A novel multivariate framework for functional gene networks enrichment analysis
Gene network analysis is critically implicated in disease research for uncovering functional modules and interaction-driven pathways underlying biological and disease processes. However, the interpretation of large inferred networks remains challenging. Although functional gene network analysis allows the interpretation of large inferred networks, challenges such as reduction of multiple network-level features to a single composite score often limit their application. This data reduction can mask the important multivariate characteristics of gene networks, hindering efficient differentiation of individual contributions of distinct network components. Hence, this study aimed to investigate a novel computational strategy called Multivariate Framework for Functional Gene Network Enrichment Analysis (mFGNA). This framework incorporated diverse network-level features from a graph-theoretical perspective, including node properties (centrality), edge connectivity patterns (Jaccard distance), interaction strengths (edge weights), alongside traditional expression levels. Notably, mFGNA preserved these multidimensional characteristics, capturing complex rewiring of gene networks across different phenotypic states. Furthermore, mFGNA adopted a gene-level permutation strategy to evaluate the enrichment hypothesis, ensuring effective statistical inference and reduced computational complexity compared with phenotype-based permutations. Extensive Monte Carlo simulations validated mFGNA through both undirected and directed gene networks, showing consistently improved performance over existing approaches across diverse pathway settings. We also applied mFGNA to investigate immune pathway perturbations in cancer cell lines and identified significant network-level dysregulation in pancreatic and non-small cell lung cancers. Cancer-specific interaction modules were dominated by human leukocyte antigen class II genes. Meanwhile, normal cell networks were characterized by hub genes such as MMP1 and MMP3 that were implicated in tissue maintenance, highlighting immune remodeling in tumors and the potential molecular targets for developing diagnostic and therapeutic interventions. Overall, the study shows that mFGNA enables effective functional pathway discovery in complex gene networks, providing mechanistic insights and potential translational targets in disease contexts.