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Modular network inference between miRNA–mRNA expression profiles using weighted co-expression network analysis

Connecting transcriptional and post-transcriptional regulatory networks solves an important puzzle in the elucidation of gene regulatory mechanisms. To decipher the complexity of these connections, we build co-expression network modules for mRNA as well as miRNA expression profiles of breast cancer...

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Detalles Bibliográficos
Autores principales: Wani, Nisar, Barh, Debmalya, Raza, Khalid
Formato: Online Artículo Texto
Lenguaje:English
Publicado: De Gruyter 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8709739/
https://www.ncbi.nlm.nih.gov/pubmed/34800012
http://dx.doi.org/10.1515/jib-2021-0029
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author Wani, Nisar
Barh, Debmalya
Raza, Khalid
author_facet Wani, Nisar
Barh, Debmalya
Raza, Khalid
author_sort Wani, Nisar
collection PubMed
description Connecting transcriptional and post-transcriptional regulatory networks solves an important puzzle in the elucidation of gene regulatory mechanisms. To decipher the complexity of these connections, we build co-expression network modules for mRNA as well as miRNA expression profiles of breast cancer data. We construct gene and miRNA co-expression modules using the weighted gene co-expression network analysis (WGCNA) method and establish the significance of these modules (Genes/miRNAs) for cancer phenotype. This work also infers an interaction network between the genes of the turquoise module from mRNA expression data and hubs of the turquoise module from miRNA expression data. A pathway enrichment analysis using a miRsystem web tool for miRNA hubs and some of their targets, reveal their enrichment in several important pathways associated with the progression of cancer.
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spelling pubmed-87097392022-01-20 Modular network inference between miRNA–mRNA expression profiles using weighted co-expression network analysis Wani, Nisar Barh, Debmalya Raza, Khalid J Integr Bioinform Article Connecting transcriptional and post-transcriptional regulatory networks solves an important puzzle in the elucidation of gene regulatory mechanisms. To decipher the complexity of these connections, we build co-expression network modules for mRNA as well as miRNA expression profiles of breast cancer data. We construct gene and miRNA co-expression modules using the weighted gene co-expression network analysis (WGCNA) method and establish the significance of these modules (Genes/miRNAs) for cancer phenotype. This work also infers an interaction network between the genes of the turquoise module from mRNA expression data and hubs of the turquoise module from miRNA expression data. A pathway enrichment analysis using a miRsystem web tool for miRNA hubs and some of their targets, reveal their enrichment in several important pathways associated with the progression of cancer. De Gruyter 2021-11-22 /pmc/articles/PMC8709739/ /pubmed/34800012 http://dx.doi.org/10.1515/jib-2021-0029 Text en © 2021 Nisar Wani et al., published by De Gruyter, Berlin/Boston https://creativecommons.org/licenses/by/4.0/This work is licensed under the Creative Commons Attribution 4.0 International License.
spellingShingle Article
Wani, Nisar
Barh, Debmalya
Raza, Khalid
Modular network inference between miRNA–mRNA expression profiles using weighted co-expression network analysis
title Modular network inference between miRNA–mRNA expression profiles using weighted co-expression network analysis
title_full Modular network inference between miRNA–mRNA expression profiles using weighted co-expression network analysis
title_fullStr Modular network inference between miRNA–mRNA expression profiles using weighted co-expression network analysis
title_full_unstemmed Modular network inference between miRNA–mRNA expression profiles using weighted co-expression network analysis
title_short Modular network inference between miRNA–mRNA expression profiles using weighted co-expression network analysis
title_sort modular network inference between mirna–mrna expression profiles using weighted co-expression network analysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8709739/
https://www.ncbi.nlm.nih.gov/pubmed/34800012
http://dx.doi.org/10.1515/jib-2021-0029
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