Identifying microRNA-mRNA regulatory networks in colorectal cancer by a combination of expression profile and bioinformatics analysis
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE35982 | GEO | 2013/01/03
SECONDARY ACCESSION(S): PRJNA151639
REPOSITORIES: GEO
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Project description:A multi-step approach combining microarray profile and bioinformatics analysis was adopted to identify the CRC specific miRNA-mRNA regulatory network. First, differentially expressed miRNAs and mRNAs were found out in CRC samples compared with normal epithelial tissues by miRNA and mRNA microarray respectively. Secondly the target mRNAs of dysregualted miRNA were identified by a combination of Pearson correlation coefficient between the expression level of miRNAs and mRNAs and online miRNA target predicting databases. Thirdly, the biological pathways which the miRNA-mRNA pairs involved in were identified by DAVID. Finally, some of the dysregualted miRNAs and mRNAs were validated by qRT-PCR RNA isolated from 8 colorectal cancer tissues and their corresponding adjacent normal tissues were analyzed.
2013-01-03 | E-GEOD-35982 | biostudies-arrayexpress
Project description:We conducted transcriptome and small RNA sequencing to identify differentially expressed genes (DEGs), miRNAs (DEMs), and lncRNAs (DELs). Function analysis on DEM-target genes can explain the regulatory roles of miRNAs in LC. The lncRNA-miRNA pairs, miRNA-mRNA pairs, and lncRNA-mRNA pairs were identified, which were then combined to construct the interplay of lncRNAs/miRNAs/mRNAs. And we used the online databases to verify the selected DEMs, DELs, and DEGs. Our study identified 2509 DEGs, 34 DEMs, and 432 DELs in LC patients. miRNA-mRNA pairs, including 1 miRNA (hsa-miR-21-5p) and 5 targeted genes (RECK, TIMP3, EHD1, RASGRP1 and ERG), were figured out. We finally found the hub subnetwork (LINC00632/has-miR-21-5p/TIMP3) by combining lncRNA-miRNA pairs, miRNA-mRNA pairs and lncRNA-mRNA pairs.
2022-01-27 | GSE194299 | GEO
Project description:we conducted transcriptome and small RNA sequencing to identify differentially expressed genes (DEGs), miRNAs (DEMs), and lncRNAs (DELs). Function analysis on DEM-target genes can explain the regulatory roles of miRNAs in LC. The lncRNA-miRNA pairs, miRNA-mRNA pairs, and lncRNA-mRNA pairs were identified, which were then combined to construct the interplay of lncRNAs/miRNAs/mRNAs. And we used the online databases to verify the selected DEMs, DELs, and DEGs. Our study identified 2509 DEGs, 34 DEMs, and 432 DELs in LC patients. miRNA-mRNA pairs, including 1 miRNA (hsa-miR-21-5p) and 5 targeted genes (RECK, TIMP3, EHD1, RASGRP1 and ERG), were figured out. We finally found the hub subnetwork (LINC00632/has-miR-21-5p/TIMP3) by combining lncRNA-miRNA pairs, miRNA-mRNA pairs and lncRNA-mRNA pairs.
2022-01-27 | GSE194300 | GEO
Project description:As the fetal heart develops, cardiomyocyte proliferation potential decreases while fatty acid oxidative capacity increases, a highly regulated transition known as cardiac maturation. Small noncoding RNAs, such as microRNAs (miRNAs), contribute to the establishment and control of tissue-specific transcriptional programs. However, small RNA expression dynamics and genome wide miRNA regulatory networks controlling maturation of the human fetal heart remain poorly understood. Transcriptome profiling of small RNAs revealed the temporal expression patterns of miRNA, piRNA, circRNA, snoRNA, snRNA and tRNA in the developing human heart between 8 and 19 weeks of gestation. Our analysis revealed that miRNAs were the most dynamically expressed small RNA species throughout mid-gestation. Cross-referencing differentially expressed miRNAs and mRNAs predicted 6,200 mRNA targets, 2134 of which were upregulated and 4066 downregulated as gestation progresses. Moreover, we found that downregulated targets of upregulated miRNAs predominantly control cell cycle progression, while upregulated targets of downregulated miRNAs are linked to energy sensing and oxidative metabolism. Furthermore, integration of miRNA and mRNA profiles with proteomes and reporter metabolites revealed that proteins encoded in mRNA targets, and their associated metabolites, mediate fatty acid oxidation and are enriched as the heart develops.This study revealed the small RNAome of the maturing human fetal heart. Furthermore, our findings suggest that coordinated activation and repression of miRNA expression throughout mid-gestation is essential to establish a dynamic miRNA-mRNA-protein network that decreases cardiomyocyte proliferation potential while increasing the oxidative capacity of the maturing human fetal heart.
2024-01-26 | PXD044936 | Pride
Project description:Abstract Background: Cerebral ischemia-reperfusion (I/R) frequently caused the late-onset neuronal damage. Breviscapine has a promote in autophagy of microvascular endothelial cells in I/R and it can inhibit the oxidative damage and apoptosis. However, the mediation mechanism of breviscapine on neuronal cell death is unclear. Methods: Firstly, transcriptome sequencing was performed on three groups of mice neuronal normal group (Control group), oxygen-glucose deprivation/reoxygenation group (OGD/R group) and breviscapine administration group (Therapy group). Differentially expressed genes (DEGs) between OGD/R and Control groups, and between Therapy and OGD/R groups were obtained by limma package. The N6-methyladenosine (m6A) methylation related DEGs were selected out by the Pearson correlation analysis. Then, prediction and confirmation of drug targets were performed by Swiss Target Prediction and UniProt Knowledgebase (UniProtKB) datebase, and key genes were obtained by Pearson correlation analysis between m6A-related DEGs and drug target genes. Next, gene set enrichment (GSEA) analysis and Ingenuity pathway analysis (IPA) were used to obtain the pathways of key genes. Finally, a circRNA-miRNA-mRNA network was constructed based on the mRNAs, circRNAs and miRNAs. Results: 2250 DEGs between OGD/R and Control groups and 757 DEGs between Therapy and OGD/R groups were selected out by differential analysis. A total of 7 m6A related DEGs including Arl4d, Gm10653, Gm1113, Kcns3, Olfml2a, Stk26 and Tfcp2l1 were obtained by Pearson correlation analysis. Four key genes (Tfcp2l1, Kcns3, Olfml2a and Arl4d) were acquired, and GSEA showed that these key genes were significantly participated in DNA repair, e2f targets and g2m checkpoint. IPA revealed that Tfcp2l1 played a significant role in human embryonic stem cell pluripotency. The circRNA-miRNA-mRNA network showed that the mmu_circ_0001258 regulated the Tfcp2l1 by mmu-miR-301b-3p. Conclusions: In conclusion, four key genes including Tfcp2l1, Kcns3, Olfml2a and Arl4d significantly associated with the treatment of OGD/R by breviscapine were acquired, which would provide a theoretical basis for clinical trials.
2024-05-17 | E-MTAB-13017 | biostudies-arrayexpress
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 (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
Project description:Proctor2017 - Identifying microRNA for muscle
regeneration during ageing (Mir143_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:
MODEL1704110003.
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 | MODEL1704110003 | 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 (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