{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T23:00:44Z","timestamp":1784847644345,"version":"3.55.0"},"reference-count":32,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T00:00:00Z","timestamp":1784851200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Genet."],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3389\/fgene.2026.1871376","type":"journal-article","created":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T22:10:25Z","timestamp":1784844625000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A novel multivariate framework for functional gene networks enrichment analysis"],"prefix":"10.3389","volume":"17","author":[{"given":"Heewon","family":"Park","sequence":"first","affiliation":[{"name":"School of Mathematics, Statistics and Data Science, Sungshin Women\u2019s University","place":["Seoul, Republic of Korea"]},{"name":"Human Genome Center, Institute of Medical Science, University of Tokyo","place":["Tokyo, Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seiya","family":"Imoto","sequence":"additional","affiliation":[{"name":"M&D Data Science Center, Institute of Science Tokyo","place":["Tokyo, Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,7,24]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"e0316716","DOI":"10.1371\/journal.pone.0316716","article-title":"MAP1LC3C repression reduces CIITA- and HLA class II expression in non-small cell lung cancer","volume":"20","author":"Barbeau","year":"2025","journal-title":"PLoS One"},{"key":"B2","doi-asserted-by":"publisher","first-page":"289","DOI":"10.1111\/j.2517-6161.1995.tb02031.x","article-title":"Controlling the false discovery rate: a practical and powerful approach to multiple testing","volume":"57","author":"Benjamini","year":"1995","journal-title":"J. R. Stat. Soc. Ser. B"},{"key":"B3","doi-asserted-by":"publisher","first-page":"e17","DOI":"10.1093\/nar\/gkad1187","article-title":"Gene set correlation enrichment analysis for interpreting and annotating gene expression profiles","volume":"52","author":"Chang","year":"2024","journal-title":"Nucleic Acids Res."},{"key":"B4","doi-asserted-by":"publisher","first-page":"103975","DOI":"10.1016\/j.compbiomed.2020.103975","article-title":"Advantages of CEMiTool for gene co-expression analysis of RNA-seq data","volume":"125","author":"Cheng","year":"2020","journal-title":"Comput. Biol. Med."},{"key":"B5","doi-asserted-by":"publisher","first-page":"2837","DOI":"10.1158\/1078-0432.ccr-20-3655","article-title":"Spatial analysis and clinical significance of HLA Class-I and Class-II subunit expression in non-small cell lung cancer","volume":"27","author":"Datar","year":"2021","journal-title":"Clin. Cancer Res."},{"key":"B6","doi-asserted-by":"crossref","first-page":"P3","DOI":"10.1186\/gb-2003-4-5-p3","article-title":"DAVID: database for annotation, visualization, and integrated discovery","volume":"4","author":"Dennis","year":"2003","journal-title":"Genome Biol."},{"key":"B7","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1016\/s0888-7543(02)00021-6","article-title":"Global functional profiling of gene expression","volume":"81","author":"Draghici","year":"2003","journal-title":"Genomics"},{"key":"B8","doi-asserted-by":"publisher","first-page":"100001","DOI":"10.1016\/j.jtocrr.2020.100001","article-title":"The role of interleukin 1\u03b2 in the pathogenesis of lung cancer","volume":"1","author":"Garon","year":"2020","journal-title":"JTO Clin. Res. Rep."},{"key":"B9","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1038\/s41392-025-02124-y","article-title":"Tissue macrophages: origin, heterogeneity, biological functions, diseases and therapeutic targets","volume":"10","author":"Guan","year":"2025","journal-title":"Signal Transduct. Target Ther."},{"key":"B10","doi-asserted-by":"publisher","first-page":"105072","DOI":"10.1016\/j.critrevonc.2025.105072","article-title":"Targeting the CCL2\/CCR2 axis in colorectal cancer: immunopharmacological perspectives and therapeutic strategies","volume":"218","author":"Hajibabaie","year":"2026","journal-title":"Crit. Rev. Oncol. Hematol."},{"key":"B11","first-page":"1","article-title":"gsean: gene set enrichment analysis using networks","author":"Jung","year":"2024","journal-title":"R. Package Version"},{"key":"B12","doi-asserted-by":"publisher","first-page":"1676","DOI":"10.4251\/wjgo.v16.i5.1676","article-title":"Interleukin-1\u03b2: friend or foe for gastrointestinal cancers","volume":"16","author":"Khawkhiaw","year":"2024","journal-title":"World J. Gastrointest. Oncol."},{"key":"B13","first-page":"4303","article-title":"A new network node similarity measure method and its applications","volume":"1403","author":"Li","year":"2014","journal-title":"arXiv"},{"key":"B14","doi-asserted-by":"publisher","first-page":"775","DOI":"10.1111\/1759-7714.12643","article-title":"High levels of CCL2 or CCL4 in the tumor microenvironment predict unfavorable survival in lung adenocarcinoma","volume":"9","author":"Li","year":"2018","journal-title":"Thorac. Cancer"},{"key":"B15","doi-asserted-by":"publisher","first-page":"101221","DOI":"10.1016\/j.tranon.2021.101221","article-title":"Targeting matrix metalloproteinase MMP3 greatly enhances oncolytic virus mediated tumor therapy","volume":"14","author":"Liang","year":"2021","journal-title":"Transl. Oncol."