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Comprehensive integrated analysis of gene expression datasets identifies key anti-cancer targets in different stages of breast cancer

Breast cancer is one of the primary threats to women's health worldwide. However, the molecular mechanisms underlying the development of breast cancer remain to be fully elucidated. The present study aimed to investigate specific target gene expression profiles in breast cancer tissues in gener...

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Autores principales: Gong, Meng-Ting, Ye, Shou-Dong, Lv, Wen-Wen, He, Kan, Li, Wen-Xing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: D.A. Spandidos 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6090421/
https://www.ncbi.nlm.nih.gov/pubmed/30112036
http://dx.doi.org/10.3892/etm.2018.6268
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author Gong, Meng-Ting
Ye, Shou-Dong
Lv, Wen-Wen
He, Kan
Li, Wen-Xing
author_facet Gong, Meng-Ting
Ye, Shou-Dong
Lv, Wen-Wen
He, Kan
Li, Wen-Xing
author_sort Gong, Meng-Ting
collection PubMed
description Breast cancer is one of the primary threats to women's health worldwide. However, the molecular mechanisms underlying the development of breast cancer remain to be fully elucidated. The present study aimed to investigate specific target gene expression profiles in breast cancer tissues in general and in different breast cancer stages, as well as to explore their functions in tumor development. For integrated analysis, a total of 5 gene expression profiling datasets for 3 different stages of breast cancer (stages I–III) were downloaded from the Gene Expression Omnibus of the National Center for Biotechnology Information. Pre-processing of these datasets was performed using the Robust Multi-array Average algorithm and global renormalization was performed for all studies. Differentially expressed genes between breast cancer patients and controls were estimated using the empirical Bayes algorithm. The Database for Annotation, Visualization and Integrated Discovery web server was used for analyzing the enrichment of the differentially expressed genes in Gene Ontology terms of the category biological process and in Kyoto Encyclopedia of Genes and Genomes pathways. Furthermore, breast cancer target genes were downloaded from the Thomson Reuters Integrity Database. We merged these target genes with the genes in breast cancer datasets. Analysis of anti-breast cancer gene networks was performed using the Genome-scale Integrated Analysis of Gene Networks in Tissues web server. The results demonstrated that the normal functions of the cell cycle, cell migration and cell adhesion were altered in all stages of breast cancer. Furthermore, 12 anti-breast cancer genes were identified to be dysregulated in at least one of the three stages. Among all of these genes, ribonucleotide reductase regulatory subunit M2 (RRM2) exhibited the highest degree of interaction with other interacting genes. Analysis of the network interactions revealed that the transcription factor of RRM2 is crucial for cancer development. Other genes, including mucin 1, progesterone receptor and cyclin-dependent kinase 5 regulatory subunit associated protein 3, also exhibited a high degree of interaction with the associated genes. In conclusion, several key anti-breast cancer genes identified in the present study are mainly associated with the regulation of the cell cycle, cell migration, cell adhesion and other cancer-associated cell functions, particularly RRM2.
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spelling pubmed-60904212018-08-15 Comprehensive integrated analysis of gene expression datasets identifies key anti-cancer targets in different stages of breast cancer Gong, Meng-Ting Ye, Shou-Dong Lv, Wen-Wen He, Kan Li, Wen-Xing Exp Ther Med Articles Breast cancer is one of the primary threats to women's health worldwide. However, the molecular mechanisms underlying the development of breast cancer remain to be fully elucidated. The present study aimed to investigate specific target gene expression profiles in breast cancer tissues in general and in different breast cancer stages, as well as to explore their functions in tumor development. For integrated analysis, a total of 5 gene expression profiling datasets for 3 different stages of breast cancer (stages I–III) were downloaded from the Gene Expression Omnibus of the National Center for Biotechnology Information. Pre-processing of these datasets was performed using the Robust Multi-array Average algorithm and global renormalization was performed for all studies. Differentially expressed genes between breast cancer patients and controls were estimated using the empirical Bayes algorithm. The Database for Annotation, Visualization and Integrated Discovery web server was used for analyzing the enrichment of the differentially expressed genes in Gene Ontology terms of the category biological process and in Kyoto Encyclopedia of Genes and Genomes pathways. Furthermore, breast cancer target genes were downloaded from the Thomson Reuters Integrity Database. We merged these target genes with the genes in breast cancer datasets. Analysis of anti-breast cancer gene networks was performed using the Genome-scale Integrated Analysis of Gene Networks in Tissues web server. The results demonstrated that the normal functions of the cell cycle, cell migration and cell adhesion were altered in all stages of breast cancer. Furthermore, 12 anti-breast cancer genes were identified to be dysregulated in at least one of the three stages. Among all of these genes, ribonucleotide reductase regulatory subunit M2 (RRM2) exhibited the highest degree of interaction with other interacting genes. Analysis of the network interactions revealed that the transcription factor of RRM2 is crucial for cancer development. Other genes, including mucin 1, progesterone receptor and cyclin-dependent kinase 5 regulatory subunit associated protein 3, also exhibited a high degree of interaction with the associated genes. In conclusion, several key anti-breast cancer genes identified in the present study are mainly associated with the regulation of the cell cycle, cell migration, cell adhesion and other cancer-associated cell functions, particularly RRM2. D.A. Spandidos 2018-08 2018-06-07 /pmc/articles/PMC6090421/ /pubmed/30112036 http://dx.doi.org/10.3892/etm.2018.6268 Text en Copyright: © Gong 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
Gong, Meng-Ting
Ye, Shou-Dong
Lv, Wen-Wen
He, Kan
Li, Wen-Xing
Comprehensive integrated analysis of gene expression datasets identifies key anti-cancer targets in different stages of breast cancer
title Comprehensive integrated analysis of gene expression datasets identifies key anti-cancer targets in different stages of breast cancer
title_full Comprehensive integrated analysis of gene expression datasets identifies key anti-cancer targets in different stages of breast cancer
title_fullStr Comprehensive integrated analysis of gene expression datasets identifies key anti-cancer targets in different stages of breast cancer
title_full_unstemmed Comprehensive integrated analysis of gene expression datasets identifies key anti-cancer targets in different stages of breast cancer
title_short Comprehensive integrated analysis of gene expression datasets identifies key anti-cancer targets in different stages of breast cancer
title_sort comprehensive integrated analysis of gene expression datasets identifies key anti-cancer targets in different stages of breast cancer
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6090421/
https://www.ncbi.nlm.nih.gov/pubmed/30112036
http://dx.doi.org/10.3892/etm.2018.6268
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