ObjectiveTo integrate multi-cohort transcriptomic, single-cell, and experimental data to identify diagnostic signature genes for septic shock, establish a peripheral blood molecular diagnostic model, and elucidate the m6A regulatory mechanisms of key genes.MethodsCandidate genes were identified from five GEO peripheral blood cohorts through batch effect-corrected differential expression analysis and WGCNA, followed by parallel GO/DO enrichment analysis. Feature genes were selected using PPI networks combined with LASSO, SVM-RFE, and random forest algorithms.
A 5-gene artificial neural network (ANN) diagnostic model was constructed and validated using ROC and logistic regression in GSE95233, GSE131761, and clinical cohorts. Immune cell composition and expression of characteristic genes in neutrophils were analyzed using CIBERSORT and GSE167363 single-cell data.
The METTL14/YTHDF1–S100A12 m6A axis was elucidated via qRT–PCR, Western blot, MeRIP-qPCR, RIP-qPCR, and Actinomycin D experiments. In CLP mice, siMETTL14 was administered for in vivo intervention and assessment of lung injury.ResultsA total of 76 sepsis-shock-associated candidate genes were identified, enriched in the bacterial defense pathway.
Five robust candidate genes (S100A12, MMP8, PGLYRP1, CEACAM8, MMP9) were selected by integrating PPI and three machine learning algorithms.
Frontiers in Immunology published a clinical update in Infectious Disease on 25 May 2026.
The item focuses on Multi-cohort transcriptomics integration for building and validating a diagnostic model of peripheral blood septic shock.
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