Identification of oral cancer related candidate genes by integrating protein-protein interactions, gene ontology, pathway analysis and immunohistochemistry

Abstract In the recent years, bioinformatics methods have been reported with a high degree of success for candidate gene identification. In this milieu, we have used an integrated bioinformatics approach assimilating information from gene ontologies (GO), protein–protein interaction (PPI) and networ...

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Autores principales:	Ravindra Kumar, Sabindra K. Samal, Samapika Routray, Rupesh Dash, Anshuman Dixit
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/6ab4a7c378f04fda894dc42d0ac6727f
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spelling	oai:doaj.org-article:6ab4a7c378f04fda894dc42d0ac6727f2021-12-02T11:40:33ZIdentification of oral cancer related candidate genes by integrating protein-protein interactions, gene ontology, pathway analysis and immunohistochemistry10.1038/s41598-017-02522-52045-2322https://doaj.org/article/6ab4a7c378f04fda894dc42d0ac6727f2017-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-02522-5https://doaj.org/toc/2045-2322Abstract In the recent years, bioinformatics methods have been reported with a high degree of success for candidate gene identification. In this milieu, we have used an integrated bioinformatics approach assimilating information from gene ontologies (GO), protein–protein interaction (PPI) and network analysis to predict candidate genes related to oral squamous cell carcinoma (OSCC). A total of 40973 PPIs were considered for 4704 cancer-related genes to construct human cancer gene network (HCGN). The importance of each node was measured in HCGN by ten different centrality measures. We have shown that the top ranking genes are related to a significantly higher number of diseases as compared to other genes in HCGN. A total of 39 candidate oral cancer target genes were predicted by combining top ranked genes and the genes corresponding to significantly enriched oral cancer related GO terms. Initial verification using literature and available experimental data indicated that 29 genes were related with OSCC. A detailed pathway analysis led us to propose a role for the selected candidate genes in the invasion and metastasis in OSCC. We further validated our predictions using immunohistochemistry (IHC) and found that the gene FLNA was upregulated while the genes ARRB1 and HTT were downregulated in the OSCC tissue samples.Ravindra KumarSabindra K. SamalSamapika RoutrayRupesh DashAnshuman DixitNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-18 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ravindra Kumar Sabindra K. Samal Samapika Routray Rupesh Dash Anshuman Dixit Identification of oral cancer related candidate genes by integrating protein-protein interactions, gene ontology, pathway analysis and immunohistochemistry
description	Abstract In the recent years, bioinformatics methods have been reported with a high degree of success for candidate gene identification. In this milieu, we have used an integrated bioinformatics approach assimilating information from gene ontologies (GO), protein–protein interaction (PPI) and network analysis to predict candidate genes related to oral squamous cell carcinoma (OSCC). A total of 40973 PPIs were considered for 4704 cancer-related genes to construct human cancer gene network (HCGN). The importance of each node was measured in HCGN by ten different centrality measures. We have shown that the top ranking genes are related to a significantly higher number of diseases as compared to other genes in HCGN. A total of 39 candidate oral cancer target genes were predicted by combining top ranked genes and the genes corresponding to significantly enriched oral cancer related GO terms. Initial verification using literature and available experimental data indicated that 29 genes were related with OSCC. A detailed pathway analysis led us to propose a role for the selected candidate genes in the invasion and metastasis in OSCC. We further validated our predictions using immunohistochemistry (IHC) and found that the gene FLNA was upregulated while the genes ARRB1 and HTT were downregulated in the OSCC tissue samples.
format	article
author	Ravindra Kumar Sabindra K. Samal Samapika Routray Rupesh Dash Anshuman Dixit
author_facet	Ravindra Kumar Sabindra K. Samal Samapika Routray Rupesh Dash Anshuman Dixit
author_sort	Ravindra Kumar
title	Identification of oral cancer related candidate genes by integrating protein-protein interactions, gene ontology, pathway analysis and immunohistochemistry
title_short	Identification of oral cancer related candidate genes by integrating protein-protein interactions, gene ontology, pathway analysis and immunohistochemistry
title_full	Identification of oral cancer related candidate genes by integrating protein-protein interactions, gene ontology, pathway analysis and immunohistochemistry
title_fullStr	Identification of oral cancer related candidate genes by integrating protein-protein interactions, gene ontology, pathway analysis and immunohistochemistry
title_full_unstemmed	Identification of oral cancer related candidate genes by integrating protein-protein interactions, gene ontology, pathway analysis and immunohistochemistry
title_sort	identification of oral cancer related candidate genes by integrating protein-protein interactions, gene ontology, pathway analysis and immunohistochemistry
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/6ab4a7c378f04fda894dc42d0ac6727f
work_keys_str_mv	AT ravindrakumar identificationoforalcancerrelatedcandidategenesbyintegratingproteinproteininteractionsgeneontologypathwayanalysisandimmunohistochemistry AT sabindraksamal identificationoforalcancerrelatedcandidategenesbyintegratingproteinproteininteractionsgeneontologypathwayanalysisandimmunohistochemistry AT samapikaroutray identificationoforalcancerrelatedcandidategenesbyintegratingproteinproteininteractionsgeneontologypathwayanalysisandimmunohistochemistry AT rupeshdash identificationoforalcancerrelatedcandidategenesbyintegratingproteinproteininteractionsgeneontologypathwayanalysisandimmunohistochemistry AT anshumandixit identificationoforalcancerrelatedcandidategenesbyintegratingproteinproteininteractionsgeneontologypathwayanalysisandimmunohistochemistry
_version_	1718395586866053120

Identification of oral cancer related candidate genes by integrating protein-protein interactions, gene ontology, pathway analysis and immunohistochemistry

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