription factors that lead to hyper-methylated regions upon transcription factor loss such as EGR1 in several cancer types. Finally, we validate that FOXA1 and GATA3 mediate hypo-methylated regions in breast cancer cells. Our work highlights the importance of some transcription factors as upstream regulators shaping DNA methylation patterns in cancer.

2022-03-16 | GSE167870 | GEO

Genome-wide transcriptional changes in the Clostridium perfringens-afflicted chicken intestine in an avian necrotic enteritis (NE) disease model

Project description:This study utilized comparative global gene expression microarray analysis to evaluate the effects of NE on intestinal immunity of chicken. Two-condition experiment, NE-afflicted chickens vs. Non-infected control chickens. Biological replicates: 2 control replicates, 2 NE-afflicted replicates with dye-switching.

2013-09-26 | E-GEOD-51154 | biostudies-arrayexpress

Methylation data from medulloblastoma tumor samples

2014-09-02 | GSE49576 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

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