Retrospective Study
Copyright ©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Clin Cases. Jul 16, 2024; 12(20): 4091-4107
Published online Jul 16, 2024. doi: 10.12998/wjcc.v12.i20.4091
Machine learning based on metabolomics unveils neutrophil extracellular trap-related metabolic signatures in non-small cell lung cancer patients undergoing chemoimmunotherapy
Yu-Ning Li, Jia-Lin Su, Shu-Hua Tan, Xing-Long Chen, Tian-Li Cheng, Zhou Jiang, Yong-Zhong Luo, Le-Meng Zhang
Yu-Ning Li, Jia-Lin Su, Shu-Hua Tan, Xing-Long Chen, School of Life and Health Sciences, Hunan University of Science and Technology, Xiangtan 411201, Hunan Province, China
Yu-Ning Li, Jia-Lin Su, Xing-Long Chen, Tian-Li Cheng, Zhou Jiang, Yong-Zhong Luo, Le-Meng Zhang, Department of Thoracic Medicine, Hunan Cancer Hospital, Changsha 410013, Hunan Province, China
Author contributions: Zhang LM contributed to study design and manuscript preparation; Li YN and Su JL contributed to manuscript writing and data analysis; Su JL, Li YN, Tan SH, and Chen XL contributed to molecular experiments, statistical analysis, and machine learning study; Luo YZ and Jiang Z contributed to clinical data collection; All authors read and approved the final manuscript.
Supported by 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.
Institutional review board statement: The current research follows the TCGA data access policies and publication guidelines. All data submitted to the TCGA database has been ethically approved. The TCGA data citation guidelines and licenses have been followed. The clinical samples collection was approved by the Institutional Ethics Committee of Hunan Cancer Hospital.
Informed consent statement: Written informed consent was obtained from all enrolled patients.
Conflict-of-interest statement: All authors declare no conflicts of interest.
Data sharing statement: All data generated or analyzed during this study are included in this published article and its supplementary information files. Additional datasets are available from the corresponding author.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Le-Meng Zhang, MD, Chief Physician, 1 Department of Thoracic Medicine, Hunan Cancer Hospital, No. 283 Tongzipo Road, Yuelu District, Changsha 410013, Hunan Province, China. zhanglemeng@hnca.org.cn
Received: March 9, 2024
Revised: May 10, 2024
Accepted: May 28, 2024
Published online: July 16, 2024
Processing time: 112 Days and 21 Hours
Abstract
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 patients.

Keywords: Non-small cell lung cancer, Chemoimmunotherapy, Neutrophil extracellular traps, Metabolomics, Machine learning

Core Tip: This study found that high neutrophil extracellular trap (NETs) levels in patients with non-small cell lung cancer were associated with poor chemotherapy immunotherapy outcomes. Further study found that 54 different metabolites existed between the high NET and low NET groups. Through screening by a machine learning algorithm, three metabolites, 8,9-epoxy-eicosatrienoic acid, L-malate, and bis(monoacylglycerol)phosphate (18:1/16:0), were found to be biomarkers to predict the effect of chemoimmunotherapy in non-small cell lung cancer patients. This study provided important clues to understanding the relationship between NET levels and the effect of chemoimmunotherapy.