Identification of potential biomarkers in dengue via integrated bioinformatic analysis.

Dengue fever virus (DENV) is a global health threat that is becoming increasingly critical. However, the pathogenesis of dengue has not yet been fully elucidated. In this study, we employed bioinformatics analysis to identify potential biomarkers related to dengue fever and clarify their underlying...

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Autores principales:	Li-Min Xie, Xin Yin, Jie Bi, Huan-Min Luo, Xun-Jie Cao, Yu-Wen Ma, Ye-Ling Liu, Jian-Wen Su, Geng-Ling Lin, Xu-Guang Guo
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270
Acceso en línea:	https://doaj.org/article/1f02be71eb00482fbe215d5158370ea5
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spelling	oai:doaj.org-article:1f02be71eb00482fbe215d5158370ea52021-12-02T20:23:42ZIdentification of potential biomarkers in dengue via integrated bioinformatic analysis.1935-27271935-273510.1371/journal.pntd.0009633https://doaj.org/article/1f02be71eb00482fbe215d5158370ea52021-08-01T00:00:00Zhttps://doi.org/10.1371/journal.pntd.0009633https://doaj.org/toc/1935-2727https://doaj.org/toc/1935-2735Dengue fever virus (DENV) is a global health threat that is becoming increasingly critical. However, the pathogenesis of dengue has not yet been fully elucidated. In this study, we employed bioinformatics analysis to identify potential biomarkers related to dengue fever and clarify their underlying mechanisms. The results showed that there were 668, 1901, and 8283 differentially expressed genes between the dengue-infected samples and normal samples in the GSE28405, GSE38246, and GSE51808 datasets, respectively. Through overlapping, a total of 69 differentially expressed genes (DEGs) were identified, of which 51 were upregulated and 18 were downregulated. We identified twelve hub genes, including MX1, IFI44L, IFI44, IFI27, ISG15, STAT1, IFI35, OAS3, OAS2, OAS1, IFI6, and USP18. Except for IFI44 and STAT1, the others were statistically significant after validation. We predicted the related microRNAs (miRNAs) of these 12 target genes through the database miRTarBase, and finally obtained one important miRNA: has-mir-146a-5p. In addition, gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were carried out, and a protein-protein interaction (PPI) network was constructed to gain insight into the actions of DEGs. In conclusion, our study displayed the effectiveness of bioinformatics analysis methods in screening potential pathogenic genes in dengue fever and their underlying mechanisms. Further, we successfully predicted IFI44L and IFI6, as potential biomarkers with DENV infection, providing promising targets for the treatment of dengue fever to a certain extent.Li-Min XieXin YinJie BiHuan-Min LuoXun-Jie CaoYu-Wen MaYe-Ling LiuJian-Wen SuGeng-Ling LinXu-Guang GuoPublic Library of Science (PLoS)articleArctic medicine. Tropical medicineRC955-962Public aspects of medicineRA1-1270ENPLoS Neglected Tropical Diseases, Vol 15, Iss 8, p e0009633 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270
spellingShingle	Arctic medicine. Tropical medicine RC955-962 Public aspects of medicine RA1-1270 Li-Min Xie Xin Yin Jie Bi Huan-Min Luo Xun-Jie Cao Yu-Wen Ma Ye-Ling Liu Jian-Wen Su Geng-Ling Lin Xu-Guang Guo Identification of potential biomarkers in dengue via integrated bioinformatic analysis.
description	Dengue fever virus (DENV) is a global health threat that is becoming increasingly critical. However, the pathogenesis of dengue has not yet been fully elucidated. In this study, we employed bioinformatics analysis to identify potential biomarkers related to dengue fever and clarify their underlying mechanisms. The results showed that there were 668, 1901, and 8283 differentially expressed genes between the dengue-infected samples and normal samples in the GSE28405, GSE38246, and GSE51808 datasets, respectively. Through overlapping, a total of 69 differentially expressed genes (DEGs) were identified, of which 51 were upregulated and 18 were downregulated. We identified twelve hub genes, including MX1, IFI44L, IFI44, IFI27, ISG15, STAT1, IFI35, OAS3, OAS2, OAS1, IFI6, and USP18. Except for IFI44 and STAT1, the others were statistically significant after validation. We predicted the related microRNAs (miRNAs) of these 12 target genes through the database miRTarBase, and finally obtained one important miRNA: has-mir-146a-5p. In addition, gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were carried out, and a protein-protein interaction (PPI) network was constructed to gain insight into the actions of DEGs. In conclusion, our study displayed the effectiveness of bioinformatics analysis methods in screening potential pathogenic genes in dengue fever and their underlying mechanisms. Further, we successfully predicted IFI44L and IFI6, as potential biomarkers with DENV infection, providing promising targets for the treatment of dengue fever to a certain extent.
format	article
author	Li-Min Xie Xin Yin Jie Bi Huan-Min Luo Xun-Jie Cao Yu-Wen Ma Ye-Ling Liu Jian-Wen Su Geng-Ling Lin Xu-Guang Guo
author_facet	Li-Min Xie Xin Yin Jie Bi Huan-Min Luo Xun-Jie Cao Yu-Wen Ma Ye-Ling Liu Jian-Wen Su Geng-Ling Lin Xu-Guang Guo
author_sort	Li-Min Xie
title	Identification of potential biomarkers in dengue via integrated bioinformatic analysis.
title_short	Identification of potential biomarkers in dengue via integrated bioinformatic analysis.
title_full	Identification of potential biomarkers in dengue via integrated bioinformatic analysis.
title_fullStr	Identification of potential biomarkers in dengue via integrated bioinformatic analysis.
title_full_unstemmed	Identification of potential biomarkers in dengue via integrated bioinformatic analysis.
title_sort	identification of potential biomarkers in dengue via integrated bioinformatic analysis.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/1f02be71eb00482fbe215d5158370ea5
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Identification of potential biomarkers in dengue via integrated bioinformatic analysis.

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