Expression of microRNAs and their gene targets are dysregulated in pre-invasive breast cancer (mRNA)
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE24506 | GEO | 2011/02/17
SECONDARY ACCESSION(S): PRJNA133519
REPOSITORIES: GEO
Similar Datasets
Project description:Introduction: microRNAs (miRNAs) are short non-coding RNAs that negatively regulate gene expression and may play a causal role in invasive breast cancer. Since many genetic aberrations of invasive disease are detectable in earlier stages, we hypothesized that miRNA expression dysregulation and the predicted changes in gene expression would also be found in early breast neoplasias. Methods: Expression profiling of 365 miRNAs by RT-qPCR was combined with laser-capture microdissection to obtain an epithelial specific miRNA expression signature of normal breast epithelium (n=9) and of paired samples of histologically normal epithelium (HN) and ductal carcinoma in situ (DCIS) (n=16). To determine how miRNAs may control the expression of co-dysregulated mRNAs we also performed gene expression microarray analysis in the same paired HN and DCIS samples and integrated this with miRNA-target prediction. We further validated several target pairs by modulating the expression levels of miRNAs in MCF7 cells and measured the expression of target mRNAs and proteins. Results: Thirty-five miRNAs were aberrantly expressed between RM, HN and DCIS. Twenty-nine miRNAs and 420 mRNAs were aberrantly expressed between HN and DCIS. Combining these two datasets with miRNA-target prediction we identified two established target pairs (miR-195:CCND1 and miR-21:NFIB) and tested several novel miRNA:mRNA target pairs. Over-expression of the putative tumor-suppressor miR-125b, under-expressed in DCIS, repressed the expression of MEMO1, which is required for ErbB2-driven cell motility (also a target of miR-125b); and NRIP1/RIP140, which modulates the transcriptional activity of the estrogen receptor. Knockdown of the putative oncogenic miRNAs miR-182 and miR-183, both highly over-expressed in DCIS, increased the expression of CBX7 (which regulates E-cadherin expression), DOK4, NMT2, and EGR1. Augmentation of CBX7 by knockdown of miR-182 expression, in turn, positively regulated the expression of E-cadherin, a key protein involved in maintaining normal epithelial cell morphology which is commonly lost during neoplastic progression. Conclusions: These data provide the first miRNA expression profile of normal breast epithelium and of pre-invasive breast carcinoma. Further, we demonstrate that altered miRNA expression can modulate gene expression changes that characterize these early cancers. We conclude that miRNA dysregulation likely plays a substantial role in early breast cancer development. Two total samples were analyzed via Affymetrix U133A. Case numbers correspond to individual patients. Each sample is identified by case number, histologic lesion and corresponding microarray ID.
2011-02-17 | E-GEOD-24506 | biostudies-arrayexpress
Project description:Introduction: microRNAs (miRNAs) are short non-coding RNAs that negatively regulate gene expression and may play a causal role in invasive breast cancer. Since many genetic aberrations of invasive disease are detectable in earlier stages, we hypothesized that miRNA expression dysregulation and the predicted changes in gene expression would also be found in early breast neoplasias. Methods: Expression profiling of 365 miRNAs by RT-qPCR was combined with laser-capture microdissection to obtain an epithelial specific miRNA expression signature of normal breast epithelium (n=9) and of paired samples of histologically normal epithelium (HN) and ductal carcinoma in situ (DCIS) (n=16). To determine how miRNAs may control the expression of co-dysregulated mRNAs we also performed gene expression microarray analysis in the same paired HN and DCIS samples and integrated this with miRNA-target prediction. We further validated several target pairs by modulating the expression levels of miRNAs in MCF7 cells and measured the expression of target mRNAs and proteins. Results: Thirty-five miRNAs were aberrantly expressed between RM, HN and DCIS. Twenty-nine miRNAs and 420 mRNAs were aberrantly expressed between HN and DCIS. Combining these two datasets with miRNA-target prediction we identified two established target pairs (miR-195:CCND1 and miR-21:NFIB) and tested several novel miRNA:mRNA target pairs. Over-expression of the putative tumor-suppressor miR-125b, under-expressed in DCIS, repressed the expression of MEMO1, which is required for ErbB2-driven cell motility (also a target of miR-125b); and NRIP1/RIP140, which modulates the transcriptional activity of the estrogen receptor. Knockdown of the putative oncogenic miRNAs miR-182 and miR-183, both highly over-expressed in DCIS, increased the expression of CBX7 (which regulates E-cadherin expression), DOK4, NMT2, and EGR1. Augmentation of CBX7 by knockdown of miR-182 expression, in turn, positively regulated the expression of E-cadherin, a key protein involved in maintaining normal epithelial cell morphology which is commonly lost during neoplastic progression. Conclusions: These data provide the first miRNA expression profile of normal breast epithelium and of pre-invasive breast carcinoma. Further, we demonstrate that altered miRNA expression can modulate gene expression changes that characterize these early cancers. We conclude that miRNA dysregulation likely plays a substantial role in early breast cancer development.
