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Identification of potential key genes and functional role of CENPF in osteosarcoma using bioinformatics and experimental analysis

Osteosarcoma, which arises from bone tissue, is considered to be one of the most common types of cancer in children and teenagers. As the etiology of osteosarcoma has not been fully elucidated, the overall prognosis for patients is generally poor. In recent years, the development of bioinformatical...

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Autores principales: Ma, Yihui, Guo, Jiaping, Li, Da, Cai, Xianhua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: D.A. Spandidos 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8652394/
https://www.ncbi.nlm.nih.gov/pubmed/34934449
http://dx.doi.org/10.3892/etm.2021.11003
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author Ma, Yihui
Guo, Jiaping
Li, Da
Cai, Xianhua
author_facet Ma, Yihui
Guo, Jiaping
Li, Da
Cai, Xianhua
author_sort Ma, Yihui
collection PubMed
description Osteosarcoma, which arises from bone tissue, is considered to be one of the most common types of cancer in children and teenagers. As the etiology of osteosarcoma has not been fully elucidated, the overall prognosis for patients is generally poor. In recent years, the development of bioinformatical technology has allowed researchers to identify numerous molecular biological characteristics associated with the prognosis of osteosarcoma using online databases. In the present study, Gene Expression Omnibus (GEO) database was used and three microarray datasets were obtained. The GEO2R web tool was utilized and differentially expressed genes (DEGs) in osteosarcoma tissue were identified. Venn analysis was performed to determine the intersection of the DEG profiles. DEGs were analyzed by Gene Ontology function and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis. Protein-protein interactions (PPIs) between these DEGs were analyzed using the Search Tool for the Retrieval of Interacting Genes database, and the PPI network was then visualized using Cytoscape software. The top ten genes were identified based on measurement of degree, density of maximum neighborhood component, maximal clique centrality and mononuclear cell counts in the PPI network, and five overlapping genes [origin recognition complex subunit 6 (ORC6), IGF-binding protein 5 (IGFBP5), minichromosome maintenance 10 replication initiation factor (MCM10), MET proto-oncogene, receptor tyrosine kinase (MET) and centromere protein F (CENPF)] were identified. Additionally, three module networks were analyzed by Molecular Complex Detection (MCODE), and six key genes [ORC6, MCM10, DEP domain containing 1 (DEPDC1), CENPF, TIMELESS interacting protein (TIPIN) and shugoshin 1 (SGOL1)] were screened. Combined with the results from Cytoscape and MCODE, eight hub genes (ORC6, MCM10, DEPDC1, CENPF, TIPIN, SGOL1, MET and IGFBP5) were obtained. Furthermore, Kaplan-Meier plotter survival analysis was used to evaluate the prognostic value of these eight hub genes in patients with osteosarcoma. Oncomine and GEPIA databases were applied to further confirm the expression levels of hub genes in tissue. Finally, the functional roles of the core gene CENPF were investigated using Cell Counting Kit-8, wound healing and Transwell assays, which indicated that CENPF knockdown inhibited the proliferation, migration and invasion of osteosarcoma cells. These results provided potential prognostic markers, as well as a basis for further investigation of the mechanism underlying osteosarcoma.
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spelling pubmed-86523942021-12-20 Identification of potential key genes and functional role of CENPF in osteosarcoma using bioinformatics and experimental analysis Ma, Yihui Guo, Jiaping Li, Da Cai, Xianhua Exp Ther Med Articles Osteosarcoma, which arises from bone tissue, is considered to be one of the most common types of cancer in children and teenagers. As the etiology of osteosarcoma has not been fully elucidated, the overall prognosis for patients is generally poor. In recent years, the development of bioinformatical technology has allowed researchers to identify numerous molecular biological characteristics associated with the prognosis of osteosarcoma using online databases. In the present study, Gene Expression Omnibus (GEO) database was used and three microarray datasets were obtained. The GEO2R web tool was utilized and differentially expressed genes (DEGs) in osteosarcoma tissue were identified. Venn analysis was performed to determine the intersection of the DEG profiles. DEGs were analyzed by Gene Ontology function and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis. Protein-protein interactions (PPIs) between these DEGs were analyzed using the Search Tool for the Retrieval of Interacting Genes database, and the PPI network was then visualized using Cytoscape software. The top ten genes were identified based on measurement of degree, density of maximum neighborhood component, maximal clique centrality and mononuclear cell counts in the PPI network, and five overlapping genes [origin recognition complex subunit 6 (ORC6), IGF-binding protein 5 (IGFBP5), minichromosome maintenance 10 replication initiation factor (MCM10), MET proto-oncogene, receptor tyrosine kinase (MET) and centromere protein F (CENPF)] were identified. Additionally, three module networks were analyzed by Molecular Complex Detection (MCODE), and six key genes [ORC6, MCM10, DEP domain containing 1 (DEPDC1), CENPF, TIMELESS interacting protein (TIPIN) and shugoshin 1 (SGOL1)] were screened. Combined with the results from Cytoscape and MCODE, eight hub genes (ORC6, MCM10, DEPDC1, CENPF, TIPIN, SGOL1, MET and IGFBP5) were obtained. Furthermore, Kaplan-Meier plotter survival analysis was used to evaluate the prognostic value of these eight hub genes in patients with osteosarcoma. Oncomine and GEPIA databases were applied to further confirm the expression levels of hub genes in tissue. Finally, the functional roles of the core gene CENPF were investigated using Cell Counting Kit-8, wound healing and Transwell assays, which indicated that CENPF knockdown inhibited the proliferation, migration and invasion of osteosarcoma cells. These results provided potential prognostic markers, as well as a basis for further investigation of the mechanism underlying osteosarcoma. D.A. Spandidos 2022-01 2021-11-25 /pmc/articles/PMC8652394/ /pubmed/34934449 http://dx.doi.org/10.3892/etm.2021.11003 Text en Copyright: © Ma et al. https://creativecommons.org/licenses/by-nc-nd/4.0/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
Ma, Yihui
Guo, Jiaping
Li, Da
Cai, Xianhua
Identification of potential key genes and functional role of CENPF in osteosarcoma using bioinformatics and experimental analysis
title Identification of potential key genes and functional role of CENPF in osteosarcoma using bioinformatics and experimental analysis
title_full Identification of potential key genes and functional role of CENPF in osteosarcoma using bioinformatics and experimental analysis
title_fullStr Identification of potential key genes and functional role of CENPF in osteosarcoma using bioinformatics and experimental analysis
title_full_unstemmed Identification of potential key genes and functional role of CENPF in osteosarcoma using bioinformatics and experimental analysis
title_short Identification of potential key genes and functional role of CENPF in osteosarcoma using bioinformatics and experimental analysis
title_sort identification of potential key genes and functional role of cenpf in osteosarcoma using bioinformatics and experimental analysis
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8652394/
https://www.ncbi.nlm.nih.gov/pubmed/34934449
http://dx.doi.org/10.3892/etm.2021.11003
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