ArticleComputational and structural biotechnology journal2023
Identification of potential diagnostic and prognostic biomarkers for sepsis based on machine learning.
Article in Computational and structural biotechnology journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 14 citations in OpenAlex.
- Early biomarkers for predicting sepsis-induced shock: insights from inflammatory pathways and immune response.Frontiers in pharmacology · 2026Review
- Diagnostic and Prognostic Biomarkers for Sepsis-Associated Liver Injury: Current Status and Future Perspectives.Gastroenterology research and practice · 2026Review
- The role of four ERS-related key genes in sepsis induced organ dysfunction and the forewarning model construction.BMC medical informatics and decision making · 2025Article
- Role of Endoplasmic Reticulum Stress-Associated Genes in Septic Neonatal Foals.Antioxidants (Basel, Switzerland) · 2025Article
- Clinical manifestations of anxiety and depression in sepsis-associated encephalopathy and multi-omics identification of cluster of differentiation 38 as an early biomarker.World journal of psychiatry · 2025Article
- MTHFD2 stabilizes LOX expression through RNA methylation modification to promote sepsis-induced acute kidney injury progression.Human cell · 2025Article
- Identification of DNA damage repair-related genes in sepsis using bioinformatics and machine learning: An observational study.Medicine · 2025Observational
- Machine learning-based transcriptmics analysis revealsFrontiers in pharmacology · 2025Article
- Maternal perinatal risk factors and full-term neonatal early-onset sepsis: A novel prediction model based on machine learning.Digital healthArticle
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: To identify potential diagnostic and prognostic biomarkers of the early stage of sepsis. Methods: The differentially expressed genes (DEGs) between sepsis and control transcriptomes were screened from GSE65682 and GSE134347 datasets. The candidate biomarkers were identified by the least absolute shrinkage and selection operator (LASSO) regression and support vector machine recursive feature elimination (SVM-RFE) analyses. The diagnostic and prognostic abilities of the markers were evaluated by plotting receiver operating characteristic (ROC) curves and Kaplan-Meier survival curves. Gene Set Enrichment Analysis (GSEA) and single-sample GSEA (ssGSEA) were performed to further elucidate the molecular mechanisms and immune-related processes. Finally, the potential biomarkers were validated in a septic mouse model by qRT-PCR and western blotting. Results: Eleven DEGs were identified between the sepsis and control samples, including YOD1, GADD45A, BCL11B, IL1R2, UGCG, TLR5, S100A12, ITK, HP, CCR7 and C19orf59 (all AUC>0.9). Furthermore, the survival analysis identified YOD1, GADD45A, BCL11B and IL1R2 as the prognostic biomarkers of sepsis. According to GSEA, four DEGs were significantly associated with immune-related processes. In addition, ssGSEA demonstrated a significant difference in the enriched immune cell populations between the sepsis and control groups (all Conclusions: We identified four potential immune-releated diagnostic and prognostic gene markers for sepsis that offer new insights into its underlying mechanisms.
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