ArticleFrontiers in immunology2024
Identification of key biomarkers and therapeutic targets in sepsis through coagulation-related gene expression and immune pathway analysis.
Article in Frontiers in immunology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Identification of immune cell mitochondrial dysfunction characteristics and clinical predictive biomarkers in sepsis via multi-cohort machine learning and single-cell RNA sequencing.Inflammation research : official journal of the European Histamine Research Society ... [et al.] · 2026Article
- Sepsis biomarkers: recent advances and future perspectives.Frontiers in immunology · 2026Review
- Machine learning-based transcriptomic analysis identifies candidate genes in sepsis-induced coagulopathy and explores the immunomodulatory potential of baicalein.Human genomics · 2025Article
- Immunothrombosis in Sepsis: Cellular Crosstalk, Molecular Triggers, and Therapeutic Opportunities-A Review.International journal of molecular sciences · 2025Review
- Association between neutrophil-platelet ratio and 28-day mortality in patients with sepsis: a retrospective analysis based on MIMIC-IV database.BMC infectious diseases · 2025Article
- Multi-cohort validation based on coagulation-related genes for predicting prognosis of esophageal squamous cell carcinoma.Frontiers in immunology · 2025Article
- Association between D-dimer to lymphocyte ratio and in hospital all-cause mortality in elderly patients with sepsis: a cohort of 1123 patients.Frontiers in cellular and infection microbiology · 2024Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sepsis, characterized by a widespread and dysregulated immune response to infection leading to organ dysfunction, presents significant challenges in diagnosis and treatment. In this study, we investigated 203 coagulation-related genes in sepsis patients to explore their roles in the disease. Through differential gene expression analysis, we identified 20 genes with altered expression patterns. Subsequent correlation analysis, visualized through circos plots and heatmaps, revealed significant relationships among these genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses indicated that these genes are involved in immune response activation, coagulation, and immune receptor activity. Disease Ontology (DO) enrichment analysis further linked these genes to autoimmune hemolytic anemia and tumor-related signaling pathways. Additionally, the CIBERSORT analysis highlighted differences in immune cell composition in sepsis patients, revealing an increase in neutrophils and monocytes and a decrease in inactive NK cells, CD8 T cells, and B cells. We employed machine learning techniques, including random forest and SVM, to construct a diagnostic model, identifying FCER1G and FYN as key biomarkers. These biomarkers were validated through their expression levels and ROC curve analysis in an independent validation cohort, demonstrating strong diagnostic potential. Single-cell analysis from the GSE167363 dataset further confirmed the distinct expression profiles of these genes across various cell types, with FCER1G predominantly expressed in monocytes, NK cells, and platelets, and FYN in CD4+ T cells and NK cells. Enrichment analysis via GSEA and ssGSEA revealed that these genes are involved in critical pathways, including intestinal immune networks, fatty acid synthesis, and antigen processing. In conclusion, our comprehensive analysis identifies FCER1G and FYN as promising biomarkers for sepsis, providing valuable insights into the molecular mechanisms of this complex condition. These findings offer new avenues for the development of targeted diagnostic and therapeutic strategies in sepsis management.
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