ArticleFrontiers in immunology2023
Diagnostic and predictive values of pyroptosis-related genes in sepsis.
Article in Frontiers in immunology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 16 citations in OpenAlex.
- Systematic characterization of pyroptosis-related gene patterns identifies potential prognostic inflammatory phenotypes in sepsis.Functional & integrative genomics · 2026Article
- Identification and experimental validation of biomarkers associated with integrated stress response in sepsis.Biochemistry and biophysics reports · 2026Article
- Article
- Development and validation of a nomogram for predicting thrombocytopenia in sepsis patients treated with linezolid.BMC pharmacology & toxicology · 2026Article
- Pyroptosis endotypes and nonlinear biomarker-mortality relationships in older adults with community-acquired pneumonia: the amplifying role of malnutrition.Frontiers in immunology · 2026Article
- Bioinformatics analysis reveals the association of bile acid metabolism-related genes with sepsis.PloS one · 2026Article
- The protective role of MDL-1 in sepsis-induced lung injury: insights from a murine CLP model.Frontiers in immunology · 2026Article
- SerpinB1 reduces neutrophil airway inflammation, airway remodeling, and pyroptosis in asthmatic mice by interacting with Elane.Human cell · 2025Article
- Machine learning based screening of biomarkers associated with cell death and immunosuppression of multiple life stages sepsis populations.Scientific reports · 2025Article
- Single-cell RNA sequencing technology was employed to construct a risk prediction model for genes associated with pyroptosis and ferroptosis in lung adenocarcinoma.Respiratory research · 2025Article
- Article
- Cell-type-specific regulators landscape and regulatory mechanisms underlying pyroptosis in uterine corpus endometrial carcinoma.Journal of Cancer · 2025Article
- Heterogeneity of Neutrophils and Immunological Function in Neonatal Sepsis: Analysis of Molecular Subtypes Based on Hypoxia-Glycolysis-Lactylation.Mediators of inflammation · 2025Article
- Pyroptosis-Related Molecular Clusters and Immune Infiltration in Pediatric Sepsis.Pediatric health, medicine and therapeutics · 2025Article
- Toll-like receptors in health and disease.MedComm · 2024Review
- Predictive Value of a Diagnostic Five-Gene Biomarker for Pediatric Sepsis.Journal of inflammation research · 2024Article
- Exploring the Role of Different Cell-Death-Related Genes in Sepsis Diagnosis Using a Machine Learning Algorithm.International journal of molecular sciences · 2023Article
- Mitigation of Sepsis-Induced Acute Lung Injury by BMSC-Derived Exosomal miR-125b-5p Through STAT3-Mediated Suppression of Macrophage Pyroptosis.International journal of nanomedicine · 2023Article
- Lysosome-Related Diagnostic Biomarkers for Pediatric Sepsis Integrated by Machine Learning.Journal of inflammation research · 2023Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Sepsis is an organ dysfunction syndrome caused by the body's dysregulated response to infection. Yet, due to the heterogeneity of this disease process, the diagnosis and definition of sepsis is a critical issue in clinical work. Existing methods for early diagnosis of sepsis have low specificity. Aims: This study evaluated the diagnostic and predictive values of pyroptosis-related genes in normal and sepsis patients and their role in the immune microenvironment using multiple bioinformatics analyses and machine-learning methods. Methods: Pediatric sepsis microarray datasets were screened from the GEO database and the differentially expressed genes (DEGs) associated with pyroptosis were analyzed. DEGs were then subjected to multiple bioinformatics analyses. The differential immune landscape between sepsis and healthy controls was explored by screening diagnostic genes using various machine-learning models. Also, the diagnostic value of these diagnosis-related genes in sepsis (miRNAs that have regulatory relationships with genes and related drugs that have regulatory relationships) were analyzed in the internal test set and external test. Results: Eight genes (CLEC5A, MALT1, NAIP, NLRC4, SERPINB1, SIRT1, STAT3, and TLR2) related to sepsis diagnosis were screened by multiple machine learning algorithms. The CIBERSORT algorithm confirmed that these genes were significantly correlated with the infiltration abundance of some immune cells and immune checkpoint sites (all Conclusion: The pyroptosis-related genes identified and verified in this study may provide a useful reference for the prediction and assessment of sepsis.
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