Cross-Tissue WGCNA Reveals Conserved Brain-Blood Transcriptomic Signatures and Potential Biomarkers for Asymptomatic Alzheimer's Disease Diagnosis

Background Alzheimer’s disease (AD) is a neurodegenerative disorder characterised by a prolonged prodromal stage, often resulting in late diagnosis and limited therapeutic efficacy. Early diagnosis at the asymptomatic stage (AsymAD), before irreversible synaptic loss, is therefore critical. Although peripheral blood offers potential for early AD detection, biomarker discovery is limited by systemic signal dilution, dependence on symptomatic cohorts, and conventional differential expression analysis, which fails to detect subtle prodromal alterations. Methods To address these methodological gaps, a multi-tiered, cross-tissue Weighted Gene Co-Expression Network Analysis (WGCNA) pipeline was developed. Unbiased co-expression networks anchored in the entorhinal cortex of AsymAD patients were constructed, and cross-tissue preservation in peripheral blood was assessed. Prioritised hub genes from the preserved module were used to train a Random Forest classifier, which was validated on an independent peripheral blood microarray dataset. Results Of ten modules identified, one module (green) was preserved in peripheral blood (Z-summary = 15.25). This module, associated with dysregulation of mitochondrial energy and proteostasis pathways, was refined into a five-gene screening panel ( ATP6AP2 , CKS1B , STAMBPL1 , SUB1 , and BET1 ). A Random Forest classifier trained with this panel distinguished AsymAD patients from healthy controls, achieving an AUC of 0.722 and an accuracy of 69.1%. Conclusion The findings present anatomically anchored and computationally predictive cross-tissue transcriptomic signatures that warrant further investigation as potential blood-based early biomarkers for AD diagnosis.