Ontology highlight
ABSTRACT:
PROVIDER: PRJNA258590 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:The Assembly of miRNA-mRNA-protein Regulatory Networks Using High-throughput Expression Data (miRNA)
| PRJNA259359 | ENA
Project description:The Assembly of miRNA-mRNA-protein Regulatory Networks Using High-throughput Expression Data (mRNA)
| PRJNA259358 | ENA
Project description:Chronic high-altitude hypoxia causes significant ovarian tissue damage. This study investigates the underlying mechanisms of ovarian injury in mice exposed to hypoxia, aiming to guide targeted therapy development. Mice were divided into either normal pressure (MP, n=15) or hypobaric hypoxia (PU, n=15) and samples from both groups were analyzed using whole-transcriptome sequencing data. Hub genes were identified through differential expression analysis and protein-protein interaction (PPI) network construction before their expression patterns were examined. Subsequently, functional enrichment was conducted. Key miRNAs, lncRNAs, circRNAs, and transcription factors (TFs) were ascertained. Finally, RT-qPCR and Western blotting (WB) were performed. Hmgcr, Ptgs2, and Mmp3 were identified as hub genes, all showing pronounced lower expression in PU samples. RT-qPCR analysis confirmed the downregulation of these genes, with WB analysis further demonstrating a significant reduction in their protein levels in PU samples. These hub genes were predominantly enriched in "oxidative phosphorylation" pathway. Furthermore, lncRNA-miRNA-mRNA, circRNA-miRNA-mRNA, and TF-mRNA regulatory networks were constructed. These networks highlighted key regulatory molecules, including the miRNA mmu-miR-144-3p, 2 lncRNAs (Miat and Neat1), 8 circRNAs (eg. novel_circ_041272-mu-miR-144-3p-Ptgs2), and 1 TF (Etv4). These results provide key insights for targeted ovarian injury therapies under low-pressure hypoxia.
2025-08-11 | GSE305161 | GEO
Project description:Chronic high-altitude hypoxia causes significant ovarian tissue damage. This study investigates the underlying mechanisms of ovarian injury in mice exposed to hypoxia, aiming to guide targeted therapy development. Mice were divided into either normal pressure (MP, n=15) or hypobaric hypoxia (PU, n=15) and samples from both groups were analyzed using whole-transcriptome sequencing data. Hub genes were identified through differential expression analysis and protein-protein interaction (PPI) network construction before their expression patterns were examined. Subsequently, functional enrichment was conducted. Key miRNAs, lncRNAs, circRNAs, and transcription factors (TFs) were ascertained. Finally, RT-qPCR and Western blotting (WB) were performed. Hmgcr, Ptgs2, and Mmp3 were identified as hub genes, all showing pronounced lower expression in PU samples. RT-qPCR analysis confirmed the downregulation of these genes, with WB analysis further demonstrating a significant reduction in their protein levels in PU samples. These hub genes were predominantly enriched in "oxidative phosphorylation" pathway. Furthermore, lncRNA-miRNA-mRNA, circRNA-miRNA-mRNA, and TF-mRNA regulatory networks were constructed. These networks highlighted key regulatory molecules, including the miRNA mmu-miR-144-3p, 2 lncRNAs (Miat and Neat1), 8 circRNAs (eg. novel_circ_041272-mu-miR-144-3p-Ptgs2), and 1 TF (Etv4). These results provide key insights for targeted ovarian injury therapies under low-pressure hypoxia.
