{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T20:31:38Z","timestamp":1792096298231,"version":"4.4.0"},"reference-count":87,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,11,3]],"date-time":"2025-11-03T00:00:00Z","timestamp":1762128000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007129","name":"Natural Science Foundation of Shandong Province","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shandong Province"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Med."],"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Anoikis, a recently identified type of programmed cell death analogous to apoptosis, has been implicated in the pathogenesis of Sj\u00f6gren\u2019s syndrome (SS). Although accumulating evidence indicates its involvement in modulating immune responses and contributing to SS progression, the precise role of anoikis in SS remains inadequately understood. This study aimed to explore anoikis-related genes (ARGs) and their molecular mechanisms in SS using public databases.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      SS datasets (GSE23117, GSE84844 and GSE12795) were retrieved from the GEO database. In total, 924 ARGs were extracted from the GeneCards and Harmonizome databases, followed by differential expression gene (DEGs) analysis and weighted gene co-expression network analysis (WGCNA). Machine learning algorithms were utilized to screen candidate biomarkers, and their diagnostic effectiveness was assessed using receiver operating characteristic (ROC) curve analysis. Concurrently, a mouse model of SS was established and validated through\n                      <jats:italic>in vivo<\/jats:italic>\n                      experiments. Immune cell infiltration in SS tissues was evaluated using CIBERSORT, and correlations between characteristic genes and immune cell profiles were analyzed. Potential drug candidates targeting these genes were identified using the DGIdb database. Subsequently, an lncRNA-miRNA-mRNA network associated with these genes was constructed, and preliminary experimental validation was conducted.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      A total of 35 differentially expressed anoikis-related genes (DEARGs) were identified. GO and KEGG enrichment analyses demonstrated that DEARGs were primarily associated with inflammation, viral infections, and the necroptosis signaling pathway. Machine learning analysis pinpointed 14 feature genes, among seven were associated with cancer (\n                      <jats:italic>NAT1<\/jats:italic>\n                      ,\n                      <jats:italic>BIRC3<\/jats:italic>\n                      ,\n                      <jats:italic>EZH2<\/jats:italic>\n                      ,\n                      <jats:italic>MAD2L1<\/jats:italic>\n                      ,\n                      <jats:italic>ATP2A3<\/jats:italic>\n                      ,\n                      <jats:italic>HMGA1<\/jats:italic>\n                      , and\n                      <jats:italic>BST2<\/jats:italic>\n                      ). Given the unclear roles of\n                      <jats:italic>SKI<\/jats:italic>\n                      and\n                      <jats:italic>PRDX4<\/jats:italic>\n                      in SS, the study focused specifically on five relevant genes,\n                      <jats:italic>MAPK3, IL15, S100A9, IFI27<\/jats:italic>\n                      , and\n                      <jats:italic>CXCL10<\/jats:italic>\n                      , which were validated by\n                      <jats:italic>in vivo<\/jats:italic>\n                      experiments. Immune cell analysis revealed increased proportions of B cells, T cells, macrophages, and other immune cells in SS tissues. Furthermore, ceRNA and drug-gene interaction networks were established, underscoring the regulatory significance of five key miRNAs (miR-30b-5p, miR-148a-3p, miR-130a, miR-483-5p, and miR-486-3p) in SS. In addition, eight candidate drugs were identified with potential for modulating SS pathogenesis.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>\n                      This study substantiates the significant involvement of anoikis in SS and suggests that\n                      <jats:italic>MAPK3, IL15, S100A9, IFI27<\/jats:italic>\n                      , and\n                      <jats:italic>CXCL10<\/jats:italic>\n                      may serve as critical biomarkers in the inflammatory progression of SS. These genes likely mediate their effects by influencing immune cell infiltration, participating in immune regulation, and modulating inflammatory responses. Our findings offer new insights into drug selection and immunotherapeutic strategies for SS.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmed.2025.1661259","type":"journal-article","created":{"date-parts":[[2025,11,3]],"date-time":"2025-11-03T06:23:49Z","timestamp":1762151029000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Identification of anoikis-related genes and immune infiltration characteristics in Sj\u00f6gren\u2019s syndrome based on machine learning"],"prefix":"10.3389","volume":"12","author":[{"given":"Lei","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziqi","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinpeng","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengjie","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,11,3]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"2778","DOI":"10.3390\/biomedicines11102778","article-title":"Circulating CD8+ T cell subsets in primary Sj\u00f6gren's 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