ArticlePloS one2021
Immune gene expression networks in sepsis: A network biology approach.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed, 24 citations in OpenAlex.
- Article
- Integrated bioinformatics analysis of molecular signatures and therapeutic targets in early sepsis.Frontiers in cellular and infection microbiology · 2026Article
- Quantifying pathway perturbations based on gene network rewiring in sepsis progression toward death or survival.Frontiers in bioinformatics · 2026Article
- Metabolic profiles of squamous cell lung carcinoma and diagnostic model construction.Scientific reports · 2025Article
- miRNA sequencing identifies immune-associated miRNAs and highlights the role of miR-193b-5p in sepsis and septic shock progression.Scientific reports · 2025Article
- Identification and validation of mFrontiers in immunology · 2025Article
- The role of m6A methylation genes in predicting poor prognosis in sepsis: identifying key biomarkers and therapeutic targets.European journal of medical research · 2024Article
- Immune Gene Networks from Lung Cancer Patients Treated with Immune Checkpoint Inhibitors.Biomedicines · 2024Article
- Comprehensive Comparison of the Capacity of Functionalized Sepharose, Magnetic Core, and Polystyrene Nanoparticles to Immuno-Precipitate Procalcitonin from Human Material for the Subsequent Quantification by LC-MS/MS.International journal of molecular sciences · 2023Article
- Metabolic profiles of lung adenocarcinoma via peripheral blood and diagnostic model construction.Scientific reports · 2023Article
- Inhibition of ITK Signaling Causes Amelioration in Sepsis-Associated Neuroinflammation and Depression-like State in Mice.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
- A simplified machine learning model utilizing platelet-related genes for predicting poor prognosis in sepsis.Frontiers in immunology · 2023Article
- Indirect Method for Estimation of Reference Intervals of Inflammatory Markers.Annals of laboratory medicine · 2023Article
- KLF17 is an important regulatory component of the transcriptomic response of Atlantic salmon macrophages toFrontiers in immunology · 2023Article
- Identifying Tumor-Associated Genes from Bilayer Networks of DNA Methylation Sites and RNAs.Life (Basel, Switzerland) · 2022Article
- Performance evaluation of presepsin using a Sysmex HISCL-5000 analyzer and determination of reference interval.Journal of clinical laboratory analysis · 2022Article
- Identification of Immune-Related Key Genes as Potential Diagnostic Biomarkers of Sepsis in Children.Journal of inflammation research · 2022Article
- Identification of a novel sepsis prognosis model and analysis of possible drug application prospects: Based on scRNA-seq and RNA-seq data.Frontiers in immunology · 2022Article
- Bioinformatics Analysis Identifies TNFRSF1A as a Biomarker of Liver Injury in Sepsis TNFRSF1A is a Biomarker for Septic Liver Injury.Genetics research · 2022Article
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To study the dysregulated host immune response to infection in sepsis, gene expression profiles from the Gene Expression Omnibus (GEO) datasets GSE54514, GSE57065, GSE64456, GSE95233, GSE66099 and GSE72829 were selected. From the Kyoto Encyclopedia of Genes and Genomes (KEGG) immune system pathways, 998 unique genes were selected, and genes were classified as follows based on gene annotation from KEGG, Gene Ontology, and Reactome: adaptive immunity, antigen presentation, cytokines and chemokines, complement, hematopoiesis, innate immunity, leukocyte migration, NK cell activity, platelet activity, and signaling. After correlation matrix formation, correlation coefficient of 0.8 was selected for network generation and network analysis. Total transcriptome was analyzed for differentially expressed genes (DEG), followed by gene set enrichment analysis. The network topological structure revealed that adaptive immunity tended to form a prominent and isolated cluster in sepsis. Common genes within the cluster from the 6 datasets included CD247, CD8A, ITK, LAT, and LCK. The clustering coefficient and modularity parameters were increased in 5/6 and 4/6 datasets in the sepsis group that seemed to be associated with functional aspect of the network. GSE95233 revealed that the nonsurvivor group showed a prominent and isolated adaptive immunity cluster, whereas the survivor group had isolated complement-coagulation and platelet-related clusters. T cell receptor signaling (TCR) pathway and antigen processing and presentation pathway were down-regulated in 5/6 and 4/6 datasets, respectively. Complement and coagulation, Fc gamma, epsilon related signaling pathways were up-regulated in 5/6 datasets. Altogether, network and gene set enrichment analysis showed that adaptive-immunity-related genes along with TCR pathway were down-regulated and isolated from immune the network that seemed to be associated with unfavorable prognosis. Prominence of platelet and complement-coagulation-related genes in the immune network was associated with survival in sepsis. Complement-coagulation pathway was up-regulated in the sepsis group that was associated with favorable prognosis. Network and gene set enrichment analysis supported elucidation of sepsis pathogenesis.
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