Cargando…

KEGG-expressed genes and pathways in triple negative breast cancer: Protocol for a systematic review and data mining

BACKGROUND: The incidence of triple negative breast cancer (TNBC) is at a relatively high level, and our study aimed to identify differentially expressed genes (DEGs) in TNBC and explore the key pathways and genes of TNBC. METHODS: The gene expression profiling (GSE86945, GSE86946 and GSE102088) dat...

Descripción completa

Detalles Bibliográficos
Autores principales: Chen, Jiarui, Liu, Chong, Cen, Jiemei, Liang, Tuo, Xue, Jiang, Zeng, Haopeng, Zhang, Zide, Xu, Guoyong, Yu, Chaojie, Lu, Zhaojun, Wang, Zequn, Jiang, Jie, Zhan, Xinli, Zeng, Jian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Wolters Kluwer Health 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7440132/
https://www.ncbi.nlm.nih.gov/pubmed/32358373
http://dx.doi.org/10.1097/MD.0000000000019986
_version_ 1783573106897453056
author Chen, Jiarui
Liu, Chong
Cen, Jiemei
Liang, Tuo
Xue, Jiang
Zeng, Haopeng
Zhang, Zide
Xu, Guoyong
Yu, Chaojie
Lu, Zhaojun
Wang, Zequn
Jiang, Jie
Zhan, Xinli
Zeng, Jian
author_facet Chen, Jiarui
Liu, Chong
Cen, Jiemei
Liang, Tuo
Xue, Jiang
Zeng, Haopeng
Zhang, Zide
Xu, Guoyong
Yu, Chaojie
Lu, Zhaojun
Wang, Zequn
Jiang, Jie
Zhan, Xinli
Zeng, Jian
author_sort Chen, Jiarui
collection PubMed
description BACKGROUND: The incidence of triple negative breast cancer (TNBC) is at a relatively high level, and our study aimed to identify differentially expressed genes (DEGs) in TNBC and explore the key pathways and genes of TNBC. METHODS: The gene expression profiling (GSE86945, GSE86946 and GSE102088) data were obtained from Gene Expression Omnibus Datasets, DEGs were identified by using R software, Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of DEGs were performed by the Database for Annotation, Visualization and Integrated Discovery (DAVID) tools, and the protein-protein interaction (PPI) network of the DEGs was constructed by the STRING database and visualized by Cytoscape software. Finally, the survival value of hub DEGs in breast cancer patients were performed by the Kaplan–Meier plotter online tool. RESULTS: A total of 2998 DEGs were identified between TNBC and health breast tissue, including 411 up-regulated DEGs and 2587 down-regulated DEGs. GO analysis results showed that down-regulated DEGs were enriched in gene expression (BP), extracellular exosome (CC), and nucleic acid binding, and up-regulated were enriched in chromatin assembly (BP), nucleosome (CC), and DNA binding (MF). KEGG pathway results showed that DEGs were mainly enriched in Pathways in cancer and Systemic lupus erythematosus and so on. Top 10 hub genes were picked out from PPI network by connective degree, and 7 of top 10 hub genes were significantly related with adverse overall survival in breast cancer patients (P < .05). Further analysis found that only EGFR had a significant association with the prognosis of triple-negative breast cancer (P < .05). CONCLUSIONS: Our study showed that DEGs were enriched in pathways in cancer, top 10 DEGs belong to up-regulated DEGs, and 7 gene connected with poor prognosis in breast cancer, including HSP90AA1, SRC, HSPA8, ESR1, ACTB, PPP2CA, and RPL4. These can provide some guidance for our research on the diagnosis and prognosis of TNBC, and further research is needed to evaluate their value in the targeted therapy of TNBC.
format Online
Article
Text
id pubmed-7440132
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher Wolters Kluwer Health
record_format MEDLINE/PubMed
spelling pubmed-74401322020-09-04 KEGG-expressed genes and pathways in triple negative breast cancer: Protocol for a systematic review and data mining Chen, Jiarui Liu, Chong Cen, Jiemei Liang, Tuo Xue, Jiang Zeng, Haopeng Zhang, Zide Xu, Guoyong Yu, Chaojie Lu, Zhaojun Wang, Zequn Jiang, Jie Zhan, Xinli Zeng, Jian Medicine (Baltimore) 4100 BACKGROUND: The incidence of triple negative breast cancer (TNBC) is at a relatively high level, and our study aimed to identify differentially expressed genes (DEGs) in TNBC and explore the key pathways and genes of TNBC. METHODS: The gene expression profiling (GSE86945, GSE86946 and GSE102088) data were obtained from Gene Expression Omnibus Datasets, DEGs were identified by using R software, Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of DEGs were performed by the Database for Annotation, Visualization and Integrated Discovery (DAVID) tools, and the