},{"key":"B16","doi-asserted-by":"publisher","first-page":"96","DOI":"10.3892\/ijo.2024.5684","article-title":"Drug resistance and tumor immune microenvironment: an overview of current understandings","volume":"65","author":"Liu","year":"2024","journal-title":"Int. J. Oncol."},{"key":"B17","doi-asserted-by":"publisher","first-page":"909","DOI":"10.1080\/17520363.2025.2571394","article-title":"Diagnostic valuation of MMP family members as biomarkers for pancreatic cancer: a systematic review and meta-analysis","volume":"19","author":"Lu","year":"2025","journal-title":"Biomark. Med."},{"key":"B18","doi-asserted-by":"publisher","first-page":"btaf378","DOI":"10.1093\/bioinformatics\/btaf378","article-title":"Gene behaviors-based network enrichment analysis and its application in revealing immune disease pathways enriched with COVID-19 severity-specific gene networks","volume":"41","author":"Park","year":"2025","journal-title":"Bioinformatics"},{"key":"B19","doi-asserted-by":"publisher","first-page":"10244","DOI":"10.1038\/s41467-025-64839-4","article-title":"Cancer cell-derived IL-1\u03b2 reverses chemo-immunotherapy resistance in non-small cell lung cancer","volume":"16","author":"Perrichet","year":"2025","journal-title":"Nat. Commun."},{"key":"B20","doi-asserted-by":"publisher","first-page":"e1171447","DOI":"10.1080\/2162402X.2016.1171447","article-title":"HLA class II antigen-processing pathway in tumors: molecular defects and clinical relevance","volume":"6","author":"Seliger","year":"2017","journal-title":"Oncoimmunology"},{"key":"B21","doi-asserted-by":"publisher","first-page":"352","DOI":"10.1186\/s12859-016-1203-6","article-title":"NEAT: an efficient network enrichment analysis test","volume":"17","author":"Signorelli","year":"2016","journal-title":"BMC Bioinforma."},{"key":"B22","doi-asserted-by":"publisher","first-page":"15545","DOI":"10.1073\/pnas.0506580102","article-title":"Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles","volume":"102","author":"Subramanian","year":"2005","journal-title":"Proc. Natl. Acad. Sci. U. S. A."},{"key":"B23","doi-asserted-by":"publisher","first-page":"13544","DOI":"10.1073\/pnas.0506577102","article-title":"Discovering statistically significant pathways in expression profiling studies","volume":"102","author":"Tian","year":"2005","journal-title":"Proc. Natl. Acad. Sci. U. S. A."},{"key":"B24","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","article-title":"Regression shrinkage and selection via the lasso","volume":"58","author":"Tibshirani","year":"1996","journal-title":"J. R. Stat. Soc. Ser. B"},{"key":"B25","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1186\/s12859-019-2716-6","article-title":"MGSEA - a multivariate gene set enrichment analysis","volume":"20","author":"Tiong","year":"2019","journal-title":"BMC Bioinforma."},{"key":"B26","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1038\/s41392-025-02280-1","article-title":"Immune evasion in cancer: mechanisms and cutting-edge therapeutic approaches","volume":"10","author":"Tufail","year":"2025","journal-title":"Signal Transduct. Target Ther."},{"key":"B27","doi-asserted-by":"publisher","first-page":"503","DOI":"10.3390\/insects14060503","article-title":"Gene Co-Expression network analysis reveals key regulatory genes in Metisa plana hormone pathways","volume":"14","author":"Vengatharajuloo","year":"2023","journal-title":"Insects"},{"key":"B28","doi-asserted-by":"publisher","first-page":"1103","DOI":"10.3892\/or.2020.7490","article-title":"Knockdown of MMP-1 inhibits the progression of colorectal cancer by suppressing the PI3K\/Akt\/c-myc signaling pathway and EMT","volume":"43","author":"Wang","year":"2020","journal-title":"Oncol. Rep."},{"key":"B29","doi-asserted-by":"publisher","first-page":"1009462","DOI":"10.3389\/fgene.2023.1009462","article-title":"Weighted gene co-expression network analysis identifies key hub genes and pathways in acute myeloid leukemia","volume":"14","author":"Wang","year":"2023","journal-title":"Front. Genet."},{"key":"B30","doi-asserted-by":"publisher","first-page":"e13115","DOI":"10.1111\/cpr.13115","article-title":"Role of the CCL2-CCR2 signalling axis in cancer: mechanisms and therapeutic targeting","volume":"54","author":"Xu","year":"2021","journal-title":"Cell Prolif."},{"key":"B31","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1111\/j.1467-9868.2005.00503.x","article-title":"Regularization and variable selection via the elastic net","volume":"67","author":"Zou","year":"2005","journal-title":"J. R. Stat. Soc. Ser. B"},{"key":"B32","doi-asserted-by":"publisher","first-page":"2173","DOI":"10.1214\/009053607000000127","article-title":"On the \u201cdegrees of freedom\u201d of the lasso","volume":"35","author":"Zou","year":"2007","journal-title":"Ann. Stat."}],"container-title":["Frontiers in Genetics"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fgene.2026.1871376\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T22:10:27Z","timestamp":1784844627000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fgene.2026.1871376\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,24]]},"references-count":32,"alternative-id":["10.3389\/fgene.2026.1871376"],"URL":"https:\/\/doi.org\/10.3389\/fgene.2026.1871376","relation":{},"ISSN":["1664-8021"],"issn-type":[{"value":"1664-8021","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7,24]]},"article-number":"1871376"}}