The Identification of Candidate Biomarkers and Pathways in Atherosclerosis by Integrated Bioinformatics Analysis

Background. Atherosclerosis (AS) is a type of yellow substance containing cholesterol in the intima of large and middle arteries, which is mostly caused by fat metabolism disorders and neurovascular dysfunction. Materials and Methods. The GSE100927 data got analyzed to find out the differentially ex...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Youwei Lu, Xi Zhang, Wei Hu, Qianhong Yang
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/8dbb85594354451c9116c43472c3f77e
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:8dbb85594354451c9116c43472c3f77e
record_format	dspace
spelling	oai:doaj.org-article:8dbb85594354451c9116c43472c3f77e2021-11-22T01:11:14ZThe Identification of Candidate Biomarkers and Pathways in Atherosclerosis by Integrated Bioinformatics Analysis1748-671810.1155/2021/6276480https://doaj.org/article/8dbb85594354451c9116c43472c3f77e2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/6276480https://doaj.org/toc/1748-6718Background. Atherosclerosis (AS) is a type of yellow substance containing cholesterol in the intima of large and middle arteries, which is mostly caused by fat metabolism disorders and neurovascular dysfunction. Materials and Methods. The GSE100927 data got analyzed to find out the differentially expressed genes (DEGs) using the limma package in R software. Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of the DEGs were assessed by the Database for Annotation, Visualization, and Integrated Discovery (DAVID). The Search Tool for the Retrieval of Interacting Genes (STRING) visualized the Protein-Protein Interaction (PPI) network of the aggregated DEGs. GSEA software was used to verify the biological process. Result. We screened 1574 DEGs from 69 groups of atherosclerotic carotid artery and 35 groups of control carotid artery, including 1033 upregulated DEGs and 541 downregulated DEGs. DEGs of AS were chiefly related to immune response, Epstein-Barr virus infection, vascular smooth muscle contraction, and cGMP-PKG signaling pathway. Through PPI networks, we found that the hub genes of AS were PTAFR, VAMP8, RNF19A, VPRBP, RNF217, KLHL42, NEDD4, SH3RF1, UBE2N, PJA2, RNF115, ITCH, SKP1, FBXW4, and UBE2H. GSEA analysis showed that GSE100927 was concentrated in RIPK1-mediated regulated necrosis, FC epsilon receptor fceri signaling, Fceri-mediated NF KB activation, TBC rabgaps, TRAF6-mediated induction of TAK1 complex within TLR4 complex, and RAB regulation of trafficking. Conclusion. Our analysis reveals that immune response, Epstein-Barr virus infection, and so on were major signatures of AS. PTAFR, VAMP8, VPRBP, RNF217, KLHL42, and NEDD4 might facilitate the AS tumorigenesis, which could be new biomarkers for diagnosis and therapy of AS.Youwei LuXi ZhangWei HuQianhong YangHindawi LimitedarticleComputer applications to medicine. Medical informaticsR858-859.7ENComputational and Mathematical Methods in Medicine, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Youwei Lu Xi Zhang Wei Hu Qianhong Yang The Identification of Candidate Biomarkers and Pathways in Atherosclerosis by Integrated Bioinformatics Analysis
description	Background. Atherosclerosis (AS) is a type of yellow substance containing cholesterol in the intima of large and middle arteries, which is mostly caused by fat metabolism disorders and neurovascular dysfunction. Materials and Methods. The GSE100927 data got analyzed to find out the differentially expressed genes (DEGs) using the limma package in R software. Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of the DEGs were assessed by the Database for Annotation, Visualization, and Integrated Discovery (DAVID). The Search Tool for the Retrieval of Interacting Genes (STRING) visualized the Protein-Protein Interaction (PPI) network of the aggregated DEGs. GSEA software was used to verify the biological process. Result. We screened 1574 DEGs from 69 groups of atherosclerotic carotid artery and 35 groups of control carotid artery, including 1033 upregulated DEGs and 541 downregulated DEGs. DEGs of AS were chiefly related to immune response, Epstein-Barr virus infection, vascular smooth muscle contraction, and cGMP-PKG signaling pathway. Through PPI networks, we found that the hub genes of AS were PTAFR, VAMP8, RNF19A, VPRBP, RNF217, KLHL42, NEDD4, SH3RF1, UBE2N, PJA2, RNF115, ITCH, SKP1, FBXW4, and UBE2H. GSEA analysis showed that GSE100927 was concentrated in RIPK1-mediated regulated necrosis, FC epsilon receptor fceri signaling, Fceri-mediated NF KB activation, TBC rabgaps, TRAF6-mediated induction of TAK1 complex within TLR4 complex, and RAB regulation of trafficking. Conclusion. Our analysis reveals that immune response, Epstein-Barr virus infection, and so on were major signatures of AS. PTAFR, VAMP8, VPRBP, RNF217, KLHL42, and NEDD4 might facilitate the AS tumorigenesis, which could be new biomarkers for diagnosis and therapy of AS.
format	article
author	Youwei Lu Xi Zhang Wei Hu Qianhong Yang
author_facet	Youwei Lu Xi Zhang Wei Hu Qianhong Yang
author_sort	Youwei Lu
title	The Identification of Candidate Biomarkers and Pathways in Atherosclerosis by Integrated Bioinformatics Analysis
title_short	The Identification of Candidate Biomarkers and Pathways in Atherosclerosis by Integrated Bioinformatics Analysis
title_full	The Identification of Candidate Biomarkers and Pathways in Atherosclerosis by Integrated Bioinformatics Analysis
title_fullStr	The Identification of Candidate Biomarkers and Pathways in Atherosclerosis by Integrated Bioinformatics Analysis
title_full_unstemmed	The Identification of Candidate Biomarkers and Pathways in Atherosclerosis by Integrated Bioinformatics Analysis
title_sort	identification of candidate biomarkers and pathways in atherosclerosis by integrated bioinformatics analysis
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/8dbb85594354451c9116c43472c3f77e
work_keys_str_mv	AT youweilu theidentificationofcandidatebiomarkersandpathwaysinatherosclerosisbyintegratedbioinformaticsanalysis AT xizhang theidentificationofcandidatebiomarkersandpathwaysinatherosclerosisbyintegratedbioinformaticsanalysis AT weihu theidentificationofcandidatebiomarkersandpathwaysinatherosclerosisbyintegratedbioinformaticsanalysis AT qianhongyang theidentificationofcandidatebiomarkersandpathwaysinatherosclerosisbyintegratedbioinformaticsanalysis AT youweilu identificationofcandidatebiomarkersandpathwaysinatherosclerosisbyintegratedbioinformaticsanalysis AT xizhang identificationofcandidatebiomarkersandpathwaysinatherosclerosisbyintegratedbioinformaticsanalysis AT weihu identificationofcandidatebiomarkersandpathwaysinatherosclerosisbyintegratedbioinformaticsanalysis AT qianhongyang identificationofcandidatebiomarkersandpathwaysinatherosclerosisbyintegratedbioinformaticsanalysis
_version_	1718418294215540736

The Identification of Candidate Biomarkers and Pathways in Atherosclerosis by Integrated Bioinformatics Analysis

Ejemplares similares