Bioinformatics Analysis for Identifying Pertinent Pathways and Genes in Sepsis

Purpose. Sepsis becomes the main death reason in hospitals with rising incidence, causing a growing economic and medical burden. However, the genes related to the pathogenesis and prognosis of sepsis are still unclear, which is a problem that needs to be solved urgently. Materials and Methods. Gene...

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Autores principales:	Yiran Li, Hongyan Zhang, Jinyan Shao, Jindong Chen, Tiancheng Zhang, Xiaoyan Meng, Ruiqing Zong, Guangzhi Jin, Feixiang Wu
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/1321ba8536e44518af8ccfc937e93bcc
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spelling	oai:doaj.org-article:1321ba8536e44518af8ccfc937e93bcc2021-11-15T01:19:07ZBioinformatics Analysis for Identifying Pertinent Pathways and Genes in Sepsis1748-671810.1155/2021/2085173https://doaj.org/article/1321ba8536e44518af8ccfc937e93bcc2021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/2085173https://doaj.org/toc/1748-6718Purpose. Sepsis becomes the main death reason in hospitals with rising incidence, causing a growing economic and medical burden. However, the genes related to the pathogenesis and prognosis of sepsis are still unclear, which is a problem that needs to be solved urgently. Materials and Methods. Gene expression profiles of GSE69528 were obtained from the National Center for Biotechnology Information. Limma software package got employed to search for differentially expressed genes (DEGs). Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) were used for enrichment analysis. Protein-protein interaction (PPI) network was built by the Search Tool for the Retrieval of Interacting Genes (STRING) database. Results. We screened 101 DEGs, containing 81 upregulated DEGs and 20 downregulated DEGs. GO analysis demonstrated that the upregulated DEGs were chiefly concentrated in negative regulation of response to interferon-gamma and regulation of granulocyte differentiation. KEGG analysis revealed that the pathways of upregulated DEGs were concentrated in prion diseases, complement and coagulation cascades, and Staphylococcus aureus infection. The PPI network constructed by upregulated DEGs contained 67 nodes (proteins) and 110 edges (interactions). Analysis of bioinformatics results showed that CEACAM8, MPO, and RETN were hub genes of sepsis. Conclusion. Our analysis reveals a series of signal pathways and key genes related to the mechanism of sepsis, which are promising biotargets and biomarkers of sepsis.Yiran LiHongyan ZhangJinyan ShaoJindong ChenTiancheng ZhangXiaoyan MengRuiqing ZongGuangzhi JinFeixiang WuHindawi 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 Yiran Li Hongyan Zhang Jinyan Shao Jindong Chen Tiancheng Zhang Xiaoyan Meng Ruiqing Zong Guangzhi Jin Feixiang Wu Bioinformatics Analysis for Identifying Pertinent Pathways and Genes in Sepsis
description	Purpose. Sepsis becomes the main death reason in hospitals with rising incidence, causing a growing economic and medical burden. However, the genes related to the pathogenesis and prognosis of sepsis are still unclear, which is a problem that needs to be solved urgently. Materials and Methods. Gene expression profiles of GSE69528 were obtained from the National Center for Biotechnology Information. Limma software package got employed to search for differentially expressed genes (DEGs). Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) were used for enrichment analysis. Protein-protein interaction (PPI) network was built by the Search Tool for the Retrieval of Interacting Genes (STRING) database. Results. We screened 101 DEGs, containing 81 upregulated DEGs and 20 downregulated DEGs. GO analysis demonstrated that the upregulated DEGs were chiefly concentrated in negative regulation of response to interferon-gamma and regulation of granulocyte differentiation. KEGG analysis revealed that the pathways of upregulated DEGs were concentrated in prion diseases, complement and coagulation cascades, and Staphylococcus aureus infection. The PPI network constructed by upregulated DEGs contained 67 nodes (proteins) and 110 edges (interactions). Analysis of bioinformatics results showed that CEACAM8, MPO, and RETN were hub genes of sepsis. Conclusion. Our analysis reveals a series of signal pathways and key genes related to the mechanism of sepsis, which are promising biotargets and biomarkers of sepsis.
format	article
author	Yiran Li Hongyan Zhang Jinyan Shao Jindong Chen Tiancheng Zhang Xiaoyan Meng Ruiqing Zong Guangzhi Jin Feixiang Wu
author_facet	Yiran Li Hongyan Zhang Jinyan Shao Jindong Chen Tiancheng Zhang Xiaoyan Meng Ruiqing Zong Guangzhi Jin Feixiang Wu
author_sort	Yiran Li
title	Bioinformatics Analysis for Identifying Pertinent Pathways and Genes in Sepsis
title_short	Bioinformatics Analysis for Identifying Pertinent Pathways and Genes in Sepsis
title_full	Bioinformatics Analysis for Identifying Pertinent Pathways and Genes in Sepsis
title_fullStr	Bioinformatics Analysis for Identifying Pertinent Pathways and Genes in Sepsis
title_full_unstemmed	Bioinformatics Analysis for Identifying Pertinent Pathways and Genes in Sepsis
title_sort	bioinformatics analysis for identifying pertinent pathways and genes in sepsis
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/1321ba8536e44518af8ccfc937e93bcc
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Bioinformatics Analysis for Identifying Pertinent Pathways and Genes in Sepsis

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