Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Subtype-specific enhancer RNAs define transcriptional regulators and prognosis in breast cancers

ABSTRACT: Gene expression is tightly controlled by DNA elements called enhancers by associating with lineage-specific transcription factors. These enhancers transcribe noncoding RNAs (called enhancer RNAs or eRNAs). eRNA expression is an early indicator of transcription factor activity and is associated with treatment response and survival in cancer patients. By analysing ~ 300 000 eRNA loci profiled using RNA-sequencing data sets from 975 breast cancer patients using machine learning approaches, we categorised eRNAs specific to breast cancer molecular subtypes and survival. We associated these eRNAs with subtype-specific mRNAs to define proximal co-expressed regulatory eRNAs (ProxCReAm), which are enriched in pathways characteristic of their respective subtypes. Interestingly, cistrome and transcription factor motif analyses on these eRNAs highlighted involvement of diverse nuclear receptors (GR/AHR for luminal and GR/RAR for basal) and FOX factors in luminal regions. Moreover, luminal eRNAs were associated with better outcomes and Her2 eRNAs with worse outcomes in patients. Overall, we demonstrate that machine learning approaches performed on RNA-seq data sets can classify functionally relevant subtype-specific and prognostic eRNAs, which can identify critical gene pathways and transcription factor networks in breast cancer.

ORGANISM(S): Homo sapiens

PROVIDER: GSE289977 | GEO | 2026/03/17

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Enhancer RNA transcriptomics-wide association study reveals an atlas of pan-cancer susceptibility eRNAs

Project description:Increasing evidence has demonstrated that enhancer RNAs (eRNAs) play essential roles in human diseases. However, the complete identification of disease-related subsets of eRNAs remains a major challenge. Here, we construct an atlas of common and rare genetic variants influencing the expression of enhancer RNAs across 49 human tissues. We identify 11,757 eRNA quantitative trait loci (eRNA-QTLs) associated with the expression of 89.75% annotated eRNAs. Mechanically, eRNA-QTLs frequently altered the binding motifs of transcription factors and were further experimentally validated by CRISPR-based base editing. We also observed that 28.48% of cancer signals were co-localized with eRNA-QTLs. Utilizing our newly developed eRNA transcriptome-wide association study (eRNA-TWAS) model, we have effectively identified 259 cancer susceptibility eRNAs spanning 23 distinct cancer types. Notably, among the identified eRNAs, these eRNA-linked cancer susceptibility genes exhibit significant dependence across various cancer cell lines. Interestingly, cancer risk can be significantly influenced by rare functional variants that are linked to eRNAs with transcriptomic outliers. Additionally, we develop a comprehensive and flexible data portal for exploring the genetic underpinnings of eRNAs. Overall, this study reveals a systematic landscape of genetic dependencies on eRNAs across pan-cancers and significantly expands the pool of disease-associated eRNAs.

2025-02-13 | GSE242322 | GEO

Breast cancer identity is determined by glucocorticoid receptor activity status

Project description:Glucocorticoid receptor (GR) is a ligand-inducible transcription factor with an intricate role in cancer biology. Using an in silico designed GR activity signature we show that GR is a tumor suppressor across diverse primary cancers. In breast cancer, GR activity status determines luminal identity, and importantly, relates to patients’ outcomes. We illustrate that GR suppresses tumor growth, mediated through its engagement with the estrogen receptor-α (ER). This steroid hormone receptor cross-talk leads to redistribution of ERα on chromatin, ultimately leading to expression of ZBTB16 gene. We define ZBTB16 as a transcriptional repressor and a tumor suppressor in ER-positive breast cancer. Importantly, highly aggressive ER-positive breast cancer cells displaying absence of GR activity can be eradicated if GR-induced gene repression is mimicked by inhibitors of the epigenetic pathway. In line with this, epigenetic regulators are highly expressed upon GR activity loss, leading to vulnerability of aggressive breast cancer cells to clinically available epigenetic inhibitors. Our findings indicate that GR functions as a tumor suppressor by repositioning ER to specific sites on chromatin, modulating targetable pathways, which has important implications for patients’ prognosis and therapeutic interventions.

2023-10-02 | PXD039209 | Pride

Activation of the P-TEFb Complex by Lethal Prostate Cancer-Associated Enhancer RNAs [RNA-Seq]

Project description:Castration resistant prostate cancer (CRPC) is a lethal disease1-4. Aberrant activation of the androgen receptor (AR) becomes a central mechanism contributing to the resistance of endocrine therapies2,3. Here we demonstrate that non-coding RNAs transcribed from the AR bound-enhancers RNAs (AR-eRNAs) are upregulated in human CRPC cells in vitro, xenografts in vivo and patient tissues. Expression of a subset of genes with elevated AR-eRNAs, including TLE1 and HTR3A, is inversely correlated with biochemical recurrence-free survival of CRPC patients. We identify aan HIV-1 TAR-like (TAR-L) motif in AR-eRNAs of AR target genes including KLK3 (or PSA) and TMPRSS2. The TAR-L motif is important for these eRNAs to bind to CYCLIN T1 of the positive transcription elongation factor b (P-TEFb) complex. Knockdown of PSA eRNA diminishes RNA polymerase II (Pol II) serine-2 (Ser-2) phosphorylation at the PSA promoter. The TAR-L motif in KLK3 eRNA is crucial for effective transcription of PSA mRNA. Together, wWe demonstrate a P-TEFb activation function of eRNA and reveal aberrant eRNA expression as a functional indicator of AR abnormality in CRPC. Our results also suggest that eRNAs as amay be a potential target for CRPC therapy. Total RNA sequencing of two prostate cancer cells. Data were generated by deep sequencing, in diplicate, using Illumina HiSeq 2000.

