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By integrating multiple AD gene expression datasets with machine learning approaches\u2014including random forest, XGBoost, LASSO, and SVM\u2014we identified 172 differentially expressed genes, with TUBB2A, RTN4, and YWHAZ emerging as top mutation\u2010associated hub genes. Critically, TUBB2A not only exhibited strong diagnostic potential (AUC = 0.822) but also harbored somatic mutations in our patient cohort, directly linking mutational events to disease manifestation. Unsupervised clustering revealed two distinct AD subtypes: one marked by widespread early gene overexpression and another (Cluster 2) dominated by endoplasmic reticulum stress\u2014likely reflecting divergent mutational landscapes. Pseudotemporal trajectory analysis demonstrated a continuous progression from normal samples to Cluster 2, suggesting that a pivotal mutational event may initiate this transition and accelerate disease progression. These findings underscore the central role of somatic and germline mutations\u2014particularly in TUBB2A\u2014in AD pathogenesis. Our study strongly supports a paradigm shift toward mutation\u2010centric biomarker development and advocates for SNP\u2010based strategies to enable early diagnosis and personalized therapeutic interventions tailored to individual mutational profiles.<\/jats:p>","DOI":"10.1155\/humu\/6627566","type":"journal-article","created":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T11:23:55Z","timestamp":1778585035000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Decoding Pathogenic Mutational Landscapes in Alzheimer\u2032s Disease Through Integrated 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