2011-02-17 | GSE24508 | GEO
Project description:Introduction: microRNAs (miRNAs) are short non-coding RNAs that negatively regulate gene expression and may play a causal role in invasive breast cancer. Since many genetic aberrations of invasive disease are detectable in earlier stages, we hypothesized that miRNA expression dysregulation and the predicted changes in gene expression would also be found in early breast neoplasias. Methods: Expression profiling of 365 miRNAs by RT-qPCR was combined with laser-capture microdissection to obtain an epithelial specific miRNA expression signature of normal breast epithelium (n=9) and of paired samples of histologically normal epithelium (HN) and ductal carcinoma in situ (DCIS) (n=16). To determine how miRNAs may control the expression of co-dysregulated mRNAs we also performed gene expression microarray analysis in the same paired HN and DCIS samples and integrated this with miRNA-target prediction. We further validated several target pairs by modulating the expression levels of miRNAs in MCF7 cells and measured the expression of target mRNAs and proteins. Results: Thirty-five miRNAs were aberrantly expressed between RM, HN and DCIS. Twenty-nine miRNAs and 420 mRNAs were aberrantly expressed between HN and DCIS. Combining these two datasets with miRNA-target prediction we identified two established target pairs (miR-195:CCND1 and miR-21:NFIB) and tested several novel miRNA:mRNA target pairs. Over-expression of the putative tumor-suppressor miR-125b, under-expressed in DCIS, repressed the expression of MEMO1, which is required for ErbB2-driven cell motility (also a target of miR-125b); and NRIP1/RIP140, which modulates the transcriptional activity of the estrogen receptor. Knockdown of the putative oncogenic miRNAs miR-182 and miR-183, both highly over-expressed in DCIS, increased the expression of CBX7 (which regulates E-cadherin expression), DOK4, NMT2, and EGR1. Augmentation of CBX7 by knockdown of miR-182 expression, in turn, positively regulated the expression of E-cadherin, a key protein involved in maintaining normal epithelial cell morphology which is commonly lost during neoplastic progression. Conclusions: These data provide the first miRNA expression profile of normal breast epithelium and of pre-invasive breast carcinoma. Further, we demonstrate that altered miRNA expression can modulate gene expression changes that characterize these early cancers. We conclude that miRNA dysregulation likely plays a substantial role in early breast cancer development. The expression of 365 microRNAs were measured in 19 total samples via multiplex reverse transcription PCR using the TaqMan Human MicroRNA Low Density Array. Patients age ranged from 42-75. Equal amounts of total RNA from 9 reduction mammoplasty samples (age range 42-75) were combined into a pooled RM control (PRM), this sample was run in triplicate. Remaining 16 samples consist of matched samples of ductal carcinoma in situ and adjacent histologically normal breast epithelium, these are identified by case number and histologic lesion.
2011-02-17 | E-GEOD-24508 | biostudies-arrayexpress
Project description:Schmitz2014 - RNA triplex formation
The model is parameterized using the
parameters for gene CCDC3 from Supplementary Table S1. The two
miRNAs which form the triplex together with CCDC3 are miR-551b and
miR-138.
This model is described in the article:
Cooperative gene regulation
by microRNA pairs and their identification using a
computational workflow.
Schmitz U, Lai X, Winter F,
Wolkenhauer O, Vera J, Gupta SK.
