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Weighted Gene Co-Expression Network Analysis Identifies Hub Genes Associated with Occurrence and Prognosis of Oral Squamous Cell Carcinoma

BACKGROUND: The aim of this study was to identify biomarkers closely related to the pathogenesis and prognosis of oral squamous cell carcinoma (OSCC) by using weighted gene co-expression network analysis (WGCNA) based on integrative transcriptome datasets. MATERIAL/METHODS: Gene expression profiles...

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Autores principales: Ge, You, Li, Wei, Ni, Qian, He, Yan, Chu, Jinjin, Wei, Pingmin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: International Scientific Literature, Inc. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6778410/
https://www.ncbi.nlm.nih.gov/pubmed/31562292
http://dx.doi.org/10.12659/MSM.916025
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author Ge, You
Li, Wei
Ni, Qian
He, Yan
Chu, Jinjin
Wei, Pingmin
author_facet Ge, You
Li, Wei
Ni, Qian
He, Yan
Chu, Jinjin
Wei, Pingmin
author_sort Ge, You
collection PubMed
description BACKGROUND: The aim of this study was to identify biomarkers closely related to the pathogenesis and prognosis of oral squamous cell carcinoma (OSCC) by using weighted gene co-expression network analysis (WGCNA) based on integrative transcriptome datasets. MATERIAL/METHODS: Gene expression profiles of OSCC were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were obtained and we then performed with Gene ontology (GO) and pathway enrichment analysis as well as protein–protein interactions (PPI) network analysis. WGCNA was used to construct the co-expression network. Multipart results were intersected to acquire the candidate genes, and survival analysis was used to identify the hub genes. RESULTS: A total of 568 DEGs, including 272 upregulated genes and 296 downregulated genes, were identified. GO and pathway analyses revealed that these DEGs were mainly enriched in extracellular matrix (ECM), ECM organization, structural constituent of muscle, and ECM-receptor interaction. The PPI network of DEGs was established, comprising 428 nodes and 1944 edges. In the co-expression network, pink module was the key module, in which 34 genes with high connectivity were identified. After the intersection of multipart results, 24 common genes were chosen as the candidate genes, among which 7 hub genes (PLAU, SERPINE1, LAMC2, ITGA5, TGFBI, FSCN1, and HLF) were identified using survival analysis. CONCLUSIONS: Seven potential biomarkers were identified as being closely related with the initiation and prognosis of OSCC and might serve as potential targets for early diagnosis and personalized therapy of OSCC.
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spelling pubmed-67784102019-10-17 Weighted Gene Co-Expression Network Analysis Identifies Hub Genes Associated with Occurrence and Prognosis of Oral Squamous Cell Carcinoma Ge, You Li, Wei Ni, Qian He, Yan Chu, Jinjin Wei, Pingmin Med Sci Monit Meta-Analysis BACKGROUND: The aim of this study was to identify biomarkers closely related to the pathogenesis and prognosis of oral squamous cell carcinoma (OSCC) by using weighted gene co-expression network analysis (WGCNA) based on integrative transcriptome datasets. MATERIAL/METHODS: Gene expression profiles of OSCC were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were obtained and we then performed with Gene ontology (GO) and pathway enrichment analysis as well as protein–protein interactions (PPI) network analysis. WGCNA was used to construct the co-expression network. Multipart results were intersected to acquire the candidate genes, and survival analysis was used to identify the hub genes. RESULTS: A total of 568 DEGs, including 272 upregulated genes and 296 downregulated genes, were identified. GO and pathway analyses revealed that these DEGs were mainly enriched in extracellular matrix (ECM), ECM organization, structural constituent of muscle, and ECM-receptor interaction. The PPI network of DEGs was established, comprising 428 nodes and 1944 edges. In the co-expression network, pink module was the key module, in which 34 genes with high connectivity were identified. After the intersection of multipart results, 24 common genes were chosen as the candidate genes, among which 7 hub genes (PLAU, SERPINE1, LAMC2, ITGA5, TGFBI, FSCN1, and HLF) were identified using survival analysis. CONCLUSIONS: Seven potential biomarkers were identified as being closely related with the initiation and prognosis of OSCC and might serve as potential targets for early diagnosis and personalized therapy of OSCC. International Scientific Literature, Inc. 2019-09-28 /pmc/articles/PMC6778410/ /pubmed/31562292 http://dx.doi.org/10.12659/MSM.916025 Text en © Med Sci Monit, 2019 This work is licensed under Creative Common Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) )
spellingShingle Meta-Analysis
Ge, You
Li, Wei
Ni, Qian
He, Yan
Chu, Jinjin
Wei, Pingmin
Weighted Gene Co-Expression Network Analysis Identifies Hub Genes Associated with Occurrence and Prognosis of Oral Squamous Cell Carcinoma
title Weighted Gene Co-Expression Network Analysis Identifies Hub Genes Associated with Occurrence and Prognosis of Oral Squamous Cell Carcinoma
title_full Weighted Gene Co-Expression Network Analysis Identifies Hub Genes Associated with Occurrence and Prognosis of Oral Squamous Cell Carcinoma
title_fullStr Weighted Gene Co-Expression Network Analysis Identifies Hub Genes Associated with Occurrence and Prognosis of Oral Squamous Cell Carcinoma
title_full_unstemmed Weighted Gene Co-Expression Network Analysis Identifies Hub Genes Associated with Occurrence and Prognosis of Oral Squamous Cell Carcinoma
title_short Weighted Gene Co-Expression Network Analysis Identifies Hub Genes Associated with Occurrence and Prognosis of Oral Squamous Cell Carcinoma
title_sort weighted gene co-expression network analysis identifies hub genes associated with occurrence and prognosis of oral squamous cell carcinoma
topic Meta-Analysis
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6778410/
https://www.ncbi.nlm.nih.gov/pubmed/31562292
http://dx.doi.org/10.12659/MSM.916025
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