[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125173-en":3,"doc-seo-125173-105":30,"detail-sidebar-cat-0-en-105":91},{"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":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125173,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Identification of lipid metabolism related immune markers in atherosclerosis through machine learning and experimental analysis","Atherosclerosis drives major cardiovascular morbidity, and conventional diagnostics often miss early-stage disease. This study integrates public atherosclerosis datasets with ssGSEA, WGCNA, differential expression, and multiple machine-learning algorithms to derive lipid metabolism–associated diagnostic genes. Immune infiltration relationships are assessed via ssGSEA immune cell scoring, while molecular docking evaluates binding potential between key markers and therapeutic agents. Experimental validation confirms the expression changes of the identified pivotal genes, supporting immune-relevant biomarker and drug-target value.","TYPE Original Research PUBLISHED 25 February 2025 DOI 10.3389/fimmu.2025.1549150  \nOPEN ACCESS  \nEDITED BY  \nMinghua Ren,  \nFirst Affiliated Hospital of Harbin Medical University, China  \nREVIEWED BY  \nChen Li,  \nFree University of Berlin, Germany Chuanlong Zhang,  \nCapital Medical University, China Wu Mianhua,  \nNanjing University of Chinese Medicine, China  \n*CORRESPONDENCE  \nDazhi Li  \n [21918363@zju.edu.cn](21918363@zju.edu.cn)[ ](21918363@zju.edu.cn)Weicheng Zhao  \n [873709201@qq.com](873709201@qq.com)  \n†These authors have contributed equally to this work  \nRECEIVED 20 December 2024  \nACCEPTED 05 February 2025  \nPUBLISHED 25 February 2025  \nCITATION  \nChen H, Wu B, Guan K, Chen L, Chai K, Ying M, Li D and Zhao W (2025)  \nIdentification of lipid metabolism related immune markers in atherosclerosis through machine learning and experimental analysis. Front. Immunol. 16:1549150 .  \ndoi: 10.3389/fimmu.2025.1549150  \nCOPYRIGHT  \n© 2025 Chen, Wu, Guan, Chen, Chai, Ying, Li and Zhao. 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 of lipid metabolism related immune markers in atherosclerosis through machine learning and experimental analysis  \nHang Chen 1†, Biao Wu 2,3†, Kunyu Guan 4†, Liang Chen 2,  \nKangjie Chai2, Maoji Ying5, Dazhi Li 2* and Weicheng Zhao 6*  \n1 Department of Thyroid Breast Vascular Surgery, Banan Hospital of Chongqing Medical University, Chongqing, China, 2 Department of Vascular Surgery, Changhai Hospital Affiliated to Naval Medical University, Shanghai, China, 3Cancer Research Centre Nantong, Nantong Tumor Hospital, Nantong, China, 4 Pediatrics, Changhai Hospital Affiliated to Naval Medical University, Shanghai, China,  \n5General Practice, Changhai Hospital Affiliated to Naval Medical University, Shanghai, China,  \n6 Department of Interventional, Shenzhen Traditional Chinese Medicine Hospital, Shenzhen, China  \nBackground: Atherosclerosis is a significant contributor to cardiovascular disease, and conventional diagnostic methods frequently fall short in the timely and accurate detection of early-stage atherosclerosis. Abnormal lipid metabolism plays a critical role in the development of atherosclerosis. Consequently, the identification of new diagnostic markers is essential for the precise diagnosis of this condition.  \nMethod: The datasets related to atherosclerosis utilized in this research were obtained from the GEO database (GSE2470, GSE24495, GSE100927 and GSE43292) . The ssGSEA technique was first utilized to assess lipid metabolism scores in samples affected by atherosclerosis, thereby aiding in the discovery of important regulatory genes linked to lipid metabolism via WGCNA. Following this, differential expression analysis and functional evaluations were carried out, after which various machine learning approaches were employed to determine significant diagnostic genes for atherosclerosis. A diagnostic model was then developed and validated through several machine learning algorithms. Furthermore, molecular docking studies were conducted to analyze the binding affinity of these key markers with therapeutic agents for atherosclerosis. The ssGSEA technique was also used to measure immune cell scores in atherosclerotic samples, aiding the exploration of the connection between key diagnostic markers and immune cells. Finally, the expression variations of the identified pivotal genes were confirmed through experimental validation.  \nResult: WGCNA identified 302 lipid metabolism-related genes in atherosclerotic samples, and functional analysis revealed that these genes are associated with multiple immune pa","cbCaiogvdpCiVwkw","https://ap.wps.com/l/cbCaiogvdpCiVwkw","pdf",18163639,1,16,"English","en",105,"# Background\n# Methods\n# Results\n# Conclusion\n# Introduction","[{\"question\":\"What datasets and computational approaches were used to discover candidate markers?\",\"answer\":\"Datasets were retrieved from the GEO database, with ssGSEA to quantify lipid metabolism scores, WGCNA to find regulatory genes, differential expression analysis for screening, and machine-learning methods to determine diagnostic genes.\"},{\"question\":\"Which genes were identified as key diagnostic markers for atherosclerosis?\",\"answer\":\"APLNR, PCDH12, PODXL, SLC40A1, TM4SF18, and TNFRSF25 were identified as key diagnostic genes after machine-learning-based screening.\"},{\"question\":\"How were immune cell relationships and therapeutic targeting potential evaluated?\",\"answer\":\"ssGSEA was used to estimate immune cell scores and link the diagnostic genes to immune infiltration, and molecular docking assessed binding affinity between the markers and therapeutic agents.\"}]","Identification of lipid metabolism related immune markers in atherosclerosis through machine learning and experimental analysis | 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