Integrated multi omics and machine learning reveal mitochondrial immunometabolic networks in sepsis associated encephalopathy.

ABSTRACT: Sepsis-associated encephalopathy (SAE) is a major complication in intensive care units, characterized by diffuse brain dysfunction due to systemic inflammation. Despite advances in critical care medicine, SAE remains a key factor in poor patient outcomes, with its pathogenesis closely related to mitochondrial damage and the release of mitochondrial DNA (mtDNA). In this study, we integrated multiple transcriptomic and single-cell sequencing datasets to comprehensively analyze mitochondrial-associated differentially expressed genes (MitoDEGs) in SAE brain tissues. Using machine learning algorithms, we identified three core biomarkers (ALDH7A1, HOGA1, and AA467197). Functional enrichment analysis showed that the upregulated genes in SAE were mainly involved in immune and inflammatory response

SUBMITTER: Zhang Z 

PROVIDER: S-EPMC12480024 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature