Identifying Hub Genes of Alzheimer’s and Parkinson’s Diseases via h-cutoff: A New Methodological Approach

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Abstract

Background: Alzheimer’s disease (AD) and Parkinson’s disease (PD) are genetically complex neurodegenerative disorders in which disease-associated genes act through coordinated immune, glial, proteostatic, metabolic, synaptic and vascular programs rather than through isolated single-gene effects. Weighted co-expression and co-occurrence networks are therefore attractive for prioritizing disease genes, but conventional hard thresholds can remove low-weight edges that carry high structural importance. Objective: This manuscript refines the original h-metrics terminology into an explicit h-cutoff framework for weighted biological networks. The revised procedure uses co-expression or co-occurrence values as edge weights, applies a one-order h-cutoff to obtain an h-subnet, adds weak bridge edges to form an h-backbone, and then applies a two-order h-cutoff within the h-backbone to identify final Hub genes. Methods: The method was formalized for non-integer edge weights by extending the integer h-strength definition to a continuous h-strength through scaling and interpolation in rank space. The analytical design was organized into AD/PD-shared, AD-specific and PD-specific layers using public resources, including GSE48350, GSE26927, GSE174367, GSE161045, NIAGADS and related AD/PD transcriptomic and genetic studies. The mathematical workflow was written as a reproducible edge-to-backbone-to-gene pipeline with numbered formulas, clearly separating functional edge strength, structural weak-bridge value and final node-level Hub gene selection. Results: The shared AD/PD h-backbone prioritized CXCR4, FLT1, HSPB1, HSPA1A, CALM3, CDC42, RAB3A, TREM2 and APOE, supporting a convergent neurodegenerative architecture involving neuroinflammation, proteostasis, glial lipid biology and synaptic/cytoskeletal remodeling. The AD-specific layer prioritized DLAT, CCDC88B, SREBF1, APOE, CLU, TREM2 and TYROBP, consistent with mitochondrial stress and glial lipid/immune regulation. The PD-specific layer prioritized PSMB8, GRIA1, RGS8, BAG3, SYN1, CALB2, SNCA and TH, consistent with proteasome/chaperone stress, synaptic signaling, alpha-synuclein biology and dopaminergic vulnerability. Conclusions: Continuous h-cutoff provides an adaptive, interpretable and reproducible alternative to arbitrary correlation thresholding. It preserves the original h-backbone principle of integrating functional and structural network information while adapting h-strength to non-integer biological edge weights. The revised method is suitable for gene-prioritization studies in AD, PD and other complex polygenic diseases.

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