[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125197-en":3,"doc-seo-125197-105":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":4,"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},125197,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","Metabolomic Machine Learning-Based Model Predicts Efficacy of Chemoimmunotherapy for Advanced Lung Squamous Cell Carcinoma","Advanced lung squamous cell carcinoma shows a low rate of driver gene positivity and limited effective treatment options compared with lung adenocarcinoma. Chemoimmunotherapy has become standard first-line therapy, yet reliable response prediction remains challenging. This study integrates untargeted serum metabolomics with machine learning to build and validate a prognostic model for patients receiving chemoimmunotherapy, identifying metabolite biomarkers linked to overall and progression-free survival differences between responders and non-responders.","TYPE Original Research PUBLISHED 02 April 2025  \nDOI 10.3389/fimmu.2025.1545976  \nOPEN ACCESS  \nEDITED BY  \nChunmiao Cai,  \nMayo Clinic, United States  \nREVIEWED BY  \nBanzhan Ruan,  \nHainan Medical University, China Thirunavukkarsu M.,  \nGlobal Institute of Engineering and Technology, India  \nHuaping Tang,  \nQingdao Municipal Hospital, China Siyu Han,  \nHelmholtz Munich-German Research Center for Environmental Health, Germany  \n*CORRESPONDENCE  \nXueyan Zhang  \n [zxychest0109@163.com](zxychest0109@163.com)[ ](zxychest0109@163.com)Hua Zhong  \n [eddiedong8@hotmail.com](eddiedong8@hotmail.com)[ ](eddiedong8@hotmail.com)Xiaoxuan Zheng  \n [milozheng59@163.com](milozheng59@163.com)  \n†These authors have contributed equally to this work  \nRECEIVED 16 December 2024  \nACCEPTED 13 March 2025  \nPUBLISHED 02 April 2025  \nCITATION  \nZheng L, Nie W, Wang S, Yang L, Hu F, Ma M, Cheng L, Lu J, Zhang B, Xu J, Li Y, Shen Y, Zhang W, Zhong R, Chu T, Han B, Zheng X, Zhong H and Zhang X (2025) Metabolomic machine learning-based model predicts efficacy of chemoimmunotherapy for advanced lung squamous cell carcinoma. Front. Immunol. 16:1545976 .  \ndoi: 10.3389/fimmu.2025.1545976  \nCOPYRIGHT  \n© 2025 Zheng, Nie, Wang, Yang, Hu, Ma, Cheng, Lu, Zhang, Xu, Li, Shen, Zhang, Zhong, Chu, Han, Zheng, Zhong 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.  \nMetabolomic machine learningbased model predicts efficacy of chemoimmunotherapy for advanced lung squamous  \ncell carcinoma  \nLiang Zheng 1†, Wei Nie 1†, Shuyuan Wang 1†, Ling Yang 2†, Fang Hu 3,4, Meili Ma 1, Lei Cheng 1, Jun Lu 1, Bo Zhang 1,  \nJianlin Xu 1, Ying Li 1, Yinchen Shen 1, Wei Zhang 1, Runbo Zhong 1, Tianqing Chu 1, Baohui Han 1, Xiaoxuan Zheng 1,5*,  \nHua Zhong 1* and Xueyan Zhang 1*  \n1 Department of Respiratory and Critical Care Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China, 2 Department of Ultrasonography, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China, 3 Department of Thoracic Medical Oncology, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Zhejiang, Hangzhou, China, 4 Hangzhou Institute of Medicine (HlM), Chinese Academy of Sciences, Zhejiang, Hangzhou, China, 5 Department of Respiratory Endoscopy, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China  \nBackground: Unlike lung adenocarcinoma, patients with advanced squamous carcinoma exhibit a low proportion of driver gene positivity, with fewer effective treatment strategies available. Chemoimmunotherapy has now become the standard first-line treatment for individuals diagnosed with advanced lungsquamous carcinoma. Serum metabolomics holds significant potential for application in predicting responses to chemoimmunotherapy and is capable of identifying and validating potential biomarkers. The aim of our study was to establish a model that can predict the prognosis of chemoimmunotherapy inpatients with advanced lung squamo us cell carcinoma, integrating metabolomics with machine learning techniques.  \nMethods: We collected 79 serum samples from patients with advanced lungsquamous cell carcinoma before receiving combined immunotherapy and performed untargeted metabolomics analysis. Patients were divided into nonresponse (NR) and response (R) groups according to overall survival (OS), and prognostic models were constructed and validated using different machine learning methods. The patients were further categorized into high-risk and low-risk groups based o","cbCaicQSYy3Ul1lN","https://ap.wps.com/l/cbCaicQSYy3Ul1lN","pdf",3067130,1,14,"English","en",105,"# Background\n## Patient cohort and rationale\n# Methods\n## Serum metabolomics and model building\n## Risk stratification and validation\n# Results\n## Differential metabolites and biomarker selection\n## Predictive performance and survival analysis\n# Conclusions","[{\"question\":\"Why is predicting response important in advanced lung squamous cell carcinoma?\",\"answer\":\"Patients with advanced squamous carcinoma often have low driver gene positivity and limited effective treatment strategies, so identifying those likely to benefit from chemoimmunotherapy is clinically valuable.\"},{\"question\":\"How were the serum metabolomics data collected and analyzed?\",\"answer\":\"The study collected serum samples from patients before receiving combined immunotherapy and performed untargeted metabolomics, then identified differential metabolites between nonresponse and response groups.\"},{\"question\":\"Which machine learning approach produced the best predictive performance?\",\"answer\":\"Random forest achieved the best results, with AUCs of 0.973 for the training set and 0.944 for the validation set.\"}]","Metabolomic Machine Learning-Based Model Predicts Efficacy of Chemoimmunotherapy for Advanced Lung Squamous Cell Carcinoma | 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is predicting response important in advanced lung squamous cell carcinoma?","Question",{"text":75,"@type":76},"Patients with advanced squamous carcinoma often have low driver gene positivity and limited effective treatment strategies, so identifying those likely to benefit from chemoimmunotherapy is clinically valuable.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were the serum metabolomics data collected and analyzed?",{"text":80,"@type":76},"The study collected serum samples from patients before receiving combined immunotherapy and performed untargeted metabolomics, then identified differential metabolites between nonresponse and response groups.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning approach produced the best predictive performance?",{"text":84,"@type":76},"Random forest achieved the best results, with AUCs of 0.973 for the training set and 0.944 for the validation 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