Published online Jul 16, 2024. doi: 10.12998/wjcc.v12.i20.4091
Revised: May 10, 2024
Accepted: May 28, 2024
Published online: July 16, 2024
Processing time: 112 Days and 21 Hours
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.
To identify the metabolic signatures associated with neutrophil extracellular traps (NETs) and chemoimmunotherapy efficacy in NSCLC patients.
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. Li
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.
The identified metabolic signatures may effectively distinguish NET levels and predict clinical benefit from chemoimmunotherapy in NSCLC patients.
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.