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ORIGINAL RESEARCH article

Front. Immunol.

Sec. Cancer Immunity and Immunotherapy

Volume 16 - 2025 | doi: 10.3389/fimmu.2025.1545976

This article is part of the Research Topic Challenges and Opportunities in Tumor Metabolomics View all 3 articles

Metabolomic Machine Learning-based Model Predicts Efficacy of Chemoimmunotherapy for Advanced Lung Squamous Cell Carcinoma

Provisionally accepted
Zheng Liang Zheng Liang 1Wei Nie Wei Nie 1Shuyuan Wang Shuyuan Wang 1Ling Yang Ling Yang 2Fang Hu Fang Hu 3Meili Ma Meili Ma 1Lei Cheng Lei Cheng 1Jun Lu Jun Lu 1Bo Zhang Bo Zhang 1Jianlin Xu Jianlin Xu 1Ying Li Ying Li 1Yinchen Shen Yinchen Shen 1Wei Zhang Wei Zhang 1Runbo Zhong Runbo Zhong 1Tianqing Chu Tianqing Chu 1Baohui Han Baohui Han 1Xiaoxuan Zheng Xiaoxuan Zheng 1Hua Zhong Hua Zhong 1Xueyan Zhang Xueyan Zhang 1*
  • 1 Department of Respiratory and Critical Care Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
  • 2 Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China
  • 3 Department of Thoracic Medical Oncology, Zhejiang Cancer Hospital, University of Chinese Academy of Sciences, Hangzhou, Jiangsu Province, China

The final, formatted version of the article will be published soon.

    Background: 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 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.We 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.Results: There 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.We 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.

    Keywords: Metabolomics, machine learning, chemoimmunotherapy, predictive model, Tumour biomarkers

    Received: 16 Dec 2024; Accepted: 13 Mar 2025.

    Copyright: © 2025 Liang, 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) or licensor 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.

    * Correspondence: Xueyan Zhang, Department of Respiratory and Critical Care Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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