Identification of Gene Signatures and Molecular Mechanisms Underlying the Comorbidity of Alzheimer's Disease and Crohn's Disease Using Machine Learning.

OBJECTIVE: Alzheimer's disease (AD) and Crohn's disease (CD) both involve inflammation and immune dysregulation, yet the potential molecular mechanisms underlying their comorbidity remain unclear. METHODS: We integrated transcriptomic data from AD and CD patients and applied differential expression analysis, weighted gene coexpression network analysis, protein-protein interaction networks, and multiple machine learning approaches to identify key comorbidity genes. Functional enrichment, single-cell sequencing validation, and virtual knockout analyses were used to explore their biological roles. Molecular docking was performed to evaluate the binding affinity of candidate small-molecule drugs to the identified core genes. RESULTS: CXCL1 and IGFBP5 were identified as core comorbidity genes. CXCL1 was associated with inflammatory signaling, including cytokine receptor binding, neutrophil migration, and NOD-like receptor signaling. IGFBP5 was linked to growth factor binding, smooth muscle cell proliferation, and extracellular matrix-receptor interactions. Single-cell and virtual knockout analyses indicated that these genes play pivotal roles in inflammation, immune regulation, cell migration, and tissue remodeling, potentially bridging central and peripheral inflammation via the gut-brain axis and IgSF CAM signaling. Candidate drug prediction and molecular docking suggested that small molecules such as Dasatinib, Mifepristone, and Retinoic acid may modulate these pathways. CONCLUSION: This study reveals the critical roles of CXCL1 and IGFBP5 in AD-CD comorbidity, providing a theoretical basis for exploring gut-brain axis mechanisms and potential targeted interventions.