{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T13:04:53Z","timestamp":1789563893165,"version":"build-2803163510"},"reference-count":34,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T00:00:00Z","timestamp":1785456000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T00:00:00Z","timestamp":1785456000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Cancer Reports"],"published-print":{"date-parts":[[2026,8]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background, Aims<\/jats:title>\n                    <jats:p>Gastric cancer (GC) pathogenesis involves complex molecular interactions that remain incompletely understood at a biological systems level. While microRNA\u201021 (miR\u201021), matrix metalloproteinases (MMPs), and vascular endothelial growth factor (VEGF) have been individually implicated in GC, their integrated role as a coordinated regulatory network remains uncharacterized. And we aim to reveal novel biology systems level insights into the miR\u201021\/MMP\/VEGF regulatory axis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In this study, 27 tumor and matched adjacent noncancerous tissues in GC patients were analyzed. Expression of miR\u201021, MMP\u20102\/9, and VEGF was analyzed using qRT\u2010PCR, Western blotting, and zymography (for MMP\u20102\/9 activity). Computational analyses integrated KEGG pathway enrichment (clusterProfiler), random forest machine learning, structural equation modeling (lavaan), and protein\u2013protein interaction network analysis (igraph) to decipher system\u2010level organization. All statistical analyses and computational modeling were performed using RStudio version 4.4.2.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Pathway analysis revealed significant enrichment in cancer\u2010related pathways, including MicroRNAs in cancer (\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20098.66\u2009\u00d7\u200910\n                      <jats:sup>\u221210<\/jats:sup>\n                      , FDR\u2009=\u20092.09\u2009\u00d7\u200910\n                      <jats:sup>\u22127<\/jats:sup>\n                      ). Random forest classification achieved a mean cross\u2010validated accuracy of 85.0% (\u00b114.8%) in discriminating tumor from normal tissues, identifying miR\u201021 as the most potent predictor. Structural equation modeling demonstrated a significant direct effect of miR\u201021 on tumor size (\u03b2\u2009=\u20090.576,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.001) and an indirect effect mediated through MMP\u20109 (\u03b2\u2009=\u20090.194,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.047). Network topology analysis confirmed miR\u201021 as the central hub (degree\u2009=\u20095).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Our integrated biology systems analysis suggests that the miR\u201021\/MMP\u20109\/VEGF axis may represent a candidate regulatory network in GC, with miR\u201021 appearing to play a central role in tumor progression. These findings offer preliminary insights and highlight molecules of interest for further studies for intervention in GC pathogenesis.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/cnr2.70634","type":"journal-article","created":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T05:12:08Z","timestamp":1785561128000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Integrated Network Analysis Suggests an\n                    <scp>miR<\/scp>\n                    \u201021\/\n                    <scp>MMP<\/scp>\n                    \/\n                    <scp>VEGF<\/scp>\n                    \u2010Associated Regulatory Axis in Gastric Cancer"],"prefix":"10.1002","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6714-5309","authenticated-orcid":false,"given":"Fatemeh","family":"Babajani","sequence":"first","affiliation":[{"name":"Department of Clinical Biochemistry, Medical School Kermanshah University of Medical Sciences  Kermanshah Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4428-7675","authenticated-orcid":false,"given":"Hadi","family":"Mozafari","sequence":"additional","affiliation":[{"name":"Department of Clinical Biochemistry, Medical School Kermanshah University of Medical Sciences  Kermanshah Iran"},{"name":"Medical Biology Research Center Kermanshah University of Medical Sciences  Kermanshah Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,7,31]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21660"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1158\/1055-9965.EPI-13-1057"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21657"},{"key":"e_1_2_10_5_1","first-page":"375","article-title":"Cancer Systems Biology: From Molecular Profiles to Pathways, Signalling Networks, and Therapeutic Vulnerabilities","author":"Verbeke L.","year":"2019","journal-title":"Oxford Textbook of Cancer Biology"},{"key":"e_1_2_10_6_1","article-title":"Constructing Predictive Cancer Systems Biology Models","author":"Rohrs J. 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