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Identification of an lncRNA-miRNA-mRNA interaction mechanism in breast cancer based on bioinformatic analysis
Non-coding RNAs serve important roles in regulating the expression of certain genes and are involved in the principal biological processes of breast cancer. The majority of studies have focused on defining the regulatory functions of long non-coding RNAs (lncRNAs) and microRNAs (miRNAs/miRs), and fe...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
D.A. Spandidos
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5647044/ https://www.ncbi.nlm.nih.gov/pubmed/28849135 http://dx.doi.org/10.3892/mmr.2017.7304 |
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author | Zhang, Yuhan Li, Yongfeng Wang, Qing Zhang, Xiping Wang, Dajin Tang, Hong Chao Meng, Xuli Ding, Xianfeng |
author_facet | Zhang, Yuhan Li, Yongfeng Wang, Qing Zhang, Xiping Wang, Dajin Tang, Hong Chao Meng, Xuli Ding, Xianfeng |
author_sort | Zhang, Yuhan |
collection | PubMed |
description | Non-coding RNAs serve important roles in regulating the expression of certain genes and are involved in the principal biological processes of breast cancer. The majority of studies have focused on defining the regulatory functions of long non-coding RNAs (lncRNAs) and microRNAs (miRNAs/miRs), and few studies have investigated how lncRNAs and miRNAs are transcriptionally regulated. In the present study, based on the breast invasive carcinoma dataset from The Cancer Genome Atlas at cBioPortal, and using a bioinformatics computational approach, an lncRNA-miRNA-mRNA network was constructed. The network consisted of 601 nodes and 706 edges, which represented the complex web of regulatory effects between lncRNAs, miRNAs and target genes. The results of the present study demonstrated that miR-510 was the most potent miRNA controller and regulator of numerous target genes. In addition, it was observed that the lncRNAs PVT1, CCAT1 and linc00861 exhibited possible interactions with clinical biomarkers, including receptor tyrosine-protein kinase erbB-2, estrogen receptor and progesterone receptor, demonstrated using RNA-protein interaction prediction software. The network of lncRNA-miRNA-mRNA interactions will facilitate further experimental studies and may be used to refine biomarker predictions for developing novel therapeutic approaches in breast cancer. |
format | Online Article Text |
id | pubmed-5647044 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | D.A. Spandidos |
record_format | MEDLINE/PubMed |
spelling | pubmed-56470442017-10-24 Identification of an lncRNA-miRNA-mRNA interaction mechanism in breast cancer based on bioinformatic analysis Zhang, Yuhan Li, Yongfeng Wang, Qing Zhang, Xiping Wang, Dajin Tang, Hong Chao Meng, Xuli Ding, Xianfeng Mol Med Rep Articles Non-coding RNAs serve important roles in regulating the expression of certain genes and are involved in the principal biological processes of breast cancer. The majority of studies have focused on defining the regulatory functions of long non-coding RNAs (lncRNAs) and microRNAs (miRNAs/miRs), and few studies have investigated how lncRNAs and miRNAs are transcriptionally regulated. In the present study, based on the breast invasive carcinoma dataset from The Cancer Genome Atlas at cBioPortal, and using a bioinformatics computational approach, an lncRNA-miRNA-mRNA network was constructed. The network consisted of 601 nodes and 706 edges, which represented the complex web of regulatory effects between lncRNAs, miRNAs and target genes. The results of the present study demonstrated that miR-510 was the most potent miRNA controller and regulator of numerous target genes. In addition, it was observed that the lncRNAs PVT1, CCAT1 and linc00861 exhibited possible interactions with clinical biomarkers, including receptor tyrosine-protein kinase erbB-2, estrogen receptor and progesterone receptor, demonstrated using RNA-protein interaction prediction software. The network of lncRNA-miRNA-mRNA interactions will facilitate further experimental studies and may be used to refine biomarker predictions for developing novel therapeutic approaches in breast cancer. D.A. Spandidos 2017-10 2017-08-21 /pmc/articles/PMC5647044/ /pubmed/28849135 http://dx.doi.org/10.3892/mmr.2017.7304 Text en Copyright: © Zhang et al. This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. |
spellingShingle | Articles Zhang, Yuhan Li, Yongfeng Wang, Qing Zhang, Xiping Wang, Dajin Tang, Hong Chao Meng, Xuli Ding, Xianfeng Identification of an lncRNA-miRNA-mRNA interaction mechanism in breast cancer based on bioinformatic analysis |
title | Identification of an lncRNA-miRNA-mRNA interaction mechanism in breast cancer based on bioinformatic analysis |
title_full | Identification of an lncRNA-miRNA-mRNA interaction mechanism in breast cancer based on bioinformatic analysis |
title_fullStr | Identification of an lncRNA-miRNA-mRNA interaction mechanism in breast cancer based on bioinformatic analysis |
title_full_unstemmed | Identification of an lncRNA-miRNA-mRNA interaction mechanism in breast cancer based on bioinformatic analysis |
title_short | Identification of an lncRNA-miRNA-mRNA interaction mechanism in breast cancer based on bioinformatic analysis |
title_sort | identification of an lncrna-mirna-mrna interaction mechanism in breast cancer based on bioinformatic analysis |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5647044/ https://www.ncbi.nlm.nih.gov/pubmed/28849135 http://dx.doi.org/10.3892/mmr.2017.7304 |
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