Bioinformatics Screening and Experimental Validation for Deciphering the Immune Signature of Late-Onset Depression

Late-onset depression (LOD) is often associated with more severe cognitive impairment and a higher risk of disability and suicide. Emerging evidence suggests that immune system problems may be involved. This study aims to systematically characterize the genetic signature of LOD based on the immune landscape.

The expression profile of GSE76826 was obtained from the Gene Expression Omnibus (GEO) database to gather gene expression data for 10 LOD patients and 12 healthy controls (HC). Various analyses, such as Single-Sample Gene Set Enrichment Analysis (ssGSEA) and Weighted Gene Co-expression Network Analysis (WGCNA), were used to mine key genes closely related to LOD. ImmuCellAI helped us understand differences in the immune environment between LOD patients and controls, and we used an LOD animal model to validate the relevant immune characteristics.

We found enriched immune pathways linked to LOD and adaptive immune responses. Using advanced bioinformatics techniques, we identified two key genes: apelin (APLN) and leptin (LEP), which have good diagnostic efficacy (AUC=0.925, 95% CI=1.00-0.83) for LOD. Neutrophil infiltration increased significantly in LOD, while CD8+ T lymphocytes (CD8_T) decreased. We finally constructed an animal model of LOD, validated two key genes and microglia marker genes in blood and hippocampus, and detected elevated pro-inflammatory factors such as interleukin-6 (IL-6) and tumor necrosis factor-α (TNF-α).

We identified and validated the presence of aberrant expression of APLN and LEP in LOD and described a possible immune mechanism involving increased release of IL-6 and TNF-α, leading to decreased CD8_T infiltration and increased neutrophil infiltration. Meanwhile, peripheral inflammation across the blood-brain barrier further promotes microglia activation, leading to neuronal damage.