[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127708-en":3,"doc-seo-127708-105":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},127708,962084928432,"Emma Wilson","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Identification and validation of CCR5 linking keloid with atopic dermatitis through comprehensive bioinformatics analysis and machine learning","Keloid is strongly associated with atopic dermatitis (AD) across ethnic groups, yet the molecular basis of this comorbidity remains incompletely defined. This study integrates comprehensive bioinformatics and machine-learning analyses using GEO gene-expression profiles from keloid and AD. Shared differentially expressed genes were used to build protein–protein interaction networks and derive hub genes, with LASSO and SVM-RFE identifying CCR5 as the key gene. Validation in independent datasets and clinical samples confirmed increased CCR5 expression, and immune infiltration linked CCR5 with Th1/Th2/Th17 enrichment.","TYPE Original Research PUBLISHED 27 February 2024  \nDOI 10.3389/fimmu.2024.1309992  \nOPEN ACCESS  \nEDITED BY Rui-qun Qi,  \nThe First Affiliated Hospital of China Medical University, China  \nREVIEWED BY  \nHans David Brightbill, Genentech Inc., United States Xiao-Yong Man,  \nZhejiang University, China  \n*CORRESPONDENCE Huinan Suo  \n [suohuinan@163.com](suohuinan@163.com)[ ](suohuinan@163.com)Juan Tao  \n [tjhappy@126.com](tjhappy@126.com)  \n†These authors have contributed equally to this work  \nRECEIVED 09 October 2023  \nACCEPTED 02 February 2024  \nPUBLISHED 27 February 2024  \nCITATION  \nZhou B, Zhou N, Liu Y, Dong E, Peng L, Wang Y, Yang L, Suo H and Tao J (2024) Identification and validation of CCR5  \nlinking keloid with atopic dermatitis through comprehensive bioinformatics analysis and machine learning.  \nFront. Immunol. 15:1309992 .  \ndoi: 10.3389/fimmu.2024.1309992  \nCOPYRIGHT  \n© 2024 Zhou, Zhou, Liu, Dong, Peng, Wang, Yang, Suo and Tao. This is an open-access article distributed under the terms of the  \nCreative Commons Attribution License (CC BY) .  \nThe 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 CCR5 linking keloid with atopic dermatitis through comprehensive bioinformatics analysis and machine learning  \nBin Zhou 1,2†, Nuoya Zhou 1,2†, Yan Liu 1,2, Enzhu Dong 1,2, Lianqi Peng 1,2, Yifei Wang 1,2, Liu Yang 1,2, Huinan Suo 1,2* and Juan Tao 1,2*  \n1 Department of Dermatology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology (HUST), Wuhan, Hubei, China, 2 Hubei Engineering Research Center for Skin Repair and Theranostics, Wuhan, Hubei, China  \nThere is sufficient evidence indicating that keloid is strongly associated with atopic dermatitis (AD) across ethnic groups. However, the molecular mechanism underlying the association is not fully understood. The aim of this study is to discover the underlying mechanism of the association between keloid and AD by integrating comprehensive bioinformatics techniques and machine learning methods. The gene expression profiles of keloid and AD were downloaded from the Gene Expression Omnibus (GEO) database. A total of 449 differentially expressed genes (DEGs) were found to be shared in keloid and AD using the training datasets of GEO (GSE158395 and GSE121212) . The hub genes were identified using the protein-protein interaction network and Cytoscape software. 20 of the most significant hub genes were selected, which were mainly involved in the regulation of the inflammatory and immune response. Through two machine learning algorithms of LASSO and SVM-RFE, CCR5 was identified asthe most important key gene. Subsequently, upregulated CCR5 gene expression was confirmed in validation GEO datasets (GSE188952 and GSE32924) and clinical samples of keloid and AD. Immune infiltration analysis showed that T helper (Th) 1, 2 and 17 cells were significantly enriched in the microenvironment of both keloid and AD. Positive correlations were found between CCR5 and Th1, Th2 and Th17 cells. Finally, two TFs of CCR5, NR3C2 and YY1, were identified, both of which were downregulated in keloid and AD tissues. Our study firstly reveals that keloid and AD shared common inflammatory and immune pathways. Moreover, CCR5 plays a key role in the pathogenesis association between keloid and AD. The common pathways and key genes may shed light on further mechanism research and targeted therapy, and may provide therapeutic interventions of keloid with AD.  \nKEYWORDS  \nkeloid, atopic dermatitis, bioinformatics, machine learning, hub genes, immune cell  \nFrontiers in Immunology 01 [frontiersin.org](frontiersin.org)  \nIntroduction  \nKeloid is a benign fibroproliferative dermal tumor","cbCaidQ8gS7hnsfb","https://ap.wps.com/l/cbCaidQ8gS7hnsfb","pdf",9767387,3,1,14,"English","en",105,"# Introduction\n## Background: keloid and immune mechanisms\n## Evidence linking keloid with atopic dermatitis\n## Immune features of atopic dermatitis\n## Need to clarify molecular pathways","[{\"question\":\"What is the main goal of the study on keloid and atopic dermatitis?\",\"answer\":\"To uncover the molecular mechanism linking keloid and atopic dermatitis by combining comprehensive bioinformatics and machine learning methods.\"},{\"question\":\"How was CCR5 identified as a key gene?\",\"answer\":\"Shared differentially expressed genes were analyzed through protein–protein interaction networks to select hub genes, and two machine-learning approaches (LASSO and SVM-RFE) highlighted CCR5 as the most important gene.\"},{\"question\":\"How did the study validate the role of CCR5?\",\"answer\":\"CCR5 upregulation was confirmed in independent validation GEO datasets and in clinical samples from patients with keloid and AD.\"}]","Identification and validation of CCR5 linking keloid with atopic dermatitis through comprehensive bioinformatics analysis and machine learning | 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is the main goal of the study on keloid and atopic dermatitis?","Question",{"text":76,"@type":77},"To uncover the molecular mechanism linking keloid and atopic dermatitis by combining comprehensive bioinformatics and machine learning methods.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How was CCR5 identified as a key gene?",{"text":81,"@type":77},"Shared differentially expressed genes were analyzed through protein–protein interaction networks to select hub genes, and two machine-learning approaches (LASSO and SVM-RFE) highlighted CCR5 as the most important gene.",{"name":83,"@type":74,"acceptedAnswer":84},"How did the study validate the role of CCR5?",{"text":85,"@type":77},"CCR5 upregulation was confirmed in independent validation GEO datasets and in clinical samples from patients with keloid and 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