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Identification of lymph node metastasis-related microRNAs in breast cancer using bioinformatics analysis
BACKGROUND: Lymph node metastasis is a significant problem in breast cancer, and its underlying molecular mechanism is still unclear. The purpose of this study is to research the molecular mechanism and to explore the key RNAs and pathways that mediate lymph node metastasis in breast cancer. METHODS...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Lippincott Williams & Wilkins
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7523764/ https://www.ncbi.nlm.nih.gov/pubmed/32991406 http://dx.doi.org/10.1097/MD.0000000000022105 |
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author | Gao, Guangyu Shi, Xinya Yao, Zhen Shen, Jiaofeng Shen, Liqin |
author_facet | Gao, Guangyu Shi, Xinya Yao, Zhen Shen, Jiaofeng Shen, Liqin |
author_sort | Gao, Guangyu |
collection | PubMed |
description | BACKGROUND: Lymph node metastasis is a significant problem in breast cancer, and its underlying molecular mechanism is still unclear. The purpose of this study is to research the molecular mechanism and to explore the key RNAs and pathways that mediate lymph node metastasis in breast cancer. METHODS: GSE100453 and GSE38167 were downloaded from the Gene Expression Omnibus (GEO) database and 569 breast cancer statistics were also downloaded from the TCGA database. Differentially expressed miRNAs were calculated by using R software and GEO2R. Gene ontology and Enriched pathway analysis of target mRNAs were analyzed by using the Database for Database of Annotation Visualization and Integrated Discovery (DAVID) and R software. The protein–protein interaction (PPI) network was performed according to Metascape, String, and Cytoscape software. RESULTS: In total, 6 differentially expressed miRNAs were selected, and 499 mRNAs were identified after filtering. The research of the Kyoto Encyclopedia of Genes and Genomes (KEGG) demonstrated that mRNAs enriched in certain tumor pathways. Also, certain hub mRNAs were highlighted after constructed and analyzed the PPI network. A total of 3 out of 6 miRNAs had a significant relationship with the overall survival (P < .05) and showed a good ability of risk prediction model of over survival. CONCLUSIONS: By utilizing bioinformatics analyses, differently expressed miRNAs were identified and constructed a complete gene network. Several potential mechanisms and therapeutic and prognostic targets of lymph node metastasis were also demonstrated in breast cancer. |
format | Online Article Text |
id | pubmed-7523764 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Lippincott Williams & Wilkins |
record_format | MEDLINE/PubMed |
spelling | pubmed-75237642020-10-14 Identification of lymph node metastasis-related microRNAs in breast cancer using bioinformatics analysis Gao, Guangyu Shi, Xinya Yao, Zhen Shen, Jiaofeng Shen, Liqin Medicine (Baltimore) 5700 BACKGROUND: Lymph node metastasis is a significant problem in breast cancer, and its underlying molecular mechanism is still unclear. The purpose of this study is to research the molecular mechanism and to explore the key RNAs and pathways that mediate lymph node metastasis in breast cancer. METHODS: GSE100453 and GSE38167 were downloaded from the Gene Expression Omnibus (GEO) database and 569 breast cancer statistics were also downloaded from the TCGA database. Differentially expressed miRNAs were calculated by using R software and GEO2R. Gene ontology and Enriched pathway analysis of target mRNAs were analyzed by using the Database for Database of Annotation Visualization and Integrated Discovery (DAVID) and R software. The protein–protein interaction (PPI) network was performed according to Metascape, String, and Cytoscape software. RESULTS: In total, 6 differentially expressed miRNAs were selected, and 499 mRNAs were identified after filtering. The research of the Kyoto Encyclopedia of Genes and Genomes (KEGG) demonstrated that mRNAs enriched in certain tumor pathways. Also, certain hub mRNAs were highlighted after constructed and analyzed the PPI network. A total of 3 out of 6 miRNAs had a significant relationship with the overall survival (P < .05) and showed a good ability of risk prediction model of over survival. CONCLUSIONS: By utilizing bioinformatics analyses, differently expressed miRNAs were identified and constructed a complete gene network. Several potential mechanisms and therapeutic and prognostic targets of lymph node metastasis were also demonstrated in breast cancer. Lippincott Williams & Wilkins 2020-09-25 /pmc/articles/PMC7523764/ /pubmed/32991406 http://dx.doi.org/10.1097/MD.0000000000022105 Text en Copyright © 2020 the Author(s). Published by Wolters Kluwer Health, Inc. http://creativecommons.org/licenses/by-nc/4.0 This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC), where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc/4.0 |
spellingShingle | 5700 Gao, Guangyu Shi, Xinya Yao, Zhen Shen, Jiaofeng Shen, Liqin Identification of lymph node metastasis-related microRNAs in breast cancer using bioinformatics analysis |
title | Identification of lymph node metastasis-related microRNAs in breast cancer using bioinformatics analysis |
title_full | Identification of lymph node metastasis-related microRNAs in breast cancer using bioinformatics analysis |
title_fullStr | Identification of lymph node metastasis-related microRNAs in breast cancer using bioinformatics analysis |
title_full_unstemmed | Identification of lymph node metastasis-related microRNAs in breast cancer using bioinformatics analysis |
title_short | Identification of lymph node metastasis-related microRNAs in breast cancer using bioinformatics analysis |
title_sort | identification of lymph node metastasis-related micrornas in breast cancer using bioinformatics analysis |
topic | 5700 |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7523764/ https://www.ncbi.nlm.nih.gov/pubmed/32991406 http://dx.doi.org/10.1097/MD.0000000000022105 |
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