Sepsis is a life-threatening syndrome associated with high morbidity and mortality, underscoring the urgent need for early diagnostic biomarkers and therapeutic targets. However, current diagnostic strategies remain insufficiently precise because of the complex immune dysregulation and immune microenvironment heterogeneity that characterize sepsis. This study aimed to identify reliable diagnostic biomarkers for sepsis and explore their immune regulatory mechanisms together with potential therapeutic relevance using multidimensional bioinformatic analyses.
Single-cell transcriptomic and bulk RNA sequencing datasets were integrated to screen candidate diagnostic genes for sepsis. Immune infiltration, co-expression network and pathway enrichment analyses were performed to explore immune regulatory mechanisms. Machine-learning approaches were used to validate the diagnostic signature, and molecular docking was conducted to predict candidate targeted compounds.
A total of 346 differentially expressed genes were identified and were mainly enriched in immune, coagulation, and metabolic pathways. CIBERSORT and single-cell analyses revealed increased neutrophils, monocytes, and γδ T cells and reduced CD8+ T cells and resting natural killer cells. Four diagnostic genes ( S100A12 , CD22 , CSTA , and UPP1 ) were prioritized. The four-gene model showed robust external performance (area under the receiver operating characteristic curve = 0.860; sensitivity = 0.781; specificity = 0.780), and interpretability analysis highlighted UPP1 and S100A12 as dominant predictors. Molecular docking suggested potential interactions between these targets and anti-inflammatory compounds.
In summary, by integrating bioinformatics and multiple machine-learning algorithms, we identified four diagnostic genes for sepsis in patient whole-blood samples. These signature genes suggest that immune dysregulation, metabolic derangement, and uncontrolled apoptosis leading to organ failure may be key mechanisms in sepsis development and progression. Combined WGCNA, CIBERSORT, and single-cell sequencing analyses indicated that the regulatory effects of these diagnostic genes on immune cells may contribute to sepsis progression and that their expression levels can distinguish patients with sepsis from healthy individuals. In addition, we developed a diagnostic model that may help support individualized risk assessment and treatment optimization after further clinical validation. These findings provide new insights into the diagnosis and management of sepsis.
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https://www.xiahepublishing.com/2590-3438/JTCCM-2025-00027
The study was recently published in the Journal of Translational Critical Care Medicine .
Journal of Translational Critical Care Medicine (JTCCM), an open access publication academically supported by The First Affiliated Hospital of Harbin Medical University, is a peer-reviewed online journal with continuous print on demand compilation of issues published. The journal allows free access (Open Access) to its contents and permits authors to self-archive final accepted version of the articles on any OAI-compliant institutional/subject-based repository.
The journal covers technical and clinical studies related to health, ethical and social issues in field of Critical Care Medicine, Translational Medicine, Respirology, Infection, Tumor, Surgery. Articles with clinical interest and implications will be given preference.
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Journal of Translational Critical Care Medicine
Immune Cell Communication Networks and Machine Learning-based Diagnostic Signatures in Sepsis: Insights from Single-cell RNA Sequencing and Cross-dataset Validation