Multi-omics machine learning identifies diagnostic gene signatures and functionally supports PRKACB involvement in macrophage inflammatory responses in sepsis.

ABSTRACT: Sepsis is a life-threatening condition caused by a dysregulated immune response, often leading to organ failure and death. Diagnosis and therapy remain challenging. This study aimed to identify biomarkers for sepsis through multi-omics analysis and experimental validation. A total of 1,166 samples from the GEO repository underwent differential analysis, WGCNA, and logistic regression to identify sepsis-associated features. After SVM-RFE screening, a 28-gene signature distinguishing sepsis from healthy controls achieved an AUC of 0.970 (sensitivity 0.939, specificity 1.000) in an independent cohort and 0.870 (sensitivity 0.906, specificity 0.700) in qRT-PCR validation. A 13-gene signature distinguishing sepsis from SIRS achieved an AUC of 1.000 (sensitivity 1.000, specificity 1.000) and 0.7

SUBMITTER: Yang L 

PROVIDER: S-EPMC12852012 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature