[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127914-en":3,"doc-seo-127914-105":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},127914,137451207643,"Noah","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Identification and validation of interferon-stimulated gene 15 as a biomarker for dermatomyositis - integrated bioinformatics analysis and machine learning","Dermatomyositis (DM) is an autoimmune disease affecting skin and muscles and may progress to fatal complications, making early diagnosis dependent on reliable biomarkers. This study integrates bioinformatics analysis with clinical sample validation to identify and confirm interferon-stimulated gene 15 (ISG15) as a diagnostic marker. Gene expression datasets were merged, hub genes were prioritized using machine learning and WGCNA, and diagnostic performance was assessed by ROC analysis, immunohistochemistry, immune infiltration inference, pathway enrichment, and drug-gene interaction prediction.","TYPE Original Research PUBLISHED 04 November 2024 DOI 10.3389/fimmu.2024.1429817  \nOPEN ACCESS  \nEDITED BY  \nJoerg Wenzel,  \nUniversity Hospital Bonn, Germany  \nREVIEWED BY  \nShuwen Ge,  \nUniversity of Lübeck, Germany Takemichi Fukasawa,  \nThe University of Tokyo Graduate School of Medicine, Japan  \n*CORRESPONDENCE  \nXingwang Wang  \n [1057600702@qq.com](1057600702@qq.com)[ ](1057600702@qq.com)Jianyong Fan  \n [1804508644@qq.com](1804508644@qq.com)  \n†These authors have contributed equally to this work  \nRECEIVED 08 May 2024  \nACCEPTED 14 October 2024  \nPUBLISHED 04 November 2024  \nCITATION  \nWang X, Hu H, Yan G, Zheng B, Luo J and Fan J (2024) Identification and validation of interferon-stimulated gene 15 as a biomarker for dermatomyositis by integrated bioinformatics analysis and machine learning. Front. Immunol. 15:1429817 .  \ndoi: 10.3389/fimmu.2024.1429817  \nCOPYRIGHT  \n© 2024 Wang, Hu, Yan, Zheng, Luo and Fan. 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.  \nIdentification and validation of interferon-stimulated gene 15 asa biomarker for dermatomyositis by integrated bioinformatics analysis and machine learning  \nXingwang Wang 1*†, Hao Hu 2†, Guangning Yan 3†, Bo Zheng 1, Jinxia Luo 3 and Jianyong Fan 1*  \n1 Department of Dermatology, General Hospital of Southern Theater Command, Guangzhou, China, 2 Department of Radiation Therapy, General Hospital of Southern Theater Command,  \nGuangzhou, China, 3 Department of Pathology, General Hospital of Southern Theater Command, Guangzhou, China  \nBackground: Dermatomyositis (DM) is an autoimmune disease that primarily affects the skin and muscles. It can lead to increased mortality, particularly when patients develop associated malignancies or experience fatal complications such as pulmonary fibrosis. Identifying reliable biomarkers is essential for the early diagnosis and treatment of DM. This study aims to identify and validate pivotal diagnostic biomarker for DM through integrated bioinformatics analysis and clinical sample validation.  \nMethods: Gene expression datasets GSE46239 and GSE142807 from the Gene Expression Omnibus (GEO) database were merged for analysis. Differentially expressed genes (DEGs) were identified and subjected to enrichment analysis. Advanced machine learning methods were utilized to further pinpoint hub genes. Weighted gene co‐expression network analysis (WGCNA) was also conducted to discover key gene modules. Subsequently, we derived intersection gene from these methods. The diagnostic performance of the candidate biomarker was evaluated using analysis with dataset GSE128314 and confirmed by immunohistochemistry (IHC) in skin lesion biopsy specimens. The CIBERSORT algorithm was used to analyze immune cell infiltration patterns in DM, then the association between the hub gene and immune cells was investigated. Gene set enrichment analysis (GSEA) was performed to understand the biomarker ’s biological functions. Finally, the drug-gene interactions were predicted using the DrugRep server.  \nResults: Interferon-stimulated gene 15 (ISG15) was identified by intersecting DEGs, advanced machine learning-selected genes and key module genes from WGCNA. ROC analysis showed ISG15 had a high Area under the curve (AUC) of 0.950. IHC findings confirmed uniformly positive expression of ISG15, particularly in perivascular regions and lymphocytes, contrasting with universally negative expression in controls. Further analysis revealed that ISG15 is involved in abnormalities in various immune cells and inflammation-related pathways. We also predicted three drugs targeting ISG15, supported ","cbCaiq6fMerZDBUd","https://ap.wps.com/l/cbCaiq6fMerZDBUd","pdf",12458375,2,1,14,"English","en",105,"# Background\n## Disease burden and diagnostic challenges\n## Type I interferon pathway and ISG15\n# Methods\n## Dataset integration and differential expression\n## Enrichment and hub gene identification\n## Validation by ROC and immunohistochemistry\n## Immune infiltration, GSEA, and drug-gene interactions\n# Results\n## ISG15 identification and diagnostic performance\n## Histological validation and immune/pathway associations\n## Predicted drug targets and docking support\n# Conclusion","[{\"question\":\"What is the main goal of this study for dermatomyositis?\",\"answer\":\"To identify and validate ISG15 as a pivotal diagnostic biomarker for dermatomyositis by combining integrated bioinformatics with clinical sample validation.\"},{\"question\":\"How were candidate genes identified and prioritized?\",\"answer\":\"Differentially expressed genes from merged GEO datasets were analyzed with enrichment methods, advanced machine learning, and weighted gene co-expression network analysis (WGCNA) to select hub genes, followed by intersection of results.\"},{\"question\":\"How was ISG15 validated as a diagnostic biomarker?\",\"answer\":\"Diagnostic performance was evaluated using ROC analysis, and immunohistochemistry on skin lesion biopsy specimens confirmed uniformly positive ISG15 expression in DM compared with negative controls.\"}]","Identification and validation of interferon-stimulated gene 15 as a biomarker for dermatomyositis - integrated bioinformatics analysis and machine learning | PDF",1785942918,35,{"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-and-validation-of-interferon-stimulated-gene-15-as-a-biomarker-for-dermatomyositis-integrated-bioinformatics-analysis-and-machine-learning","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/identification-and-validation-of-interferon-stimulated-gene-15-as-a-biomarker-for-dermatomyositis-integrated-bioinformatics-analysis-and-machine-learning/127914/",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","2026-08-27","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 for dermatomyositis?","Question",{"text":76,"@type":77},"To identify and validate ISG15 as a pivotal diagnostic biomarker for dermatomyositis by combining integrated bioinformatics with clinical sample validation.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How were candidate genes identified and prioritized?",{"text":81,"@type":77},"Differentially expressed genes from merged GEO datasets were analyzed with enrichment methods, advanced machine learning, and weighted gene co-expression network analysis (WGCNA) to select hub genes, followed by intersection of results.",{"name":83,"@type":74,"acceptedAnswer":84},"How was ISG15 validated as a diagnostic biomarker?",{"text":85,"@type":77},"Diagnostic performance was evaluated using ROC analysis, and immunohistochemistry on skin lesion biopsy specimens confirmed uniformly positive ISG15 expression in DM compared with negative controls.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]