Recent advances in gene network analysis have improved our understanding of complex disease mechanisms; however, interpreting estimated gene networks remains challenging. Existing methods for pathway enrichment analysis focus on gene sets and therefore fail to capture interaction-level information that is critical for understanding disease-related molecular interplays. Here, we propose a novel computational strategy for gene network enrichment analysis (GNEA) that evaluates pathway overrepresentation at the edge level, explicitly incorporating both network structure and the biological importance of hub genes. Thus, our strategy provides reliable biological results. We demonstrated the efficacy of our approach through Monte Carlo simulations of myeloid neoplasms and pan-cancer-related pathway-enriched gene network analysis. The proposed strategy was applied to immune disease pathway-enriched gene network analysis. Our results identify inflammatory bowel disease-related pathways enriched in both acute myeloid leukemia (AML)-aged and AML-young networks, and asthma-related pathways enriched in healthy-young networks. Our results suggested that "activation of CD40 and CD40LG" and "mutual activation between HLA-DPB1 and IL4R" are potential markers to uncover AML-related mechanisms. Overall, this study demonstrates that GNEA provides a powerful framework for uncovering biologically meaningful interaction-level insights into complex diseases.
Park et al. (2026) studied this question.