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Updated: Jan 17, 2026

A Data-Driven Approach to Quantifying Immune States in Sepsis
Published on: February 7, 2025
Single-Cell Multi-Omics Deciphers Core Gene Networks and Immune Interaction Collapse in Sepsis-Associated T Cell
Xiang Li1, Zhibin Chen1, Yandong Yao1
1Department of Emergency Medicine, The Affiliated Hospital, Southwest Medical University, Luzhou, 646000, People's Republic of China.
Introduction:
Sepsis is a life-threatening condition characterized by immune dysregulation, yet the mechanisms underlying T cell dysfunction remain poorly understood.
Methods:
We integrated multi-omics data from public GEO datasets and prospective cohorts. Single-cell transcriptomic analysis was applied to identify core genes, followed by diagnostic and prognostic validation. Cell-cell interaction networks were constructed to investigate signaling alterations, and cross-platform validation was conducted.
Results:
Seven core genes (LTB, CD3D, TRAF3IP3, CD3G, GZMM, HLA-DPB1, CD3E) were identified, showing strong diagnostic value (AUC ≥ 0.86) and prognostic significance (HR=4.50 for CD3E). Network analysis revealed collapse of critical signaling axes (HLA-DRA-MHCII, ITGB2-CD226) and aberrant activation of inhibitory pathways (LGALS9-CD45), leading to a "co-stimulation inhibition-checkpoint activation" imbalance. Cross-platform validation confirmed conserved downregulation of these genes in sepsis, which contributed to immune exhaustion via disrupted T cell differentiation trajectories and impaired intercellular communication.
Conclusion:
Our findings highlight novel biomarkers and potential therapeutic targets for sepsis immunotherapy by systematically deciphering core gene networks and immune interaction collapse in T cell dysfunction.
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