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Area of Science:

  • Neuroscience and Genetics
  • Computational Biology and Bioinformatics
  • Biomarker Discovery

Background:

  • Alzheimer's Disease (AD) poses a significant global health challenge due to a lack of effective early diagnostic methods.
  • Multi-omics technologies and artificial intelligence offer novel avenues for developing predictive models for AD.
  • Biomarker-based approaches are crucial for improving the early detection of Alzheimer's Disease.

Purpose of the Study:

  • To integrate multi-omics data for identifying core Alzheimer's Disease risk genes with potential causal links.
  • To develop a robust early diagnostic model for Alzheimer's Disease using machine learning.
  • To provide a theoretical framework for precision intervention strategies in Alzheimer's Disease.

Main Methods:

  • Integrated Mendelian Randomization (MR), differential expression analysis, and Weighted Gene Co-Expression Network Analysis (WGCNA) for risk gene discovery.
  • Applied eight machine learning algorithms and constructed a Nomogram predictive model based on informative features.
  • Validated the model in an independent cohort and performed Gene Set Enrichment Analysis (GSEA) for mechanistic insights.

Main Results:

  • Identified VASP, PIP4K2A, RRP36, METTL7A, and AP2M1 as key diagnostic genes, with RRP36 and PIP4K2A consistently emerging as core risk genes.
  • The five-gene Nomogram model demonstrated high diagnostic performance (AUC = 0.964) in the validation cohort.
  • GSEA suggested RRP36's role in cytoskeletal remodeling and neuroinflammation, and PIP4K2A's involvement in synaptic dysfunction.

Conclusions:

  • This study pioneers the integration of MR, gene expression, and WGCNA with machine learning for AD risk gene discovery and diagnostic model development.
  • RRP36 and PIP4K2A are identified as core risk genes potentially driving AD progression through cytoskeletal changes, neuroinflammation, and synaptic impairment.
  • The validated five-gene panel offers novel biomarkers and methodological support for early Alzheimer's Disease screening and precision intervention.