Multi-platform integration of brain and CSF proteomes reveals biomarker panels for Alzheimer's disease.

Alzheimer's disease (AD) is the leading cause of dementia and represents a progressive, irreversible neurodegenerative disorder. Given the complexity and heterogeneity of AD, which involves numerous interrelated molecular pathways, large-scale proteomics datasets are essential for robust biomarker discovery. Comprehensive proteomic profiling enables the unbiased identification of novel biomarkers across diverse biological processes, thereby increasing the likelihood of finding sensitive and specific candidates for early diagnosis and therapeutic targeting. In this study, we analyzed 28 large-scale proteomics datasets obtained from the AD Knowledge Portal and published studies. The data comprise tandem mass tag, label-free quantification, and proximity extension assay measurements from brain tissue and cerebrospinal fluid. To enhance analytical power, we integrated these proteomic profiles with corresponding clinical information to construct comprehensive feature sets for subsequent machine learning analysis. Using Random Forest and Logistic Regression models, we identified a panel of proteins capable of distinguishing AD patients from healthy controls. Several of these biomarkers have been previously validated in the context of AD, while others represent novel candidates not yet reported as AD-associated. These newly identified biomarkers warrant further experimental validation and hold promise for improving early diagnosis as well as guiding the development of targeted therapies for AD.