ArticleFrontiers in cellular and infection microbiology2026
Machine learning identifies PPARG as a diagnostic biomarker for sepsis linked to CD14/NF-κB signaling: integrated transcriptomics and experimental validation.
Article in Frontiers in cellular and infection microbiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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6 authors.
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Abstract
Background: Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection. Early diagnosis remains challenging due to substantial clinical and biological heterogeneity. CD14 is a central pattern-recognition receptor in innate immune activation, but the downstream network linking CD14 to immunometabolic regulation remains incompletely defined. We aimed to identify CD14-associated blood-based diagnostic biomarkers for sepsis and explore potential regulatory mechanisms. Methods: Whole-blood transcriptomic datasets were retrieved from the Gene Expression Omnibus. GSE236713 was the discovery cohort and GSE65682 the external validation cohort. Candidate genes were identified through overlap of differentially expressed genes between Results: Five feature genes ( Conclusion: By integrating machine learning with experimental validation, this study prioritized PPARG as a diagnostic biomarker for sepsis and provided supportive evidence for its association with CD14/NF-κB signaling. These findings offer a basis for developing host-response-based diagnostic signatures and further investigation of PPARG-related immunometabolic regulation in sepsis.
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