Network-Based Prioritization of Network-Peripheral Gene Modules in Autism Spectrum Disorder Using Integrative Transcriptomic and Proteomic Data.

Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental condition with complex genetic and molecular architecture. While numerous ASD-associated genes have been identified, how distributed gene modules, particularly network-peripheral genes, contribute to ASD-related biological processes remains incompletely understood. Here, peripheral gene modules refer to genes located at the topological periphery of molecular interaction networks, characterized by non-hub or lower global centrality yet functional connectivity within ASD-relevant pathways, rather than to peripheral tissues or blood-derived samples. We applied an integrative in silico framework combining bulk transcriptomic, proteomic, and single-cell RNA-seq datasets derived from human brain cortex samples. Network-based analyses were performed using STRING and Cytoscape to identify functionally coherent gene modules, with deliberate prioritization of network-peripheral nodes based on distribution-based closeness centrality metrics. Functional enrichment analyses were conducted using Pathway Commons and KEGG, and cell-type-specific expression patterns were evaluated using published single-cell transcriptomic data. Network analysis identified peripheral gene modules associated with synaptic vesicle trafficking, nuclear–cytoplasmic transport, RNA surveillance, ciliary function, apoptosis, and lipid metabolism. Key genes including ITSN1, NUP133, UPF3B, IFT88, and BIRC5 exhibited consistent network connectivity and distinct expression patterns across neuronal and glial cell populations. Enrichment analyses highlighted coordinated involvement of SUMOylation, mRNA processing, and axon guidance pathways. This study presents a reproducible, hypothesis-generating computational framework for prioritizing network-peripheral gene modules relevant to ASD. The findings support a distributed, systems-level model of ASD pathophysiology and identify candidate molecular modules for future experimental and translational investigation.