Potential Mitochondria-Related Key Genes in Post-Traumatic Stress Disorder Analyzed by Machine Learning Methods.

Purpose: Post-traumatic stress disorder (PTSD) is a debilitating psychiatric disorder triggered by exposure to traumatic events. Emerging evidence suggests that mitochondrial dysfunction may contribute to PTSD pathogenesis by disrupting cellular energy metabolism, increasing oxidative stress, and impairing neuroplasticity. This study investigates mitochondrial dysfunction-associated biomarkers, potentially opening new avenues for targeted therapeutic approaches. Methods: Gene expression matrices from datasets GSE199841 and GSE81761 were derived from peripheral blood samples, used to identify differentially expressed genes (DEGs) between PTSD patients and healthy controls. Functional annotation and enrichment analysis were carried out using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. Next, least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE), and random forest were employed to screen and prioritize potential biomarkers, followed by validation through receiver operating characteristic (ROC) analysis and independent cohort from human verification. We also examined the expression of mitoDEGs in peripheral blood from PTSD-related mouse models by RT-qPCR. Given the close interplay between mitochondrial metabolism and immune function, we investigated the relationship between key MitoDEGs and immune infiltration proportions. Results: DEGs in PTSD were enriched in mitochondrial pathways, including mitochondrial ribosome function and nitric oxide synthase regulation. Machine learning identified Conclusion: Mitochondrial-related genes