protein-protein interaction (PPI) network of the DEGs was constructed by the STRING database and visualized by Cytoscape software. Finally, the survival value of hub DEGs in breast cancer patients were performed by the Kaplan–Meier plotter online tool. RESULTS: A total of 2998 DEGs were identified between TNBC and health breast tissue, including 411 up-regulated DEGs and 2587 down-regulated DEGs. GO analysis results showed that down-regulated DEGs were enriched in gene expression (BP), extracellular exosome (CC), and nucleic acid binding, and up-regulated were enriched in chromatin assembly (BP), nucleosome (CC), and DNA binding (MF). KEGG pathway results showed that DEGs were mainly enriched in Pathways in cancer and Systemic lupus erythematosus and so on. Top 10 hub genes were picked out from PPI network by connective degree, and 7 of top 10 hub genes were significantly related with adverse overall survival in breast cancer patients (P < .05). Further analysis found that only EGFR had a significant association with the prognosis of triple-negative breast cancer (P < .05). CONCLUSIONS: Our study showed that DEGs were enriched in pathways in cancer, top 10 DEGs belong to up-regulated DEGs, and 7 gene connected with poor prognosis in breast cancer, including HSP90AA1, SRC, HSPA8, ESR1, ACTB, PPP2CA, and RPL4. These can provide some guidance for our research on the diagnosis and prognosis of TNBC, and further research is needed to evaluate their value in the targeted therapy of TNBC. Wolters Kluwer Health 2020-05-01 /pmc/articles/PMC7440132/ /pubmed/32358373 http://dx.doi.org/10.1097/MD.0000000000019986 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 4100
Chen, Jiarui
Liu, Chong
Cen, Jiemei
Liang, Tuo
Xue, Jiang
Zeng, Haopeng
Zhang, Zide
Xu, Guoyong
Yu, Chaojie
Lu, Zhaojun
Wang, Zequn
Jiang, Jie
Zhan, Xinli
Zeng, Jian
KEGG-expressed genes and pathways in triple negative breast cancer: Protocol for a systematic review and data mining
title KEGG-expressed genes and pathways in triple negative breast cancer: Protocol for a systematic review and data mining
title_full KEGG-expressed genes and pathways in triple negative breast cancer: Protocol for a systematic review and data mining
title_fullStr KEGG-expressed genes and pathways in triple negative breast cancer: Protocol for a systematic review and data mining
title_full_unstemmed KEGG-expressed genes and pathways in triple negative breast cancer: Protocol for a systematic review and data mining
title_short KEGG-expressed genes and pathways in triple negative breast cancer: Protocol for a systematic review and data mining
title_sort kegg-expressed genes and pathways in triple negative breast cancer: protocol for a systematic review and data mining
topic 4100
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7440132/
https://www.ncbi.nlm.nih.gov/pubmed/32358373
http://dx.doi.org/10.1097/MD.0000000000019986
work_keys_str_mv AT chenjiarui keggexpressedgenesandpathwaysintriplenegativebreastcancerprotocolforasystematicreviewanddatamining
AT liuchong keggexpressedgenesandpathwaysintriplenegativebreastcancerprotocolforasystematicreviewanddatamining
AT cenjiemei keggexpressedgenesandpathwaysintriplenegativebreastcancerprotocolforasystematicreviewanddatamining
AT liangtuo keggexpressedgenesandpathwaysintriplenegativebreastcancerprotocolforasystematicreviewanddatamining
AT xuejiang keggexpressedgenesandpathwaysintriplenegativebreastcancerprotocolforasystematicreviewanddatamining
AT zenghaopeng keggexpressedgenesandpathwaysintriplenegativebreastcancerprotocolforasystematicreviewanddatamining
AT zhangzide keggexpressedgenesandpathwaysintriplenegativebreastcancerprotocolforasystematicreviewanddatamining
AT xuguoyong keggexpressedgenesandpathwaysintriplenegativebreastcancerprotocolforasystematicreviewanddatamining
AT yuchaojie keggexpressedgenesandpathwaysintriplenegativebreastcancerprotocolforasystematicreviewanddatamining
AT luzhaojun keggexpressedgenesandpathwaysintriplenegativebreastcancerprotocolforasystematicreviewanddatamining
AT wangzequn keggexpressedgenesandpathwaysintriplenegativebreastcancerprotocolforasystematicreviewanddatamining
AT jiangjie keggexpressedgenesandpathwaysintriplenegativebreastcancerprotocolforasystematicreviewanddatamining
AT zhanxinli keggexpressedgenesandpathwaysintriplenegativebreastcancerprotocolforasystematicreviewanddatamining
AT zengjian keggexpressedgenesandpathwaysintriplenegativebreastcancerprotocolforasystematicreviewanddatamining