2016-02-12 | E-GEOD-55030 | biostudies-arrayexpress

Activation of the P-TEFb Complex by Lethal Prostate Cancer-Associated Enhancer RNAs [ChIP-Seq]

Project description:Castration resistant prostate cancer (CRPC) is a lethal disease1-4. Aberrant activation of the androgen receptor (AR) becomes a central mechanism contributing to the resistance of endocrine therapies2,3. Here we demonstrate that non-coding RNAs transcribed from the AR bound-enhancers RNAs (AR-eRNAs) are upregulated in human CRPC cells in vitro, xenografts in vivo and patient tissues. Expression of a subset of genes with elevated AR-eRNAs, including TLE1 and HTR3A, is inversely correlated with biochemical recurrence-free survival of CRPC patients. We identify aan HIV-1 TAR-like (TAR-L) motif in AR-eRNAs of AR target genes including KLK3 (or PSA) and TMPRSS2. The TAR-L motif is important for these eRNAs to bind to CYCLIN T1 of the positive transcription elongation factor b (P-TEFb) complex. Knockdown of PSA eRNA diminishes RNA polymerase II (Pol II) serine-2 (Ser-2) phosphorylation at the PSA promoter. The TAR-L motif in KLK3 eRNA is crucial for effective transcription of PSA mRNA. Together, wWe demonstrate a P-TEFb activation function of eRNA and reveal aberrant eRNA expression as a functional indicator of AR abnormality in CRPC. Our results also suggest that eRNAs as amay be a potential target for CRPC therapy. Androgen receptor (AR) binding sites in human prostate cancer cell lines, LNCaP and C4-2, were studied using ChIP-seq. ChIP enriched and input DNA were sequenced using Illumina HiSeq 2000.

2016-02-12 | E-GEOD-55031 | biostudies-arrayexpress

Profiling Protein Post-Translational Modifications in Plasma-derived Extracellular Vesicles as Fingerprints for Breast Cancer Subtypes

Project description:Addressing tumor heterogeneity in breast cancer research is crucial, given the distinct subtypes like triple-negative, luminal A/B, and HER2, requiring precise differentiation for effective treatment. This study introduces a non-invasive method by analyzing post-translationally modified proteins in plasma extracellular vesicles (EVs), which play a role in immune regulation and intercellular communication. Examining modifications like phosphorylation, acetylation, and glycosylation in EVs provides insights into breast cancer dynamics. One hundred one plasma samples from luminal A/B, triple-negative breast cancer, and healthy individuals underwent discovery and validation experiments. The study identified over 28,000 unique non-modified peptides, 5,000 phosphopeptides, 680 acetyl peptides, and 1,300 glycopeptides that were successfully characterized. Bioinformatics analyses revealed significant overexpression of 815 non-modified proteins, 3,958 phosphopeptides, 352 acetyl peptides, and 895 glycopeptides in luminal A/B or triple-negative breast cancer subtypes. Phosphorylated and glycosylated PD-L1 peptides emerged as potential markers for breast cancer, regardless of subtype. Aligning findings with literature and PAM50 gene signatures highlighted markers correlated with lower survival rates. The study also conducted 123 scheduled parallel reaction monitoring (PRM) analyses, leveraging machine learning to pinpoint a panel of specific modification sites with high accuracy in subtype differentiation. This research reveals diagnostic markers and enhances understanding of the molecular landscape, contributing to more effective and personalized breast cancer diagnostics and treatments.

2025-11-01 | PXD057412 | JPOST Repository

Rev-Erbs repress macrophage gene expression by inhibiting enhancer-directed transcription

Project description:Rev-Erba and Rev-Erbb are nuclear receptors that regulate the expression of genes involved in the control of circadian rhythm, metabolism, and inflammatory responses. Rev-Erbs function as transcriptional repressors by recruiting NCoR/HDAC3 co-repressor complexes to Rev-Erb response elements in enhancers and promoters of target genes, but the molecular basis for cell-specific programs of repression is not known. Here, we present evidence that in macrophages, Rev-Erbs regulate target gene expression by inhibiting the functions of distal enhancers that are selected by macrophage lineage-determining factors, thereby establishing a macrophage-specific program of repression. Remarkably, the repressive functions of Rev-Erbs are associated with their ability to inhibit the transcription of enhancer-derived RNAs (eRNAs). Furthermore, targeted degradation of eRNAs at two enhancers subject to negative regulation by Rev-Erbs resulted in reduced expression of nearby mRNAs, implying a direct role of these eRNAs in enhancer function. By precisely defining eRNA start sites using a method that quantifies nascent 5' ends (5'-GRO-Seq), we show that transfer of full enhancer activity to a target promoter requires both the sequences mediating transcription factor binding and the specific sequences encoding the eRNA transcript. These studies provide evidence for direct roles of eRNAs in contributing to enhancer functions and suggest that Rev-Erbs act to suppress gene expression at a distance by repressing eRNA transcription. Using ChIPseq, GRO-seq, and 5'GRO-seq to determine mechanism of RevErb in transcriptional regulation in macrophages

