Metabolomic machine learning-based model predicts efficacy of chemoimmunotherapy for advanced lung squamous cell carcinoma
BackgroundUnlike 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 lung sq...
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Frontiers Media S.A.
2025-04-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2025.1545976/full |
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| author | Liang Zheng Wei Nie Shuyuan Wang Ling Yang Fang Hu Fang Hu Meili Ma Lei Cheng Jun Lu Bo Zhang Jianlin Xu Ying Li Yinchen Shen Wei Zhang Runbo Zhong Tianqing Chu Baohui Han Xiaoxuan Zheng Xiaoxuan Zheng Hua Zhong Xueyan Zhang |
| author_facet | Liang Zheng Wei Nie Shuyuan Wang Ling Yang Fang Hu Fang Hu Meili Ma Lei Cheng Jun Lu Bo Zhang Jianlin Xu Ying Li Yinchen Shen Wei Zhang Runbo Zhong Tianqing Chu Baohui Han Xiaoxuan Zheng Xiaoxuan Zheng Hua Zhong Xueyan Zhang |
| author_sort | Liang Zheng |
| collection | DOAJ |
| description | BackgroundUnlike 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 lung squamous 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 in patients with advanced lung squamous cell carcinoma, integrating metabolomics with machine learning techniques.MethodsWe collected 79 serum samples from patients with advanced lung squamous cell carcinoma before receiving combined immunotherapy and performed untargeted metabolomics analysis. Patients were divided into non-response (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 on the median risk score, to assess the model's predictive performance.ResultsThere were significant differences in metabolites and metabolic pathways between NR and R groups, and 117 differential metabolites were preliminarily screened (p < 0.05, VIP > 1). Further, least absolute shrinkage and selection operator (LASSO) and random forest (RF) were used to identify metabolites, and then their common metabolites were used as the best biomarkers to build a prediction model containing 8 differential metabolites. Based on these biomarkers, RF, support vector machine (SVM) and logistic regression were used to randomly divide patients into training and validation sets in a 7:3 ratio, respectively. We found that the RF method resulted in area under curves (AUCs) of 0.973 and 0.944 for the training and validation sets, respectively, with the best predictive performance. Subsequently, both OS and progression-free survival (PFS) were notably reduced in the high-risk group when contrasted with the low-risk group.ConclusionsWe developed a model containing 8 metabolites based on metabolomics and machine learning that may predict survival outcomes in patients with advanced lung squamous cell carcinoma undergoing chemoimmunotherapy, helping to more accurately assess efficacy and prognosis in clinical practice. |
| format | Article |
| id | doaj-art-9fa4fad5993c49aa8793e86788ee8e20 |
| institution | DOAJ |
| issn | 1664-3224 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Immunology |
| spelling | doaj-art-9fa4fad5993c49aa8793e86788ee8e202025-08-20T03:06:41ZengFrontiers Media S.A.Frontiers in Immunology1664-32242025-04-011610.3389/fimmu.2025.15459761545976Metabolomic machine learning-based model predicts efficacy of chemoimmunotherapy for advanced lung squamous cell carcinomaLiang Zheng0Wei Nie1Shuyuan Wang2Ling Yang3Fang Hu4Fang Hu5Meili Ma6Lei Cheng7Jun Lu8Bo Zhang9Jianlin Xu10Ying Li11Yinchen Shen12Wei Zhang13Runbo Zhong14Tianqing Chu15Baohui Han16Xiaoxuan Zheng17Xiaoxuan Zheng18Hua Zhong19Xueyan Zhang20Department of Respiratory and Critical Care Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Respiratory and Critical Care Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Respiratory and Critical Care Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Ultrasonography, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Thoracic Medical Oncology, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Zhejiang, Hangzhou, ChinaHangzhou Institute of Medicine (HlM), Chinese Academy of Sciences, Zhejiang, Hangzhou, ChinaDepartment of Respiratory and Critical Care Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Respiratory and Critical Care Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Respiratory and Critical Care Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Respiratory and Critical Care Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Respiratory and Critical Care Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Respiratory and Critical Care Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Respiratory and Critical Care Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Respiratory and Critical Care Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Respiratory and Critical Care Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Respiratory and Critical Care Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Respiratory and Critical Care Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Respiratory and Critical Care Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Respiratory Endoscopy, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Respiratory and Critical Care Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaDepartment of Respiratory and Critical Care Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, ChinaBackgroundUnlike 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 lung squamous 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 in patients with advanced lung squamous cell carcinoma, integrating metabolomics with machine learning techniques.MethodsWe collected 79 serum samples from patients with advanced lung squamous cell carcinoma before receiving combined immunotherapy and performed untargeted metabolomics analysis. Patients were divided into non-response (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 on the median risk score, to assess the model's predictive performance.ResultsThere were significant differences in metabolites and metabolic pathways between NR and R groups, and 117 differential metabolites were preliminarily screened (p < 0.05, VIP > 1). Further, least absolute shrinkage and selection operator (LASSO) and random forest (RF) were used to identify metabolites, and then their common metabolites were used as the best biomarkers to build a prediction model containing 8 differential metabolites. Based on these biomarkers, RF, support vector machine (SVM) and logistic regression were used to randomly divide patients into training and validation sets in a 7:3 ratio, respectively. We found that the RF method resulted in area under curves (AUCs) of 0.973 and 0.944 for the training and validation sets, respectively, with the best predictive performance. Subsequently, both OS and progression-free survival (PFS) were notably reduced in the high-risk group when contrasted with the low-risk group.ConclusionsWe developed a model containing 8 metabolites based on metabolomics and machine learning that may predict survival outcomes in patients with advanced lung squamous cell carcinoma undergoing chemoimmunotherapy, helping to more accurately assess efficacy and prognosis in clinical practice.https://www.frontiersin.org/articles/10.3389/fimmu.2025.1545976/fullmetabolomicsmachine learningchemoimmunotherapypredictive modeltumor biomarkers |
| spellingShingle | Liang Zheng Wei Nie Shuyuan Wang Ling Yang Fang Hu Fang Hu Meili Ma Lei Cheng Jun Lu Bo Zhang Jianlin Xu Ying Li Yinchen Shen Wei Zhang Runbo Zhong Tianqing Chu Baohui Han Xiaoxuan Zheng Xiaoxuan Zheng Hua Zhong Xueyan Zhang Metabolomic machine learning-based model predicts efficacy of chemoimmunotherapy for advanced lung squamous cell carcinoma Frontiers in Immunology metabolomics machine learning chemoimmunotherapy predictive model tumor biomarkers |
| title | Metabolomic machine learning-based model predicts efficacy of chemoimmunotherapy for advanced lung squamous cell carcinoma |
| title_full | Metabolomic machine learning-based model predicts efficacy of chemoimmunotherapy for advanced lung squamous cell carcinoma |
| title_fullStr | Metabolomic machine learning-based model predicts efficacy of chemoimmunotherapy for advanced lung squamous cell carcinoma |
| title_full_unstemmed | Metabolomic machine learning-based model predicts efficacy of chemoimmunotherapy for advanced lung squamous cell carcinoma |
| title_short | Metabolomic machine learning-based model predicts efficacy of chemoimmunotherapy for advanced lung squamous cell carcinoma |
| title_sort | metabolomic machine learning based model predicts efficacy of chemoimmunotherapy for advanced lung squamous cell carcinoma |
| topic | metabolomics machine learning chemoimmunotherapy predictive model tumor biomarkers |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2025.1545976/full |
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