A meta-refined human Alzheimer's disease-associated gene subset shows partial mouse-model pathway correspondence and limited cross-cohort machine-learning transportability.

INTRODUCTION

Common Alzheimer's disease (AD) mouse models are widely used, but their molecular correspondence with human AD remains uncertain.

METHODS

We analyzed 15 post mortem human brain datasets, four human non-brain or in vitro sensitivity datasets, and nine AD-related mouse-model molecular-profiling datasets; GSE222494 was analyzed separately for single-nucleus localization. Refined107, a meta-refined 107-gene human AD-associated gene subset, was evaluated using recurrence-matched control sampling, independent pathway-level comparison, within cohort out-of-fold (OOF) classification, and ordered cross-cohort transportability.

RESULTS

Refined107 comprised 37 Tier 1 and 70 Tier 2 genes and showed greater statistical consistency than recurrence-matched controls. Functional annotation implicated inflammatory, blood-brain barrier, metabolic, neuronal, and developmental programs. Among 636 recurrent human pathways, 226 showed same-direction support in at least one evaluated mouse-model family, indicating partial and model-dependent human-mouse correspondence. Post-selection within-cohort OOF areas under the curve (AUCs) were 0.833 to 0.980, whereas ordered cross-cohort AUCs were 0.288 to 0.720.

DISCUSSION: Refined107 represents a statistically consistent, meta-refined human AD-associated gene subset rather than a comprehensive disease signature. Human-mouse pathway correspondence was partial and model-dependent, and within-cohort classification did not translate into robust cross-cohort transportability.