Transcriptome and experimental verification identified candidate biomarkers related to mitochondrial metabolism in sepsis-associated encephalopathy

BACKGROUND: Mitochondrial metabolism (MM) abnormalities have been implicated in multiple diseases, but its contribution to sepsis-associated encephalopathy (SAE) remains insufficiently understood. The purpose of this research was to explore the candidate biomarkers associated with MM in SAE and the underlying mechanisms. METHODS: The relevant transcriptome data were acquired from the public databases. MM-related genes (MM-RGs) were scoured from published articles. Candidate biomarker identification integrated differential expression profiling, PPI network construction, machine learning algorithms, ROC curve evaluation, and expression quantification. Immune infiltration, multilayer perceptron (MLP) network, Gene Set Enrichment Analysis (GSEA), molecular regulatory network, drug prediction, and molecular docking were applied to probe the mechanisms of candidate biomarkers in SAE. The expression levels of candidate biomarkers were further validated in brain tissues of the LPS induced SAE mouse model. RESULTS: Four MM-related genes-INSIG1, SREBF1, CIDEC, and PNPLA3-were identified as candidate biomarkers, and their expression patterns in the rodent model were consistent with bioinformatics predictions. Within the GSE135838 dataset, the MLP model demonstrated good performance in distinguishing SAE from control samples. Differential immune cells included resting mast cells, naïve B cells, and activated natural killer (NK) cells. Gene set enrichment analysis (GSEA) indicated significant enrichment in the allograft rejection pathway. The regulatory network involved transcription factors (e.g., THRB), miRNAs (e.g., hsa-mir-29c-3p), and lncRNAs (e.g., KCNQ1OT1). Furthermore, experimental validation confirmed that these candidate biomarkers were significantly downregulated in SAE samples. CONCLUSION: This study identified and experimentally validated four MM-related candidate biomarkers in SAE, revealing their potential roles in immune dysregulation. These candidate biomarkers require further validation in independent cohorts and in a more diverse range of animal models before clinical translation.