2013-06-05 | E-GEOD-45914 | biostudies-arrayexpress

Functional Importance of eRNAs for Estrogen-dependent Gene Transcriptional Activation

Project description:The functional importance of gene enhancers in regulated gene expression is well established. In addition to widespread transcription of long non-coding RNA (ncRNA) transcripts in mammalian cells, bidirectional ncRNAs referred to as eRNAs are present on enhancers. However, it has remained unclear whether these eRNAs are functional, or merely a reflection of enhancer activation. Here, we report that 17 ?-estradiol (E2)-bound estrogen receptor alpha (ER?) on enhancers causes a global increase in eRNA transcription on enhancers adjacent to E2 upregulated coding genes. These induced eRNAs, as functional transcripts, appear to exert important roles for the observed ligand-dependent induction of target coding genes, causing an increased strength of specific enhancer:promoter looping initiated by ER? binding. Cohesin, present on many ER?-regulated enhancers even prior to ligand treatment, apparently contributes to E2-dependent gene activation by stabilizing E2/ER?/eRNA-induced enhancer:promoter looping. Our data indicate that eRNAs are likely to exert important functions in many regulated programs of gene transcription. The ChIP-seqs in this study measure the binding landscape of master transcription regulator of estrogen signaling - ER?, together with common histone marks including H3K27ac and H3K4me1 in MCF7 cells. These data serve as the basis to understand the enhancer map and subsequent analysis of eRNA expression using GRO-seq. The GRO-seq measures the trancription of nascent RNAs in the genome. From MCF7 cells treated with veichle or estrodial, we could identify estrogen-regulated eRNAs and subsequently could study their functions.

2013-06-04 | E-GEOD-45822 | biostudies-arrayexpress

Prediction of Breast Cancer Estrogen Receptor Status using Machine Learning

Project description:Gene expression profiles were generated from 199 primary breast cancer patients. Samples 1-176 were used in another study, GEO Series GSE22820, and form the training data set in this study. Sample numbers 200-222 form a validation set. This data is used to model a machine learning classifier for Estrogen Receptor Status. RNA was isolated from 199 primary breast cancer patients. A machine learning classifier was built to predict ER status using only three gene features.

2013-01-01 | E-GEOD-29210 | biostudies-arrayexpress

Assessing eRNAs associated with a cytokine-sensing mammary super-enhancer

Project description:Enhancers are transcription factor platforms that also bind RNA polymerase II and generate enhancer RNAs (eRNA). Although eRNAs have been suggested as predictors of enhancer activities and possibly key components facilitating transcription, there is little genetic evidence to support this. We address the biology of eRNAs in vivo and investigate eRNA patterns, expression levels and possible functions within a mammary-specific super-enhancer that is composed of three units with distinct transcriptional capacities. We show that eRNA levels do not correspond with the activities of their respective enhancer units. However, changes in eRNA expression upon deletion of individual enhancer units reflect the change in overall super-enhancer activity. These data provide genetic evidence that eRNA levels are not a reliable readout of individual enhancers, but they predict superenhancer activity in the absence of constituent elements.

2018-06-01 | GSE104206 | GEO

ChIP-Seq with reference exogenous genome (ChIP-Rx) of MYC in parental U2OS cells

Project description:Emerging evidence suggests that MYC binds RNAs, but its functional consequences remain unclear. Here, we integrate multi-omics data and reveal that MYC broadly binds enhancer RNAs (eRNAs), which exhibit high cancer- and tissue-specific expression in cancer cell lines and patient tumors. Moreover, we developed a computational pipeline to identify potential cis-regulatory MYC-eRNA target genes, with most predicted eRNA-target pairs supported by RNA polymerase II-mediated chromatin interaction data. Among these, we functionally characterized MERG1 as an oncogenic eRNA that promotes breast cancer tumorigenesis. Mechanistically, MERG1 interacts with MYC to enhance its occupancy at the GREB1 promoter, driving chromatin remodeling and epigenetic activation. This process specifically amplifies GREB1 expression and promotes tumor progression. Finally, nanoparticle-mediated delivery of antisense oligonucleotides targeting MERG1 suppresses MYC-mediated breast cancer growth. These results advance our understanding of the enhancer-driven regulation of gene expression and tumorigenesis and provide insights into the regulatory landscape of MYC in cancer.

2026-03-13 | GSE324781 | GEO

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