{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T16:12:54Z","timestamp":1786119174617,"version":"build-2736575974"},"reference-count":132,"publisher":"Public Library of Science (PLoS)","issue":"8","license":[{"start":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T00:00:00Z","timestamp":1785974400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:sec id=\"sec001\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Diffuse Gastric Cancer (DGC) is an aggressive subtype with a poor prognosis and a lack of specific biomarkers, representing a critical unmet need in oncology. This study aimed to elucidate the key molecular drivers of DGC by integrating RNA-seq data with a multi-faceted bioinformatics approach.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec002\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We analyzed RNA-seq data from young-onset DGC and normal tissues (GSE113255, GSE122401). Machine learning (ML) feature selection (ReliefF algorithm) was used to prioritize genes, followed by protein-protein interaction network analysis to identify hub genes. Their roles were further investigated through Gene Ontology, KEGG pathway analysis, tumor microenvironment immune infiltration, miRNA-regulatory network analysis, transcription factor prediction, and computational drug repurposing analyses.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec003\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Our ML\u2011driven approach identified seven hub genes central to DGC pathogenesis including\n                      <jats:italic>CCL5<\/jats:italic>\n                      ,\n                      <jats:italic>CXCR4<\/jats:italic>\n                      ,\n                      <jats:italic>MMP9<\/jats:italic>\n                      ,\n                      <jats:italic>FOXP3<\/jats:italic>\n                      ,\n                      <jats:italic>IL18<\/jats:italic>\n                      ,\n                      <jats:italic>TNFSF11<\/jats:italic>\n                      , and\n                      <jats:italic>TNFSF13B<\/jats:italic>\n                      . Among these, three prioritized core hub genes (\n                      <jats:italic>CXCR4<\/jats:italic>\n                      ,\n                      <jats:italic>MMP9<\/jats:italic>\n                      , and\n                      <jats:italic>TNFSF13B<\/jats:italic>\n                      ) were selected based on statistically significant overexpression and complementary functional roles. Specifically,\n                      <jats:italic>CXCR4<\/jats:italic>\n                      showed a Fold Change of 3.12 (Log\n                      <jats:sub>2<\/jats:sub>\n                      FC\u2009=\u20091.64, FDR\u2009=\u20090.01),\n                      <jats:italic>MMP9<\/jats:italic>\n                      exhibited the highest magnitude of upregulation (Fold Change\u2009=\u200916.58, Log\n                      <jats:sub>2<\/jats:sub>\n                      FC\u2009=\u20094.05), and\n                      <jats:italic>TNFSF13B<\/jats:italic>\n                      demonstrated the most statistically significant differential expression (Fold Change\u2009=\u20092.32, Log\n                      <jats:sub>2<\/jats:sub>\n                      FC\u2009=\u20091.21, FDR\u2009&lt;\u20090.000001). High\n                      <jats:italic>CXCR4<\/jats:italic>\n                      expression was identified as a potential prognostic indicator associated with poorer overall survival in the TCGA\u2011STAD cohort (HR\u2009=\u20091.5, p\u2009=\u20090.0072). We delineated a core regulatory circuitry where NF\u2011\u03baB (NFKB1\/RELA) masterfully regulates the hub gene network. Drug repurposing analysis nominated several FDA\u2011approved agents, including the VEGF\u2011A inhibitor Bevacizumab, which indirectly suppresses\n                      <jats:italic>CXCR4<\/jats:italic>\n                      and\n                      <jats:italic>MMP9<\/jats:italic>\n                      .\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec004\">\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>\n                      This pilot study establishes a robust integrative framework that synergizes ML with network biology. It nominates\n                      <jats:italic>CXCR4<\/jats:italic>\n                      and\n                      <jats:italic>TNFSF13B<\/jats:italic>\n                      as candidate therapeutic targets and highlights the potential of ML\u2011based feature selection for discovering biologically relevant, context\u2011dependent prognostic indicators in DGC. However, we emphasize that these findings are exploratory and hypothesis\u2011generating, requiring independent validation through experimental studies and larger cohorts before any clinical translation.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1371\/journal.pone.0354961","type":"journal-article","created":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T17:43:23Z","timestamp":1786038203000},"page":"e0354961","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Uncovering the molecular landscape of young-onset diffuse gastric cancer: A relieff-based feature selection analysis on RNA-Seq data"],"prefix":"10.1371","volume":"21","author":[{"given":"Zahra Khalili","family":"Azni","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1845-2748","authenticated-orcid":true,"given":"Matia 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