A Meta-analysis to Identify Common Key Genes Across Ageing, Alzheimer's and Parkinson's Diseases.

Background: Ageing (AG) is associated with cognitive decline and an increased risk of developing neurodegenerative diseases (NDs) like Alzheimer's disease (AD) and Parkinson's disease (PD). While individual diseases have been widely studied, cross-condition convergence at the transcriptomic and regulatory levels has not been systematically defined.

Objective: To identify a conserved molecular core shared across AG, AD and PD and to understand its functional and regulatory architecture using integrative network biology.

Methods: Four independent human brain transcriptomic datasets (n = 173 samples) representing AG, AD and PD were analysed using false discovery rate correction (FDR < 0.05). Genes commonly dysregulated across all conditions were identified via intersection analysis. Functional enrichment, protein-protein interaction (PPI) network analysis, and microRNA (miRNA) regulatory mapping were performed using clusterProfiler, STRING and multiMiR frameworks.

Results: A conserved set of 142 genes was identified across AG, AD and PD, with 94.4% exhibiting consistent directionality of regulation. AG clustered transcriptionally closer to AD than PD, while PD displayed stronger amplitude of dysregulation. Functional enrichment analysis revealed dominant involvement in synaptic signalling, axonal transport, vesicle trafficking and calcium homeostasis. Network analysis identified three essential regulatory hubs, CALM3, CDC42 and RAB3A. They are critical to neuronal signalling and cytoskeletal dynamics. miRNA analysis revealed coordinated regulation of hub genes by disease-associated miRNAs, including miR-29, miR-34, miR-7 and miR-195, and identified shared disease-associated regulators across AG, AD and PD conditions.

Conclusion: This study defines a shared neurodegenerative molecular core that bridges physiological AG with pathological neurodegeneration. The integration of transcriptomic, network, and miRNA analyses reveals systems-level convergence and identifies key regulatory nodes as attractive targets for cross-disease therapeutic strategies.