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Multidimensional computational study to understand non-coding RNA interactions in breast cancer metastasis

Metastasis is a major breast cancer hallmark due to which tumor cells tend to relocate to regional or distant organs from their organ of origin. This study is aimed to decipher the interaction among 113 differentially expressed genes, interacting non-coding RNAs and drugs (614 miRNAs, 220 lncRNAs an...

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Autores principales: Chakraborty, Sohini, Banerjee, Satarupa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10516999/
https://www.ncbi.nlm.nih.gov/pubmed/37737288
http://dx.doi.org/10.1038/s41598-023-42904-6
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author Chakraborty, Sohini
Banerjee, Satarupa
author_facet Chakraborty, Sohini
Banerjee, Satarupa
author_sort Chakraborty, Sohini
collection PubMed
description Metastasis is a major breast cancer hallmark due to which tumor cells tend to relocate to regional or distant organs from their organ of origin. This study is aimed to decipher the interaction among 113 differentially expressed genes, interacting non-coding RNAs and drugs (614 miRNAs, 220 lncRNAs and 3241 interacting drugs) associated with metastasis in breast cancer. For an extensive understanding of genetic interactions in the diseased state, a backbone gene co-expression network was constructed. Further, the mRNA–miRNA–lncRNA–drug interaction network was constructed to identify the top hub RNAs, significant cliques and topological parameters associated with differentially expressed genes. Then, the mRNAs from the top two subnetworks constructed are considered for transcription factor (TF) analysis. 39 interacting miRNAs and 1641 corresponding TFs for the eight mRNAs from the subnetworks are also utilized to construct an mRNA–miRNA–TF interaction network. TF analysis revealed two TFs (EST1 and SP1) from the cliques to be significant. TCGA expression analysis of miRNAs and lncRNAs as well as subclass-based and promoter methylation-based expression, oncoprint and survival analysis of the mRNAs are also done. Finally, functional enrichment of mRNAs is also performed. Significant cliques identified in the study can be utilized for identification of newer therapeutic interventions for breast cancer. This work will also help to gain a deeper insight into the complicated molecular intricacies to reveal the potential biomarkers involved with breast cancer progression in future.
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spelling pubmed-105169992023-09-24 Multidimensional computational study to understand non-coding RNA interactions in breast cancer metastasis Chakraborty, Sohini Banerjee, Satarupa Sci Rep Article Metastasis is a major breast cancer hallmark due to which tumor cells tend to relocate to regional or distant organs from their organ of origin. This study is aimed to decipher the interaction among 113 differentially expressed genes, interacting non-coding RNAs and drugs (614 miRNAs, 220 lncRNAs and 3241 interacting drugs) associated with metastasis in breast cancer. For an extensive understanding of genetic interactions in the diseased state, a backbone gene co-expression network was constructed. Further, the mRNA–miRNA–lncRNA–drug interaction network was constructed to identify the top hub RNAs, significant cliques and topological parameters associated with differentially expressed genes. Then, the mRNAs from the top two subnetworks constructed are considered for transcription factor (TF) analysis. 39 interacting miRNAs and 1641 corresponding TFs for the eight mRNAs from the subnetworks are also utilized to construct an mRNA–miRNA–TF interaction network. TF analysis revealed two TFs (EST1 and SP1) from the cliques to be significant. TCGA expression analysis of miRNAs and lncRNAs as well as subclass-based and promoter methylation-based expression, oncoprint and survival analysis of the mRNAs are also done. Finally, functional enrichment of mRNAs is also performed. Significant cliques identified in the study can be utilized for identification of newer therapeutic interventions for breast cancer. This work will also help to gain a deeper insight into the complicated molecular intricacies to reveal the potential biomarkers involved with breast cancer progression in future. Nature Publishing Group UK 2023-09-22 /pmc/articles/PMC10516999/ /pubmed/37737288 http://dx.doi.org/10.1038/s41598-023-42904-6 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Chakraborty, Sohini
Banerjee, Satarupa
Multidimensional computational study to understand non-coding RNA interactions in breast cancer metastasis
title Multidimensional computational study to understand non-coding RNA interactions in breast cancer metastasis
title_full Multidimensional computational study to understand non-coding RNA interactions in breast cancer metastasis
title_fullStr Multidimensional computational study to understand non-coding RNA interactions in breast cancer metastasis
title_full_unstemmed Multidimensional computational study to understand non-coding RNA interactions in breast cancer metastasis
title_short Multidimensional computational study to understand non-coding RNA interactions in breast cancer metastasis
title_sort multidimensional computational study to understand non-coding rna interactions in breast cancer metastasis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10516999/
https://www.ncbi.nlm.nih.gov/pubmed/37737288
http://dx.doi.org/10.1038/s41598-023-42904-6
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