{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T11:06:07Z","timestamp":1789124767617,"version":"build-2803163510"},"reference-count":42,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T00:00:00Z","timestamp":1789084800000},"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. Immunol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Atherosclerosis (AS) is a chronic inflammatory vascular disease characterized by immune dysregulation, metabolic disturbance, and progressive vascular remodeling. Although anoikis resistance has been extensively investigated in cancer biology, its involvement in immune-cell persistence and plaque progression in AS remains poorly understood.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We integrated two publicly available bulk transcriptomic datasets (GSE100927 and GSE28829) to identify differentially expressed genes (DEGs) and performed weighted gene co-expression network analysis (WGCNA) to determine disease-associated gene modules. Anoikis-related candidate genes were obtained by intersecting DEGs, WGCNA-derived module genes, and a curated anoikis-related gene set, and were further prioritized using least absolute shrinkage and selection operator (LASSO) regression and random forest algorithms. Immune infiltration and cell type\u2013specific expression patterns were evaluated using CIBERSORT and single-cell RNA sequencing (scRNA-seq) analyses. The expression of the leading candidate was validated using both\n                      <jats:italic>in vitro<\/jats:italic>\n                      and\n                      <jats:italic>in vivo<\/jats:italic>\n                      experiments.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      We identified eight core anoikis-related hub genes\u2014HAVCR2, CD36, PLAU, TNF, PYCARD, SERPINA1, BLNK, and CHI3L1\u2014that were closely associated with AS. These genes were enriched in inflammatory and metabolic pathways and showed strong diagnostic performance across validation analyses, with AUC values greater than 0.83. Immune deconvolution and single-cell transcriptomic analyses revealed that the anoikis-related signature was predominantly localized to macrophage and T-cell compartments within atherosclerotic lesions. Among the identified candidates, HAVCR2 showed the most consistent disease-associated pattern. HAVCR2 expression was markedly increased in oxLDL-stimulated THP-1-derived macrophages and was enriched in macrophage-rich atherosclerotic lesions in high-fat diet\u2013fed ApoE\n                      <jats:sup>\u2212\/\u2212<\/jats:sup>\n                      mice, supporting its association with lipid-induced macrophage activation and vascular inflammation.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study defines a macrophage-centered anoikis-related immune signature in atherosclerosis and identifies HAVCR2, together with CD36, PLAU, TNF, PYCARD, SERPINA1, BLNK, and CHI3L1, as potential biomarkers and candidate targets for future diagnostic and therapeutic investigation.<\/jats:p>\n                  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