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Exploring the biomarkers and potential therapeutic drugs for sepsis via integrated bioinformatic analysis.
Liang, Pingping; Wu, Yongjian; Qu, Siying; Younis, Muhammad; Wang, Wei; Wu, Zhilong; Huang, Xi.
Afiliación
  • Liang P; Foshan Fourth People's Hospital, Guangdong Province, Foshan, 528041, China.
  • Wu Y; Center for Infection and Immunity and Guangdong Provincial Engineering Research Center of Molecular Imaging, the Fifth Affiliated Hospital of Sun Yat-Sen University, Guangdong Province, Zhuhai, 519000, China.
  • Qu S; Center for Infection and Immunity and Guangdong Provincial Engineering Research Center of Molecular Imaging, the Fifth Affiliated Hospital of Sun Yat-Sen University, Guangdong Province, Zhuhai, 519000, China.
  • Younis M; Department of Clinical Laboratory, Zhuhai Hospital of Integrated Traditional Chinese and Western Medicine, The Second People's Hospital of Zhuhai, Guangdong Province, Zhuhai, 519020, China.
  • Wang W; Foshan Fourth People's Hospital, Guangdong Province, Foshan, 528041, China.
  • Wu Z; Center for Infection and Immunity and Guangdong Provincial Engineering Research Center of Molecular Imaging, the Fifth Affiliated Hospital of Sun Yat-Sen University, Guangdong Province, Zhuhai, 519000, China.
  • Huang X; Foshan Fourth People's Hospital, Guangdong Province, Foshan, 528041, China.
BMC Infect Dis ; 24(1): 32, 2024 Jan 02.
Article en En | MEDLINE | ID: mdl-38166628
ABSTRACT

BACKGROUND:

Sepsis is a life-threatening condition caused by an excessive inflammatory response to an infection, associated with high mortality. However, the regulatory mechanism of sepsis remains unclear.

RESULTS:

In this study, bioinformatics analysis revealed the novel key biomarkers associated with sepsis and potential regulators. Three public datasets (GSE28750, GSE57065 and GSE95233) were employed to recognize the differentially expressed genes (DEGs). Taking the intersection of DEGs from these three datasets, GO and KEGG pathway enrichment analysis revealed 537 shared DEGs and their biological functions and pathways. These genes were mainly enriched in T cell activation, differentiation, lymphocyte differentiation, mononuclear cell differentiation, and regulation of T cell activation based on GO analysis. Further, pathway enrichment analysis revealed that these DEGs were significantly enriched in Th1, Th2 and Th17 cell differentiation. Additionally, five hub immune-related genes (CD3E, HLA-DRA, IL2RB, ITK and LAT) were identified from the protein-protein interaction network, and sepsis patients with higher expression of hub genes had a better prognosis. Besides, 14 drugs targeting these five hub related genes were revealed on the basis of the DrugBank database, which proved advantageous for treating immune-related diseases.

CONCLUSIONS:

These results strengthen the new understanding of sepsis development and provide a fresh perspective into discriminating the candidate biomarkers for predicting sepsis as well as identifying new drugs for treating sepsis.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Sepsis / Perfilación de la Expresión Génica Tipo de estudio: Diagnostic_studies / Prognostic_studies Límite: Humans Idioma: En Revista: BMC Infect Dis Asunto de la revista: DOENCAS TRANSMISSIVEIS Año: 2024 Tipo del documento: Article País de afiliación: China

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Sepsis / Perfilación de la Expresión Génica Tipo de estudio: Diagnostic_studies / Prognostic_studies Límite: Humans Idioma: En Revista: BMC Infect Dis Asunto de la revista: DOENCAS TRANSMISSIVEIS Año: 2024 Tipo del documento: Article País de afiliación: China
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