Identifying of biomarkers associated with gastric cancer based on 11 topological analysis methods of CytoHubba

Abstract Gastric cancer (GC) is one of the most common types of malignancy. Its potential molecular mechanism has not been clarified. In this study, we aimed to explore potential biomarkers and prognosis-related hub genes associated with GC. The gene chip dataset GSE79973 was downloaded from the GEO...

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Autores principales:	Hua Ma, Zhihui He, Jing Chen, Xu Zhang, Pingping Song
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f837fc71c6264fffab75a807e1c2fe73
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spelling	oai:doaj.org-article:f837fc71c6264fffab75a807e1c2fe732021-12-02T15:23:06ZIdentifying of biomarkers associated with gastric cancer based on 11 topological analysis methods of CytoHubba10.1038/s41598-020-79235-92045-2322https://doaj.org/article/f837fc71c6264fffab75a807e1c2fe732021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-79235-9https://doaj.org/toc/2045-2322Abstract Gastric cancer (GC) is one of the most common types of malignancy. Its potential molecular mechanism has not been clarified. In this study, we aimed to explore potential biomarkers and prognosis-related hub genes associated with GC. The gene chip dataset GSE79973 was downloaded from the GEO datasets and limma package was used to identify the differentially expressed genes (DEGs). A total of 1269 up-regulated and 330 down-regulated genes were identified. The protein-protein interactions (PPI) network of DEGs was constructed by STRING V11 database, and 11 hub genes were selected through intersection of 11 topological analysis methods of CytoHubba in Cytoscape plug-in. All the 11 selected hub genes were found in the module with the highest score from PPI network of all DEGs by the molecular complex detection (MCODE) clustering algorithm. In order to explore the role of the 11 hub genes, we performed GO function and KEGG pathway analysis for them and found that the genes were enriched in a variety of functions and pathways among which cellular senescence, cell cycle, viral carcinogenesis and p53 signaling pathway were the most associated with GC. Kaplan-Meier analysis revealed that 10 out of the 11 hub genes were related to the overall survival of GC patients. Further, seven of the 11 selected hub genes were verified significantly correlated with GC by uni- or multivariable Cox model and LASSO regression analysis including C3, CDK1, FN1, CCNB1, CDC20, BUB1B and MAD2L1. C3, CDK1, FN1, CCNB1, CDC20, BUB1B and MAD2L1 may serve as potential prognostic biomarkers and therapeutic targets for GC.Hua MaZhihui HeJing ChenXu ZhangPingping SongNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Hua Ma Zhihui He Jing Chen Xu Zhang Pingping Song Identifying of biomarkers associated with gastric cancer based on 11 topological analysis methods of CytoHubba
description	Abstract Gastric cancer (GC) is one of the most common types of malignancy. Its potential molecular mechanism has not been clarified. In this study, we aimed to explore potential biomarkers and prognosis-related hub genes associated with GC. The gene chip dataset GSE79973 was downloaded from the GEO datasets and limma package was used to identify the differentially expressed genes (DEGs). A total of 1269 up-regulated and 330 down-regulated genes were identified. The protein-protein interactions (PPI) network of DEGs was constructed by STRING V11 database, and 11 hub genes were selected through intersection of 11 topological analysis methods of CytoHubba in Cytoscape plug-in. All the 11 selected hub genes were found in the module with the highest score from PPI network of all DEGs by the molecular complex detection (MCODE) clustering algorithm. In order to explore the role of the 11 hub genes, we performed GO function and KEGG pathway analysis for them and found that the genes were enriched in a variety of functions and pathways among which cellular senescence, cell cycle, viral carcinogenesis and p53 signaling pathway were the most associated with GC. Kaplan-Meier analysis revealed that 10 out of the 11 hub genes were related to the overall survival of GC patients. Further, seven of the 11 selected hub genes were verified significantly correlated with GC by uni- or multivariable Cox model and LASSO regression analysis including C3, CDK1, FN1, CCNB1, CDC20, BUB1B and MAD2L1. C3, CDK1, FN1, CCNB1, CDC20, BUB1B and MAD2L1 may serve as potential prognostic biomarkers and therapeutic targets for GC.
format	article
author	Hua Ma Zhihui He Jing Chen Xu Zhang Pingping Song
author_facet	Hua Ma Zhihui He Jing Chen Xu Zhang Pingping Song
author_sort	Hua Ma
title	Identifying of biomarkers associated with gastric cancer based on 11 topological analysis methods of CytoHubba
title_short	Identifying of biomarkers associated with gastric cancer based on 11 topological analysis methods of CytoHubba
title_full	Identifying of biomarkers associated with gastric cancer based on 11 topological analysis methods of CytoHubba
title_fullStr	Identifying of biomarkers associated with gastric cancer based on 11 topological analysis methods of CytoHubba
title_full_unstemmed	Identifying of biomarkers associated with gastric cancer based on 11 topological analysis methods of CytoHubba
title_sort	identifying of biomarkers associated with gastric cancer based on 11 topological analysis methods of cytohubba
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/f837fc71c6264fffab75a807e1c2fe73
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_version_	1718387363752706048

Identifying of biomarkers associated with gastric cancer based on 11 topological analysis methods of CytoHubba

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