| ISSN |
2307-8960 (online) |
| 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: http://creativecommons.org/licenses/by-nc/4.0/ |
| Copyright |
©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved. |
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Engineering, Biomedical |
| Manuscript Type |
Retrospective Study |
| Article Title |
Machine learning based on metabolomics unveils neutrophil extracellular trap-related metabolic signatures in non-small cell lung cancer patients undergoing chemoimmunotherapy
|
| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Yu-Ning Li, Jia-Lin Su, Shu-Hua Tan, Xing-Long Chen, Tian-Li Cheng, Zhou Jiang, Yong-Zhong Luo and Le-Meng Zhang |
| ORCID |
|
| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| National Natural Science Foundation of Hunan Province |
2023JJ60039 |
| Natural Science Foundation of Hunan Province National Health Commission |
B202303027655 |
| atural Science Foundation of Changsha Science and Technology Bureau |
Kq2208150 |
| Wu Jieping Foundation of China |
320.6750.2022-22-59, 320.6750.2022-17-41 |
| HUI LAN PUBLIC FOUNDATION |
HL-HS2020-1 |
| Guangdong Association of Clinical Trials (GACT) /Chinese Thoracic Oncology Group (CTONG) and Guangdong Provincial Key Lab of Translational Medicine in Lung Cancer |
Grant No. 2017B030314120 |
| science and technology innovation Program of Hunan Province |
2023SK4024 |
|
| 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 |
| Key Words |
Non-small cell lung cancer; Chemoimmunotherapy; Neutrophil extracellular traps; Metabolomics; Machine learning |
| Core Tip |
This study found that high neutrophil extracellular traps (NETs) levels in patients with non-small cell lung cancer (NSCLC) were associated with poor chemotherapy immunotherapy outcomes. Further study found that 54 different metabolites existed between the H_NETs and L_NETs groups, mainly involved in arachidonic acid and purine metabolism. Through screening by machine learning algorithm, it was found that 3 metabolites, 8, 9-epoxy-eicosatrienoic acid, L-malic acid and Bis(monoacylglycerol)phosphates (18:1/16:0), may be biomarkers to predict the effect of chemotherapy immunotherapy in NSCLC patients. This study provides important clues to understanding the relationship between NETs levels and the effect of chemotherapy immunotherapy. |
| Publish Date |
2024-07-01 06:03 |
| Citation |
<p>Li YN, Su JL, Tan SH, Chen XL, Cheng TL, Jiang Z, Luo YZ, Zhang LM. Machine learning based on metabolomics unveils neutrophil extracellular trap-related metabolic signatures in non-small cell lung cancer patients undergoing chemoimmunotherapy. <i>World J Clin Cases</i> 2024; 12(20): 4091-4107</p> |
| URL |
https://www.wjgnet.com/2307-8960/full/v12/i20/4091.htm |
| DOI |
https://dx.doi.org/10.12998/wjcc.v12.i20.4091 |
