Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Pan-cancer predictions of transcription factors mediating aberrant DNA methylation

ABSTRACT: Pan-cancer predictions of transcription factors mediating aberrant DNA methylation

PROVIDER: PRJNA705363 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR13800839.fastq.gz	Fastqsanger.gz
	SRR13800840.fastq.gz	Fastqsanger.gz
	SRR13800841.fastq.gz	Fastqsanger.gz
	SRR13800842.fastq.gz	Fastqsanger.gz
	SRR13800843.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 21

Similar Datasets

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

Predicting Master Transcription Factors from Pan-Cancer Expression Data

Project description:Critical developmental “master transcription factors” (MTFs) can be subverted during tumorigenesis to control expression of oncogenic transcriptional programs. Current approaches to identify MTFs rely on chromatin immunoprecipitation-sequencing data, which is currently unavailable for many cancer types. We developed the CaCTS (Cancer Core Transcription factor Specificity) algorithm to prioritize candidate MTFs using pan-cancer RNA-sequencing data. CaCTS identified candidate MTFs across 34 tumor types and 140 subtypes, including known MTFs. We also made novel predictions, including for cancer types/subtypes for which MTFs are unknown. This included PAX8, SOX17, and MECOM as candidate MTFs in ovarian cancer (OV). In OV cells, these factors are required for viability, lie proximal to super-enhancers, co-occupy regulatory elements globally and co-bind at critical gene loci encoding OV biomarkers. Identification of tumor MTFs, especially for tumor types with limited understanding of transcriptional drivers, paves the way to therapeutic targeting of MTFs in a broad spectrum of cancers.

2021-04-19 | GSE150443 | GEO

Predicting master transcription factors from pan-cancer expression data

2021-09-15 | GSE152885 | GEO

Predicting Master Transcription Factors from Pan-Cancer Expression Data

Project description:Predicting Master Transcription Factors from Pan-Cancer Expression Data

| PRJNA632047 | ENA

Predicting master transcription factors from pan-cancer expression data

Project description:Predicting master transcription factors from pan-cancer expression data

| PRJNA640799 | ENA

Aberrant phase separation of transcription factors in human developmental disorders

Project description:Aberrant phase separation of transcription factors in human developmental disorders

| PRJNA528953 | ENA

Multiomic Profiling Reveals Metabolic Alterations Mediating Aberrant Platelet Activity and Inflammation in Myeloproliferative Neoplasms

Project description:Multiomic Profiling Reveals Metabolic Alterations Mediating Aberrant Platelet Activity and Inflammation in Myeloproliferative Neoplasms

| PRJNA1023674 | ENA

Transcriptome profiling of maize transcription factor mutants to probe gene regulatory network predictions

Project description:Loss-of-function mutant alleles were characterized for 22 maize transcription factors to test the functional relevance of prior yeast-one-hybrid and co-expression gene regulatory network predictions. The transcriptome and phenolic metabolome of these mutants were profiled to document the functional roles of these transcription factors. A subset of the prior gene regulatory network predictions were supported but had limited overall power in predicting changes in gene expression or phenolic compounds. The limitations of testing transcription factor-target predictions with single TF knockouts are discussed.

2024-10-23 | GSE280139 | GEO

A Virus-Packageable CRISPR Screen Identifies Host Factors Mediating Interferon Inhibition of HIV

Project description:A Virus-Packageable CRISPR Screen Identifies Host Factors Mediating Interferon Inhibition of HIV

| PRJNA486290 | ENA

HnRNP K regulates inflammatory gene expression by mediating splicing pattern of transcriptional factors

Project description:HnRNP K regulates inflammatory gene expression by mediating splicing pattern of transcriptional factors

| PRJNA624114 | ENA