ArticleJournal of healthcare engineering2022
Analysis of Sepsis Markers and Pathogenesis Based on Gene Differential Expression and Protein Interaction Network.
Article in Journal of healthcare engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 10 citations in OpenAlex.
- Nanomaterial-nucleic acid probe synergy: accelerating rapid pathogen detection and antimicrobial susceptibility testing in bloodstream infections.Folia microbiologica · 2026Review
- Bioinformatics identification and experimental validation of hub genes associated with narcolepsy type 1.Journal of clinical and translational science · 2026Article
- Machine learning-based transcriptomic analysis identifies candidate genes in sepsis-induced coagulopathy and explores the immunomodulatory potential of baicalein.Human genomics · 2025Article
- Identification of immune infiltration and immune-related biomarkers of periprosthetic joint infection.Heliyon · 2024Article
- Identification and validation of a novel four-gene diagnostic model for neonatal early-onset sepsis with bacterial infection.European journal of pediatrics · 2023Article
- Circulating sepsis-related metabolite sphinganine could protect against intestinal damage during sepsis.Frontiers in immunology · 2023Article
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
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Abstract
objectiveThe purpose of the present study is to screen the hub genes associated with sepsis, comprehensively understand the occurrence and progress mechanism of sepsis, and provide new targets for clinical diagnosis and treatment of sepsis.
methodsThe microarray data of GSE9692 and GSE95233 were downloaded from the Gene Expression Omnibus (GEO) database. The dataset GSE9692 contained 29 children with sepsis and 16 healthy children, while the dataset GSE95233 included 102 septic subjects and 22 healthy volunteers. Differentially expressed genes (DEGs) were screened by GEO2R online analysis. The DAVID database was applied to conduct functional enrichment analysis of the DEGs. The STRING database was adopted to acquire protein-protein interaction (PPI) networks.
resultsWe identified 286 DEGs (217 upregulated DEGs and 69 downregulated DEGs) in the dataset GSE9692 and 357 DEGs (236 upregulated DEGs and 121 downregulated DEGs) in the dataset GSE95233. After the intersection of DEGs of the two datasets, a total of 98 co-DEGs were obtained. DEGs associated with sepsis were involved in inflammatory responses such as T cell activation, leukocyte cell-cell adhesion, leukocyte-mediated immunity, cytokine production, immune effector process, lymphocyte-mediated immunity, defense response to fungus, and lymphocyte-mediated immunity. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis suggested that sepsis was connected to bacterial and viral infections. Through PPI network analysis, we screened the most important hub genes, including ITK, CD247, MMP9, CD3D, MMP8, KLRK1, and GZMK.
conclusionsIn conclusion, the present study revealed an unbalanced immune response at the transcriptome level of sepsis and identified genes for potential biomarkers of sepsis, such as ITK, CD247, MMP9, CD3D, MMP8, KLRK1, and GZMK.
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