Leveraging Diverse Regulated Cell Death Patterns to Identify Diagnosis Biomarkers for Alzheimer's Disease.

Background: The functions of regulated cell death (RCD) are closely related to Alzheimer's disease (AD). However, very few studies have systematically investigated the diagnosis and immunologic role of RCD-related genes in AD patients.

Methods: 8 multicenter AD cohorts were included in this study, and then were merged into a meta cohort. Then, an unsupervised clustering analysis was carried out to detect unique subtypes of AD based on RCD-related genes. Subsequently, differently expressed genes (DEGs) and weighted correlation network analysis (WGCNA) between subtypes were identified. Finally, to establish an optimal risk model, an RCD.score was constructed by using computational algorithm (10 machine-learning algorithms, 113 combinations).

Results: We identified two distinct subtypes based on RCD-related genes, each exhibiting distinct hallmark pathway activity and immunologic landscape. Specifically, cluster.A patients had a higher immune infiltration, a higher immune modulators and poor AD progression. Utilizing the shared DEGs and WGCNA of these subtypes, we constructed an RCD.score that demonstrated excellent predictive ability in AD across multiple datasets. Furthermore, RCD.score was identified to exhibit the strongest association with poor AD progression. Mechanistically, we observed activation of signaling pathways and effective immune infiltration and immune modulators in the high RCD.score group, thus leading to a poor AD progression. Additionally, Mendelian randomization screening revealed four genes (CXCL1, ENTPD2, METTL7A, and SERPINB6) as feature genes for AD.

Conclusion: The RCD model is a valuable tool in categorizing AD patients. This model can be of great assistance to clinicians in determining the most suitable personalized treatment plan for each individual AD patient.