[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127637-en":3,"doc-seo-127637-105":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},127637,962084925502,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Identification of immune-related genes in diagnosing retinopathy of prematurity with sepsis through bioinformatics analysis and machine learning","Immune dysregulation is strongly linked to the development of retinopathy of prematurity (ROP) and sepsis, motivating the search for genes that can support diagnosis. Public ROP and sepsis transcriptomic datasets were analyzed with differential expression and weighted gene co-expression network analysis to obtain differentially expressed and module genes. Functional enrichment clarified involved pathways, while immune-related candidates were prioritized using SVM-RFE, LASSO, and random forest, then assessed by nomograms and ROC curves. Immune-cell composition was characterized using CIBERSORT, and cMAP highlighted candidate therapeutics for sepsis.","TYPE Original Research PUBLISHED 10 November 2023 DOI 10.3389/fgene.2023.1264873  \nOPEN ACCESS  \nEDITED BY  \nXiao Chang,  \nChildren ’s Hospital of Philadelphia, United States  \nREVIEWED BY  \nPengyuan Du,  \nShandong First Medical University and Shandong Academy of Medical Science, China  \nYingchao Song,  \nShandong First Medical University and Shandong Academy of Medical Sciences, China  \n*CORRESPONDENCE  \nYu Xu,  \n [xuyu01@xinhuamed.com.cn](xuyu01@xinhuamed.com.cn)  \nRECEIVED 15 August 2023  \nACCEPTED 30 October 2023  \nPUBLISHED 10 November 2023  \nCITATION  \nChen H, Chen E, Lu Y and Xu Y (2023), Identification of immune-related genes in diagnosing retinopathy of prematurity with sepsis through bioinformatics analysis and machine learning.  \nFront. Genet. 14:1264873 .  \ndoi: 10.3389/fgene.2023.1264873  \nCOPYRIGHT  \n© 2023 Chen, Chen, Lu and Xu. 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 immune-related genes in diagnosing retinopathy of prematurity with sepsis through bioinformatics analysis and machine learning  \nHan Chen 1, Enguang Chen 1, Yao Lu 2 and Yu Xu 1*  \n1Department of Ophthalmology, School of Medicine, Xinhua Hospital, Shanghai Jiao Tong University, Shanghai, China, 2Department of Ophthalmology, Anhui No. 2 Provincial People’s Hospital, Anhui, Hefei, China  \nBackground: There is increasing evidence indicating that immune system dysregulation plays a pivotal role in the pathogenesis of retinopathy of prematurity (ROP) and sepsis. This study aims to identify key diagnostic candidate genes in ROP with sepsis.  \nMethods: We obtained publicly available data on ROP and sepsis from the gene expression omnibus database. Differential analysis and weighted gene correlation network analysis (WGCNA) were performed to identify differentially expressed genes (DEGs) and key module genes. Subsequently, we conducted functional enrichment analysis to gain insights into the biological functions and pathways. To identify immune-related pathogenic genes and potential mechanisms, we employed several machine learning algorithms, including Support Vector Machine Recursive Feature Elimination (SVM-RFE), Least Absolute Shrinkage and Selection Operator (LASSO), and Random Forest (RF) . We evaluated the diagnostic performance using nomogram and Receiver Operating Characteristic (ROC) curves. Furthermore, we used CIBERSORT to investigate immune cell dysregulation in sepsis and performed cMAP analysis to identify potential therapeutic drugs.  \nResults: The sepsis dataset comprised 352 DEGs, while the ROP dataset had 307 DEGs and 420 module genes. The intersection between DEGs for sepsis and module genes for ROP consisted of 34 genes, primarily enriched in immunerelated pathways. After conducting PPI network analysis and employing machine learning algorithms, we pinpointed five candidate hub genes. Subsequent evaluation using nomograms and ROC curves underscored their robust diagnostic potential. Immune cell infiltration analysis revealed immune cell dysregulation. Finally, through cMAP analysis, we identified some small molecule compounds that have the potential for sepsis treatment.  \nConclusion: Five immune-associated candidate hub genes (CLEC5A, KLRB1, LCN2, MCEMP1, and MMP9) were recognized, and the nomogram for the diagnosis of ROP with sepsis was developed.  \nKEYWORDS  \nretinopathy of prematurity, sepsis, machine learning, immune infiltration, diagnostic value  \nFrontiers in Genetics 01 [frontiersin.org](frontiersin.org)  \n1 Introduction  \nSepsis is a severe systemic infection characterized by adysregulated host response, leadin","cbCaiak3wcmrHHqy","https://ap.wps.com/l/cbCaiak3wcmrHHqy","pdf",4201572,2,1,14,"English","en",105,"# Introduction\n## Sepsis in neonates\n## Retinopathy of prematurity (ROP)\n## Links among inflammation, sepsis, and ROP\n# Methods\n## Data sources and preprocessing\n## Differential expression and WGCNA\n## Functional enrichment\n## Machine learning feature selection\n## Diagnostic evaluation\n## Immune cell infiltration analysis\n## cMAP drug exploration\n# Results\n## Differentially expressed genes and module intersection\n## Hub gene identification\n## Nomogram and ROC performance\n## Immune cell dysregulation\n## Candidate therapeutics from cMAP\n# Conclusion","[{\"question\":\"What is the main goal of the study?\",\"answer\":\"To identify immune-related candidate genes that can diagnose retinopathy of prematurity complicated with sepsis using bioinformatics and machine learning.\"},{\"question\":\"How were candidate genes identified?\",\"answer\":\"Public ROP and sepsis gene-expression data were processed with differential analysis and WGCNA to obtain DEGs and module genes, whose overlap was used for downstream functional analysis and machine-learning-based feature selection.\"},{\"question\":\"How did the researchers evaluate diagnostic performance?\",\"answer\":\"A nomogram and ROC curves were used to assess the diagnostic potential of the selected hub genes.\"}]","Identification of immune-related genes in diagnosing retinopathy of prematurity with sepsis through bioinformatics analysis and machine learning | PDF",1785940445,35,{"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-of-immune-related-genes-in-diagnosing-retinopathy-of-prematurity-with-sepsis-through-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-of-immune-related-genes-in-diagnosing-retinopathy-of-prematurity-with-sepsis-through-bioinformatics-analysis-and-machine-learning/127637/",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","2026-08-22","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 the study?","Question",{"text":76,"@type":77},"To identify immune-related candidate genes that can diagnose retinopathy of prematurity complicated with sepsis using bioinformatics and machine learning.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How were candidate genes identified?",{"text":81,"@type":77},"Public ROP and sepsis gene-expression data were processed with differential analysis and WGCNA to obtain DEGs and module genes, whose overlap was used for downstream functional analysis and machine-learning-based feature selection.",{"name":83,"@type":74,"acceptedAnswer":84},"How did the researchers evaluate diagnostic performance?",{"text":85,"@type":77},"A nomogram and ROC curves were used to assess the diagnostic potential of the selected hub genes.","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"]