Machine learning based on metabolomics unveils neutrophil extracellular trap-related metabolic signatures in non-small cell lung cancer patients undergoing chemoimmunotherapy  

作　　者：Yu-Ning Li Jia-Lin Su Shu-Hua Tan Xing-Long Chen Tian-Li Cheng Zhou Jiang Yong-Zhong Luo Le-Meng Zhang 

机构地区：[1]School of Life and Health Sciences,Hunan University of Science and Technology,Xiangtan 411201,Hunan Province,China [2]Department of Thoracic Medicine,Hunan Cancer Hospital,Changsha 410013,Hunan Province,China

出　　处：《World Journal of Clinical Cases》2024年第20期4091-4107,共17页世界临床病例杂志

基　　金：the National Natural Science Foundation of Hunan Province,No.2023JJ60039;Natural Science Foundation of Hunan Province National Health Commission,No.B202303027655;Natural Science Foundation of Changsha Science and Technology Bureau,No.Kq2208150;Wu Jieping Foundation of China,No.320.6750.2022-22-59,320.6750.2022-17-41;Guangdong Association of Clinical Trials(GACT)/Chinese Thoracic Oncology Group(CTONG);and Guangdong Provincial Key Lab of Translational Medicine in Lung Cancer,No.2017B030314120.

摘　　要：BACKGROUND Non-small cell lung cancer(NSCLC)is the primary form of lung cancer,and the combination of chemotherapy with immunotherapy offers promising treatment options for patients suffering from this disease.However,the emergence of drug resistance significantly limits the effectiveness of these therapeutic strategies.Consequently,it is imperative to devise methods for accurately detecting and evaluating the efficacy of these treatments.AIM To identify the metabolic signatures associated with neutrophil extracellular traps(NETs)and chemoimmunotherapy efficacy in NSCLC patients.METHODS In total,159 NSCLC patients undergoing first-line chemoimmunotherapy were enrolled.We first investigated the characteristics influencing clinical efficacy.Circulating levels of NETs and cytokines were measured by commercial kits.Liquid chromatography tandem mass spectrometry quantified plasma metabolites,and differential metabolites were identified.Least absolute shrinkage and selection operator,support vector machine-recursive feature elimination,and random forest algorithms were employed.By using plasma metabolic profiles and machine learning algorithms,predictive metabolic signatures were established.RESULTS First,the levels of circulating interleukin-8,neutrophil-to-lymphocyte ratio,and NETs were closely related to poor efficacy of first-line chemoimmunotherapy.Patients were classed into a low NET group or a high NET group.A total of 54 differential plasma metabolites were identified.These metabolites were primarily involved in arachidonic acid and purine metabolism.Three key metabolites were identified as crucial variables,including 8,9-epoxyeicosatrienoic acid,L-malate,and bis(monoacylglycerol)phosphate(18:1/16:0).Using metabolomic sequencing data and machine learning methods,key metabolic signatures were screened to predict NET level as well as chemoimmunotherapy efficacy.CONCLUSION The identified metabolic signatures may effectively distinguish NET levels and predict clinical benefit from chemoimmunotherapy in NSCLC patien

关 键 词：Non-small cell lung cancer CHEMOIMMUNOTHERAPY Neutrophil extracellular traps Metabolomics Machine learning 

分 类 号：R735.7[医药卫生—肿瘤]