Nucleic Acids Res. 2014 Jul; 42(12):
7539-7552
Abstract:
MicroRNAs (miRNAs) are an integral part of gene regulation
at the post-transcriptional level. Recently, it has been shown
that pairs of miRNAs can repress the translation of a target
mRNA in a cooperative manner, which leads to an enhanced
effectiveness and specificity in target repression. However, it
remains unclear which miRNA pairs can synergize and which genes
are target of cooperative miRNA regulation. In this paper, we
present a computational workflow for the prediction and
analysis of cooperating miRNAs and their mutual target genes,
which we refer to as RNA triplexes. The workflow integrates
methods of miRNA target prediction; triplex structure analysis;
molecular dynamics simulations and mathematical modeling for a
reliable prediction of functional RNA triplexes and target
repression efficiency. In a case study we analyzed the human
genome and identified several thousand targets of cooperative
gene regulation. Our results suggest that miRNA cooperativity
is a frequent mechanism for an enhanced target repression by
pairs of miRNAs facilitating distinctive and fine-tuned target
gene expression patterns. Human RNA triplexes predicted and
characterized in this study are organized in a web resource at
www.sbi.uni-rostock.de/triplexrna/.
This model is hosted on
BioModels Database
and identified by:
BIOMD0000000530.
To cite BioModels Database, please use:
BioModels Database:
An enhanced, curated and annotated resource for published
quantitative kinetic models.
To the extent possible under law, all copyright and related or
neighbouring rights to this encoded model have been dedicated to
the public domain worldwide. Please refer to
CC0
Public Domain Dedication for more information.
2024-09-02 | BIOMD0000000530 | BioModels
Project description:Proliferative breast lesions, such as simple ductal hyperplasia (SH) and atypical ductal hyperplasia (ADH), are candidate precursors to ductal carcinoma in situ (DCIS) and invasive cancer. To better understand their relationship to more advanced disease, we used microdissection and DNA microarrays to profile the gene expression of patient-matched histologically normal (HN), ADH, and DCIS from 12 patients with ER+ sporadic breast cancer. SH were profiled from a subset of cases. We found 837 differentially expressed genes between DCIS-HN and 447 between ADH-HN, with >90% of the ADH-HN genes also present among the DCIS-HN genes. Only 61 genes were identified between ADH-DCIS. Expression differences were reproduced in an independent cohort of patient-matched lesions by qRT-PCR. Many breast cancer-related genes and pathways were dysregulated in ADH and maintained in DCIS. Particularly, cell adhesion and extracellular matrix (ECM) interactions were overrepresented. Focal adhesion was the top pathway in each gene set. We conclude that ADH and DCIS share highly similar gene expression and are distinct from HN. In contrast, SH appear more similar to HN. These data provide genetic evidence that ADH (but not SH) are often precursors to cancer and suggest cancer-related genetic changes, particularly adhesion and ECM pathways, are dysregulated prior to invasion and even before malignancy is apparent. These findings could lead to novel risk stratification, prevention, and treatment approaches. Patient-matched (HN, SH, ADH, DCIS) samples were isolated from within patients with ER+ sporadic breast cancers via laser capture microdissection. Use of patient-matched samples decreases between patient variations.
2009-09-08 | GSE16873 | GEO
Project description:Proliferative breast lesions, such as simple ductal hyperplasia (SH) and atypical ductal hyperplasia (ADH), are candidate precursors to ductal carcinoma in situ (DCIS) and invasive cancer. To better understand their relationship to more advanced disease, we used microdissection and DNA microarrays to profile the gene expression of patient-matched histologically normal (HN), ADH, and DCIS from 12 patients with ER+ sporadic breast cancer. SH were profiled from a subset of cases. We found 837 differentially expressed genes between DCIS-HN and 447 between ADH-HN, with >90% of the ADH-HN genes also present among the DCIS-HN genes. Only 61 genes were identified between ADH-DCIS. Expression differences were reproduced in an independent cohort of patient-matched lesions by qRT-PCR. Many breast cancer-related genes and pathways were dysregulated in ADH and maintained in DCIS. Particularly, cell adhesion and extracellular matrix (ECM) interactions were overrepresented. Focal adhesion was the top pathway in each gene set. We conclude that ADH and DCIS share highly similar gene expression and are distinct from HN. In contrast, SH appear more similar to HN. These data provide genetic evidence that ADH (but not SH) are often precursors to cancer and suggest cancer-related genetic changes, particularly adhesion and ECM pathways, are dysregulated prior to invasion and even before malignancy is apparent. These findings could lead to novel risk stratification, prevention, and treatment approaches. Patient-matched (HN, SH, ADH, DCIS) samples were isolated from within patients with ER+ sporadic breast cancers via laser capture microdissection. Use of patient-matched samples decreases between patient variations. Forty total samples were analyzed via Affymetrix U133A. Patient age ranged from 48-92. Case numbers correspond to individual patients. Each sample is identified by case number, histologic lesion and corresponding microarray ID.
