ArticleJournal of inflammation research2021
Screening of Key Genes of Sepsis and Septic Shock Using Bioinformatics Analysis.
Article in Journal of inflammation research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed, 34 citations in OpenAlex.
- Integrative metabolite-protein interaction networks reveal potential pathways and biomarkers in sepsis.BMC infectious diseases · 2026Article
- Longitudinal blood transcriptome profiling reveals immune dynamics in sepsis.BMC infectious diseases · 2026Article
- A matched case-control study on Escherichia coli factors contributing to sepsis and septic shock in bacteraemic patients.Communications medicine · 2026Article
- ICU-acquired infections and mortality in community-acquired pneumonia-induced sepsis: Insights from a transcriptomic analysis.PloS one · 2026Article
- Exploring ENPP5 as a diagnostic biomarker for sepsis: a comprehensive bioinformatics analysis.BMC infectious diseases · 2025Article
- Immune dysregulation in COVID-19 induced ARDS in kidney transplant recipients revealed by single-cell RNA sequencing.Scientific reports · 2025Article
- Identification of potential crucial genes and biomarkers from neutrophils in sepsis using bioinformatics analysis.Medicine · 2025Article
- Article
- Multi-omics machine learning identifies diagnostic gene signatures and functionally supports PRKACB involvement in macrophage inflammatory responses in sepsis.Frontiers in immunology · 2025Article
- From Molecular Mechanisms to Clinical Therapy: Understanding Sepsis-Induced Multiple Organ Dysfunction.International journal of molecular sciences · 2024Review
- Exploring the biomarkers and potential therapeutic drugs for sepsis via integrated bioinformatic analysis.BMC infectious diseases · 2024Article
- Genetic polymorphisms, biomarkers and signaling pathways associated with septic shock: from diagnosis to therapeutic targets.Burns & trauma · 2024Review
- Bioinformatics Analysis of Gene Expression Profiles for Diagnosing Sepsis and Risk Prediction in Patients with Sepsis.International journal of molecular sciences · 2023Article
- Identification and validation of a novel four-gene diagnostic model for neonatal early-onset sepsis with bacterial infection.European journal of pediatrics · 2023Article
- Integrated analysis of multi-omics data reveals T cell exhaustion in sepsis.Frontiers in immunology · 2023Article
- Classification of subtypes and identification of dysregulated genes in sepsis.Frontiers in cellular and infection microbiology · 2023Article
- Single-Cell RNA Sequencing and Transcriptome Analysis Revealed the Immune Microenvironment and Gene Markers of Acute Respiratory Distress Syndrome.Journal of inflammation research · 2023Article
- Integrative single-cell RNA sequencing and metabolomics decipher the imbalanced lipid-metabolism in maladaptive immune responses during sepsis.Frontiers in immunology · 2023Article
- Identification of key biomarkers in neonatal sepsis by integrated bioinformatics analysis and clinical validation.Heliyon · 2022Article
- Gene expression profiling reveals candidate biomarkers and probable molecular mechanisms in chronic stress.Bioengineered · 2022Article
Corrections and comments
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Authors and funding
11 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveSepsis is a disease associated with high mortality. We performed bioinformatic analysis to identify key biomarkers associated with sepsis and septic shock.
methodsThe top 20% of genes showing the greatest variance between sepsis and controls in the GSE13904 dataset (children) were screened by co-expression network analysis. The differentially expressed genes (DEGs) were identified through analyzing differential gene expression between sepsis patients and control in the GSE13904 (children) and GSE154918 (adult) data sets. Intersection analysis of module genes and DEGs was performed to identify common DEGs for enrichment analysis, protein-protein interaction network (PPI network) analysis, and Short Time-series Expression Miner (STEM) analysis. The PPI network genes were ranked by degree of connectivity, and the top 100 sepsis-associated genes were identified based on the area under the receiver operating characteristic curve (AUC). In addition, we evaluated differences in immune cell infiltration between sepsis patients and controls in children (GSE13904, GSE25504) and adults (GSE9960, GSE154918). Finally, we analyzed differences in DNA methylation levels between sepsis patients and controls in GSE138074 (adults).
resultsThe common genes were associated mainly with up-regulated inflammatory and metabolic responses, as well as down-regulated immune responses. Sepsis patients showed lower infiltration by most types of immune cells. Genes in the PPI network with AUC values greater than 0.9 in both GSE13904 (children) and GSE154918 (adults) were screened as key genes for diagnosis. These key genes (MAPK14, FGR, RHOG, LAT, PRKACB, UBE2Q2, ITK, IL2RB, and CD247) were also identified in STEM analysis to be progressively dysregulated across controls, sepsis patients and patients with septic shock. In addition, the expression of MAPK14, FGR, and CD247 was modified by methylation.
conclusionThis study identified several potential diagnostic genes and inflammatory and metabolic responses mechanisms associated with the development of sepsis.
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