Gene Network Enrichment Analysis and Its Application to Explore Enriched Immune Disease Pathways for Gene Network of Acute Myeloid Leukemia Cell Lines.

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 -related pathways enriched in both acute myeloid leukemia (AML)-aged and AML-young networks, and -related pathways enriched in healthy-young networks. Our results suggested that "activation of and " and "mutual activation between and " 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.