2009-09-08 | E-GEOD-16873 | biostudies-arrayexpress
Project description:Proctor2017 - Identifying microRNA for muscle
regeneration during ageing (Mirs_in_muscle)
This model is described in the article:
Using computer simulation
models to investigate the most promising microRNAs to improve
muscle regeneration during ageing
Carole J. Proctor & Katarzyna
Goljanek-Whysall
Nature Scientific Reports
Abstract:
MicroRNAs (miRNAs) regulate gene expression through
interactions with target sites within mRNAs, leading to
enhanced degradation of the mRNA or inhibition of translation.
Skeletal muscle expresses many different miRNAs with important
roles in adulthood myogenesis (regeneration) and myofibre
hypertrophy and atrophy, processes associated with muscle
ageing. However, the large number of miRNAs and their targets
mean that a complex network of pathways exists, making it
difficult to predict the effect of selected miRNAs on
age-related muscle wasting. Computational modelling has the
potential to aid this process as it is possible to combine
models of individual miRNA:target interactions to form an
integrated network. As yet, no models of these interactions in
muscle exist. We created the first model of miRNA:target
interactions in myogenesis based on experimental evidence of
individual miRNAs which were next validated and used to make
testable predictions. Our model confirms that miRNAs regulate
key interactions during myogenesis and can act by promoting the
switch between quiescent/proliferating/differentiating
myoblasts and by maintaining the differentiation process. We
propose that a threshold level of miR-1 acts in the initial
switch to differentiation, with miR-181 keeping the switch on
and miR-378 maintaining the differentiation and miR-143
inhibiting myogenesis.
This model is hosted on
BioModels Database
and identified by:
MODEL1704110004.
To cite BioModels Database, please use:
Chelliah V et al. BioModels: ten-year
anniversary. Nucl. Acids Res. 2015, 43(Database
issue):D542-8.
To the extent possible under law, all copyright and related or
neighbouring rights to this encoded model have been dedicated to
the public domain worldwide. Please refer to
CC0
Public Domain Dedication for more information.
2017-09-26 | MODEL1704110004 | BioModels
Project description:Proctor2017 - Identifying microRNA for muscle regeneration during ageing (Mir1_in_muscle)
This model is described in the article:
Using computer simulation
models to investigate the most promising microRNAs to improve
muscle regeneration during ageing
Carole J. Proctor & Katarzyna
Goljanek-Whysall
Nature Scientific Reports
Abstract:
MicroRNAs (miRNAs) regulate gene expression through
interactions with target sites within mRNAs, leading to
enhanced degradation of the mRNA or inhibition of translation.
Skeletal muscle expresses many different miRNAs with important
roles in adulthood myogenesis (regeneration) and myofibre
hypertrophy and atrophy, processes associated with muscle
ageing. However, the large number of miRNAs and their targets
mean that a complex network of pathways exists, making it
difficult to predict the effect of selected miRNAs on
age-related muscle wasting. Computational modelling has the
potential to aid this process as it is possible to combine
models of individual miRNA:target interactions to form an
integrated network. As yet, no models of these interactions in
muscle exist. We created the first model of miRNA:target
interactions in myogenesis based on experimental evidence of
individual miRNAs which were next validated and used to make
testable predictions. Our model confirms that miRNAs regulate
key interactions during myogenesis and can act by promoting the
switch between quiescent/proliferating/differentiating
myoblasts and by maintaining the differentiation process. We
propose that a threshold level of miR-1 acts in the initial
switch to differentiation, with miR-181 keeping the switch on
and miR-378 maintaining the differentiation and miR-143
inhibiting myogenesis.
This model is hosted on
BioModels Database
and identified by:
MODEL1704110000.
To cite BioModels Database, please use:
Chelliah V et al. BioModels: ten-year
anniversary. Nucl. Acids Res. 2015, 43(Database
issue):D542-8.
