[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128325-en":3,"doc-seo-128325-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128325,962085564381,"Clementine","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Identification of anoikis-related genes and immune infiltration characteristics in Sjögren’s syndrome based on machine learning - Original Research","This original research investigates how anoikis, a programmed cell death process analogous to apoptosis, contributes to Sjögren’s syndrome (SS) by identifying anoikis-related genes and clarifying their links to immune infiltration. Public SS datasets from GEO were integrated with gene extraction, differential expression, and weighted gene co-expression network analyses. Machine learning selected candidate biomarkers validated by ROC analysis and in vivo mouse experiments. Immune infiltration was quantified using CIBERSORT, while ceRNA, drug–gene interactions, and network construction supported miRNA-driven regulatory roles.","OPEN ACCESS  \nEDITED BY  \nSandra I. Anjo,  \nCentre for Innovative Biomedicine and Biotechnology-University of Coimbra (CIBB-UC), Portugal  \nREVIEWED BY  \nJianan Zhao,  \nTemple University, United States Ping Jiang,  \nShanghai Jiao Tong University, China  \n*CORRESPONDENCE  \nMengjie Wang  \n [wmjzy1996@163.com](wmjzy1996@163.com)[ ](wmjzy1996@163.com)Ying Liu  \n [lytt_1994@163.com](lytt_1994@163.com)[ ](lytt_1994@163.com)RECEIVED 07 July 2025 ACCEPTED 09 October 2025 PUBLISHED 03 November 2025 CORRECTED 19 November 2025  \nCITATION  \nWang L, Xu Z, Zhou X, Liu Y and Wang M (2025) Identification of  \nanoikis-related genes and immune infiltration characteristics in Sjögren’s syndrome based on machine learning.  \nFront. Med. 12:1661259.  \ndoi: 10.3389/fmed.2025.1661259  \nCOPYRIGHT  \n© 2025 Wang, Xu, Zhou, Liu and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nTYPE Original Research PUBLISHED 03 November 2025 DOI 10.3389/fmed.2025.1661259  \nIdentification of anoikis-related genes and immune infiltration characteristics in Sjögren’s syndrome based on machine learning  \nLei Wang 1, Ziqi Xu 1, Xinpeng Zhou 1, Ying Liu 1* and Mengjie Wang 1,2*  \n1 Department of Rheumatology, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, China, 2 Institute of Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan, China  \nObjective: Anoikis, a recently identified type of programmed cell death analogous to apoptosis, has been implicated in the pathogenesis of Sjögren’s 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.  \nMethods: 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 in vivo 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 lncRNAmiRNA-mRNA network associated with these genes was constructed, and preliminary experimental validation was conducted.  \nResults: 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 thenecroptosis signaling pathway. Machine learning analysis pinpointed 14 feature genes, among seven were associated with cancer (NAT1, BIRC3, EZH2, MAD2L1, ATP2A3, HMGA1, and BST2) . Given the unclear roles of SKI and PRDX4 in SS, the study focused specifically on five relevant genes, MAPK3, IL15, S100A9, IFI27, and CXCL10, which were validated by in vivo experiments. Immune cell analysis revealed increased proportions of B cells, T cells, macrophages, and other  \nimmune cells in SS tissues. Furthermore, ceRNA and drug-gene interaction networks were established, unde","cbCaifA9RB4bBvlb","https://ap.wps.com/l/cbCaifA9RB4bBvlb","pdf",7078051,3,1,19,"English","en",105,"# Objective\n# Methods\n## Bioinformatics and machine learning workflow\n## Experimental validation and immune infiltration analysis\n# Results\n## Differential anoikis-related genes and enrichment\n## Feature genes and key validated genes\n## Immune infiltration findings and networks\n# Conclusion\n# Keywords","[{\"question\":\"What is the main goal of this study in Sjögren’s syndrome?\",\"answer\":\"The study aims to identify anoikis-related genes and uncover their molecular mechanisms in SS, including how they relate to immune infiltration and inflammation progression.\"},{\"question\":\"Which datasets and analytical approaches were used to find candidate genes?\",\"answer\":\"SS datasets were retrieved from GEO, followed by extraction of anoikis-related genes, differential expression analysis, WGCNA, and machine learning screening. Diagnostic performance was assessed with ROC curves.\"},{\"question\":\"How were immune infiltration characteristics evaluated in SS tissues?\",\"answer\":\"Immune cell infiltration proportions were quantified using CIBERSORT, and correlations between characteristic genes and immune cell profiles were analyzed.\"}]","Identification of anoikis-related genes and immune infiltration characteristics in Sjögren’s syndrome based on machine learning - Original Research | PDF",1785946847,48,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"identification-of-anoikis-related-genes-and-immune-infiltration-characteristics-in-sjogrens-syndrome-based-on-machine-learning-original-research","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":20},"https://docshare.wps.com/document/research-report/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/identification-of-anoikis-related-genes-and-immune-infiltration-characteristics-in-sjogrens-syndrome-based-on-machine-learning-original-research/128325/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-30","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What is the main goal of this study in Sjögren’s syndrome?","Question",{"text":76,"@type":77},"The study aims to identify anoikis-related genes and uncover their molecular mechanisms in SS, including how they relate to immune infiltration and inflammation progression.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which datasets and analytical approaches were used to find candidate genes?",{"text":81,"@type":77},"SS datasets were retrieved from GEO, followed by extraction of anoikis-related genes, differential expression analysis, WGCNA, and machine learning screening. 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