2025-07-29 | GSE303173 | GEO
Project description:Chronic high-altitude hypoxia causes significant ovarian tissue damage. This study investigates the underlying mechanisms of ovarian injury in mice exposed to hypoxia, aiming to guide targeted therapy development. Mice were divided into either normal pressure (MP, n=15) or hypobaric hypoxia (PU, n=15) and samples from both groups were analyzed using whole-transcriptome sequencing data. Hub genes were identified through differential expression analysis and protein-protein interaction (PPI) network construction before their expression patterns were examined. Subsequently, functional enrichment was conducted. Key miRNAs, lncRNAs, circRNAs, and transcription factors (TFs) were ascertained. Finally, RT-qPCR and Western blotting (WB) were performed. Hmgcr, Ptgs2, and Mmp3 were identified as hub genes, all showing pronounced lower expression in PU samples. RT-qPCR analysis confirmed the downregulation of these genes, with WB analysis further demonstrating a significant reduction in their protein levels in PU samples. These hub genes were predominantly enriched in "oxidative phosphorylation" pathway. Furthermore, lncRNA-miRNA-mRNA, circRNA-miRNA-mRNA, and TF-mRNA regulatory networks were constructed. These networks highlighted key regulatory molecules, including the miRNA mmu-miR-144-3p, 2 lncRNAs (Miat and Neat1), 8 circRNAs (eg. novel_circ_041272-mu-miR-144-3p-Ptgs2), and 1 TF (Etv4). These results provide key insights for targeted ovarian injury therapies under low-pressure hypoxia.
2025-07-29 | GSE303172 | GEO
Project description:Model destription:
The model describes the stochastic dynamics of two variables, protein and mRNA of a gene with constitutive expression.
Publication:
A New Efficient Approach to Fit Stochastic Models on the Basis of High-throughput Experimental Data Using a Model of IRF7 Gene Expression as Case Study
Authors
Luis U. Aguilera, Christoph Zimmer and Ursula Kummer.
2017-03-17 | MODEL1608100000 | BioModels
Project description:The experiment aims to identify regulatory miRNA networks influencing mRNA profiles in oral lichen planus (OLP). RNA and miRNA were extracted simultaniously using miRVana (Ambion, Life Technologies). Sample and array processing was carried out according to the manufacturer's guidelines. Affymetrix raw data was processed using AGCC Expression Console 1.1 (Affymetrix), employing RMA normalization. Linking miRNA and mRNA was performed with a correlation analysis, while a false discovery rate was used to exclude false-positive correlations between miRNAs and their predicted targets.
2013-05-06 | GSE38616 | GEO
Project description:The experiment aims to identify regulatory miRNA networks influencing mRNA profiles in oral lichen planus (OLP). RNA and miRNA were extracted simultaniously using miRVana (Ambion, Life Technologies). Sample and array processing was carried out according to the manufacturer's guidelines. Affymetrix raw data was processed using AGCC Expression Console 1.1 (Affymetrix), employing RMA normalization. Linking miRNA and mRNA was performed with a correlation analysis, while a false discovery rate was used to exclude false-positive correlations between miRNAs and their predicted targets.
2013-05-06 | GSE38615 | GEO
Project description:The experiment aims to identify regulatory miRNA networks influencing mRNA profiles in oral lichen planus (OLP). RNA and miRNA were extracted simultaniously using miRVana (Ambion, Life Technologies). Sample and array processing was carried out according to the manufacturer's guidelines. Affymetrix raw data was processed using AGCC Expression Console 1.1 (Affymetrix), employing RMA normalization. Linking miRNA and mRNA was performed with a correlation analysis, while a false discovery rate was used to exclude false-positive correlations between miRNAs and their predicted targets. 7 cases (OLP) and 7 controls (healthy individuals). Genome wide mRNA and miRNA screening, linking both datasets (see summary).
2013-05-06 | E-GEOD-38615 | biostudies-arrayexpress
Project description:The experiment aims to identify regulatory miRNA networks influencing mRNA profiles in oral lichen planus (OLP). RNA and miRNA were extracted simultaneously using miRVana (Ambion, Life Technologies). Sample and array processing was carried out according to the manufacturer's guidelines. Affymetrix raw data was processed using AGCC Expression Console 1.1 (Affymetrix), employing RMA normalization. Linking miRNA and mRNA was performed with a correlation analysis, while a false discovery rate was used to exclude false-positive correlations between miRNAs and their predicted targets. 7 cases (OLP) and 7 controls (healthy individuals). Genome wide mRNA and miRNA screening, linking both datasets (see summary).
2013-05-06 | E-GEOD-38616 | biostudies-arrayexpress