Machine learning and bioinformatics framework integration reveal potential characteristic genes related to immune cell infiltration in post-traumatic stress disorder.

ABSTRACT: Post-traumatic stress disorder (PTSD) is a psychological condition with a high incidence rate and widespread influence, but its pathophysiological mechanism is unknown, and existing treatment options have limited efficacy. Exploration of potential biomarkers and therapeutic targets for PTSD aids in the understanding of its pathogenesis and therapeutic strategies. Human PTSD sample data were retrieved from public databases, merged, and corrected to eliminate bias. The differentially expressed genes (DEGs) were then evaluated using bioinformatics tools, and the pathological mechanism was investigated using GO, KEGG, DO, and GSEA enrichment analyses. Furthermore, key genes were identified using machine learning methods, and gene interaction was investigated by establishing a protein-protein i

SUBMITTER: Qi P 

PROVIDER: S-EPMC12808217 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature