[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126232-en":3,"doc-seo-126232-105":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},126232,2336475104042,"Skyler","https://ap-avatar.wpscdn.com/avatar/22000c4c32af1715be0?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786537525561427321",8,"Research & Report","Machine learning-based prediction model for lung ischemia-reperfusion injury - insights from disulfidptosis-related genes","This original research builds a machine-learning prediction framework for lung ischemia-reperfusion injury (IRI) after lung transplantation by linking IRI risk to disulfidptosis-related gene activity. GEO datasets (GSE145989 and GSE127003) support differential expression and functional enrichment analyses, followed by joint screening using generalized linear model, support vector machine, and random forest. The resulting model highlights SLC7A11 and LRPPRC with strong cross-dataset predictive performance. CIBERSORT and validation experiments also characterize immune-cell shifts, and drug candidates are proposed via CMap.","TYPE Original Research PUBLISHED 05 June 2025  \nDOI 10.3389/fphar.2025.1545111  \nOPEN ACCESS  \nEDITED BY  \nXin Jiang,  \nNational University of Singapore, Singapore  \nREVIEWED BY  \nArun Samidurai,  \nVirginia Commonwealth University, United States  \nXiaoguang Liu,  \nZhejiang University, China  \n*CORRESPONDENCE  \nGuangjian Zhang,  \n [michael8039@xjtu.edu.cn](michael8039@xjtu.edu.cn)  \n†These authors have contributed equally to this work  \nRECEIVED 14 December 2024  \nACCEPTED 29 May 2025  \nPUBLISHED 05 June 2025  \nCITATION  \nZhang Y, Sun J, Lin Y, Jiang R, Dong N, Dong H, Li P, Feng J, Zhu Z and Zhang G (2025) Machine learning-based prediction model for lung ischemia-reperfusion injury: insights from disulfidptosis-related genes.  \nFront. Pharmacol. 16:1545111 .  \ndoi: 10.3389/fphar.2025.1545111  \nCOPYRIGHT  \n© 2025 Zhang, Sun, Lin, Jiang, Dong, Dong, Li, Feng, Zhu and Zhang. 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.  \nMachine learning-based prediction model for lung  \nischemia-reperfusion injury: insights from disulfidptosis-related genes  \nYanpeng Zhang 1,2,3†, Jingyang Sun 1,2,3†, Yihan Lin 1,2,3†, Rongxuan Jiang 1,2,3, Niuniu Dong 1,2,3, Huanhuan Dong 1,2,3, Peng Li 1,2,3, Jinteng Feng 1,2,3, Zijiang Zhu 4 and Guangjian Zhang 1,2,3*  \n1Department of Thoracic Surgery, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, China, 2Key Laboratory of Enhanced Recovery After Surgery of Integrated Chinese and Western Medicine, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, China, 3Biobank, The First Affiliated Hospital of Xi’an Jiaotong University, Xi ’an, Shaanxi, China, 4 Department of Thoracic Surgery, Gansu Province Central Hospital, Lanzhou, Gansu, China  \nObjective: This study aims to explore potential ischemia-reperfusion injury (IRI) predictive biomarkers related to disulfidptosis following lung transplantation.  \nMethods: The study utilized datasets from the GEO database, specifically GSE145989 and GSE127003, which include samples of lung cold ischemia andreperfusion following transplantation. Differential expressed analysis and functional enrichment analysis were conducted to identify key genes associated with lung transplant IRI. Multiple machine learning algorithms (Generalized Linear Model, Support Vector Machine, and Random Forest) were applied for joint screening, leading to the construction of a predictive model. The CIBERSORT method was used to assess the infiltration levels of immune cells in lung tissue samples post-transplant. Finally, cell line and animal experiments were carried out to validate the effectiveness and applicability of the model.  \nResults: A total of 14,592 hub differential-expressed genes were identified, showing significant changes in cold ischemia and reperfusion samples. Using the three machine learning algorithms for joint analysis, a predictive model composed of SLC7A11 and LRPPRC was constructed. This model demonstrated excellent predictive efficacy across multiple datasets, with area under the curve (AUC) values of 0 .742 and 0 . 938, respectively. Additionally, significant differences in neutrophils and macrophages were observed in lung transplant cold ischemia and reperfusion samples. Based on the differential genes associated with disulfidptosis and utilizing the CMap database, we identified two potential drugs targeting IRI: olanzapine and vortioxetine. Ultimately, cell line and animal experiments validated the predictive model’s reliability and potential clinical value, revealing that disulfidptosis presents in IRI, and high SLC7A11 expression promo","cbCaivdPgvaNefCk","https://ap.wps.com/l/cbCaivdPgvaNefCk","pdf",4015079,3,1,13,"English","en",105,"# Objective\n# Methods\n# Results\n## Predictive model performance\n## Immune-cell and drug-candidate findings\n# Conclusion","[{\"question\":\"What goal does the study pursue?\",\"answer\":\"The study aims to identify ischemia-reperfusion injury predictive biomarkers related to disulfidptosis following lung transplantation.\"},{\"question\":\"Which datasets and analytical steps are used to build the model?\",\"answer\":\"It uses GEO datasets GSE145989 and GSE127003, performs differential expression and functional enrichment analyses, then applies multiple machine learning algorithms for joint gene screening to construct the predictive model.\"},{\"question\":\"What biomarkers does the final model identify and how are they interpreted?\",\"answer\":\"The predictive model is composed of SLC7A11 and LRPPRC, showing that high SLC7A11 expression promotes IRI while low LRPPRC expression contributes to its occurrence.\"}]","Machine learning-based prediction model for lung ischemia-reperfusion injury - insights from disulfidptosis-related genes | PDF",1785903956,33,{"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},"machine-learning-based-prediction-model-for-lung-ischemia-reperfusion-injury-insights-from-disuldptosis-related-genes","",{"@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/machine-learning-based-prediction-model-for-lung-ischemia-reperfusion-injury-insights-from-disuldptosis-related-genes/126232/",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","2026-08-15","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 goal does the study pursue?","Question",{"text":76,"@type":77},"The study aims to identify ischemia-reperfusion injury predictive biomarkers related to disulfidptosis following lung transplantation.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which datasets and analytical steps are used to build the model?",{"text":81,"@type":77},"It uses GEO datasets GSE145989 and GSE127003, performs differential expression and functional enrichment analyses, then applies multiple machine learning algorithms for joint gene screening to construct the predictive model.",{"name":83,"@type":74,"acceptedAnswer":84},"What biomarkers does the final model identify and how are they interpreted?",{"text":85,"@type":77},"The predictive model is composed of SLC7A11 and LRPPRC, showing that high SLC7A11 expression promotes IRI while low LRPPRC expression contributes to its occurrence.","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":48,"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"]