Identification of potential immune-related diagnostic biomarkers for major depressive disorder and ischemic stroke using bioinformatics analysis

Ischemic stroke (IS) and major depressive disorder (MDD) are the leading causes of mortality and disability worldwide. Emerging evidence suggests a link between IS and MDD. However, the underlying molecular mechanisms remain unclear. We aimed to explore the common susceptibility genes and underlying mechanisms of IS and MDD. A retrospective cohort study was conducted to analyze the history of depression in patients with IS. We identified co-susceptible genes for the two diseases through differential expression gene analysis, weighted gene co-expression network analysis (WGCNA), and machine learning methods, and established a diagnostic nomogram for these two diseases. We compared immune infiltration in patients with IS, MDD, and controls, and analyzed the correlation between co-susceptible genes and immune cells. Finally, the expression levels of co-susceptible genes were confirmed using animal models; subsequent functional analyses and targeted small-molecule drug screening were conducted on these genes. This retrospective cohort study found a history of depression among patients with IS. Bioinformatics analysis identified five co-susceptible genes ( CD163 , FOLR3 , GADD45A , ECHDC2 , and RPLP0 ), and their expression levels were validated in a mouse model. A diagnostic nomogram based on these genes demonstrated high predictive accuracy for both IS and MDD. This is the first study to use bioinformatics and machine learning methods to screen for co-susceptible genes between IS and MDD and construct a diagnostic model to establish genetic and immune associations linking the two diseases, providing new insights for their prevention and diagnosis.