To the extent possible under law, all copyright and related or
neighbouring rights to this encoded model have been dedicated to
the public domain worldwide. Please refer to
CC0
Public Domain Dedication for more information.
2017-09-26 | MODEL1704110000 | BioModels
Project description:Proctor2017 - Identifying microRNA for muscle regeneration during ageing (Mir378_in_muscle)
This model is described in the article:
Using computer simulation
models to investigate the most promising microRNAs to improve
muscle regeneration during ageing
Carole J. Proctor & Katarzyna
Goljanek-Whysall
Scientific Reports
Abstract:
MicroRNAs (miRNAs) regulate gene expression through
interactions with target sites within mRNAs, leading to
enhanced degradation of the mRNA or inhibition of translation.
Skeletal muscle expresses many different miRNAs with important
roles in adulthood myogenesis (regeneration) and myofibre
hypertrophy and atrophy, processes associated with muscle
ageing. However, the large number of miRNAs and their targets
mean that a complex network of pathways exists, making it
difficult to predict the effect of selected miRNAs on
age-related muscle wasting. Computational modelling has the
potential to aid this process as it is possible to combine
models of individual miRNA:target interactions to form an
integrated network. As yet, no models of these interactions in
muscle exist. We created the first model of miRNA:target
interactions in myogenesis based on experimental evidence of
individual miRNAs which were next validated and used to make
testable predictions. Our model confirms that miRNAs regulate
key interactions during myogenesis and can act by promoting the
switch between quiescent/proliferating/differentiating
myoblasts and by maintaining the differentiation process. We
propose that a threshold level of miR-1 acts in the initial
switch to differentiation, with miR-181 keeping the switch on
and miR-378 maintaining the differentiation and miR-143
inhibiting myogenesis.
This model is hosted on
BioModels Database
and identified by:
MODEL1704110002.
To cite BioModels Database, please use:
Chelliah V et al. BioModels: ten-year
anniversary. Nucl. Acids Res. 2015, 43(Database
issue):D542-8.
To the extent possible under law, all copyright and related or
neighbouring rights to this encoded model have been dedicated to
the public domain worldwide. Please refer to
CC0
Public Domain Dedication for more information.
2017-09-26 | MODEL1704110002 | BioModels
Project description:Proctor2017 - Identifying microRNA for muscle
regeneration during ageing (Mir181_in_muscle)
This model is described in the article:
Using computer simulation
models to investigate the most promising microRNAs to improve
muscle regeneration during ageing
Carole J. Proctor & Katarzyna
Goljanek-Whysall
Scientific Reports
Abstract:
MicroRNAs (miRNAs) regulate gene expression through
interactions with target sites within mRNAs, leading to
enhanced degradation of the mRNA or inhibition of translation.
Skeletal muscle expresses many different miRNAs with important
roles in adulthood myogenesis (regeneration) and myofibre
hypertrophy and atrophy, processes associated with muscle
ageing. However, the large number of miRNAs and their targets
mean that a complex network of pathways exists, making it
difficult to predict the effect of selected miRNAs on
age-related muscle wasting. Computational modelling has the
potential to aid this process as it is possible to combine
models of individual miRNA:target interactions to form an
integrated network. As yet, no models of these interactions in
muscle exist. We created the first model of miRNA:target
interactions in myogenesis based on experimental evidence of
individual miRNAs which were next validated and used to make
testable predictions. Our model confirms that miRNAs regulate
key interactions during myogenesis and can act by promoting the
switch between quiescent/proliferating/differentiating
myoblasts and by maintaining the differentiation process. We
propose that a threshold level of miR-1 acts in the initial
switch to differentiation, with miR-181 keeping the switch on
and miR-378 maintaining the differentiation and miR-143
inhibiting myogenesis.
This model is hosted on
BioModels Database
and identified by:
MODEL1704110001.
To cite BioModels Database, please use:
Chelliah V et al. BioModels: ten-year
anniversary. Nucl. Acids Res. 2015, 43(Database
issue):D542-8.
To the extent possible under law, all copyright and related or
neighbouring rights to this encoded model have been dedicated to
the public domain worldwide. Please refer to
CC0
Public Domain Dedication for more information.
2017-09-26 | MODEL1704110001 | BioModels