Metabolomic machine learning-based model predicts efficacy of chemoimmunotherapy for advanced lung squamous cell carcinoma.

Journal: Frontiers in immunology
PMID:

Abstract

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.

Authors

  • Liang Zheng
    Urban Transport Research Center, School of Traffic and Transportation Engineering, Central South University, Changsha, Hunan, 410075 PR China. Electronic address: zhengliang@csu.edu.cn.
  • Wei Nie
    Radiation Oncology Division, Inova Schar Cancer Institute, Fairfax, VA, United States of America.
  • Shuyuan Wang
    Whiting School of Engineering, Johns Hopkins University, Baltimore, Maryland, USA.
  • Ling Yang
    Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine Shanghai 201203 China pwang@shutcm.edu.cn.
  • Fang Hu
    Key Laboratory of Medical Imaging and Artificial Intelligence of Hunan Province, Xiangnan University, Chenzhou, 423000, Hunan, China.
  • Meili Ma
    Department of Respiratory and Critical Care Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Lei Cheng
    State Key Laboratory of Oral Diseases, Sichuan University, Chengdu, China.
  • Jun Lu
    School of Acupuncture-moxibustion and Tuina, Beijing University of Chinese Medicine, Beijing 100029, China.
  • Bo Zhang
    Department of Clinical Pharmacology, Key Laboratory of Clinical Cancer Pharmacology and Toxicology Research of Zhejiang Province, Affiliated Hangzhou First People's Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310006, PR China.
  • Jianlin Xu
    Department of Respiratory and Critical Care Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Ying Li
    School of Information Engineering, Chang'an University, Xi'an 710010, China.
  • Yinchen Shen
    Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, National Clinical Research Center for Eye Diseases, Shanghai Key Laboratory of Ocular Fundus Diseases, Shanghai Engineering Center for Visual Science and Photomedicine, Shanghai Engineering Center for Precise Diagnosis and Treatment of Eye Diseases, No. 100 Haining Road, Shanghai 20080, PR China.
  • Wei Zhang
    The First Affiliated Hospital of Nanchang University, Nanchang, China.
  • Runbo Zhong
    Department of Respiratory and Critical Care Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Tianqing Chu
    Department of Respiratory and Critical Care Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Baohui Han
    Department of Respiratory Disease, Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai 200030, China. Electronic address: xkyyhan@gmail.com.
  • Xiaoxuan Zheng
    Department of Respiratory and Critical Care Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Hua Zhong
    Department of Population Health, NYU Grossman School of Medicine, New York, NY, USA.
  • Xueyan Zhang
    National Institute of Occupational Health and Poison Control, Chinese Center for Disease Control and Prevention, Beijing 100050, China.