Abstract
Lung cancer is one of the most harmful cancers in the world, endangering the lives and health of many people. Although there are various methods to treat lung cancer at present, but lung cancer is asymptomatic in the early stages and has a high recurrence rate after late treatment which make it difficult to cure with conventional treatments. Drug combinations for the treatment of lung cancer have been used in many clinical studies. In this study, we constructed a recurrence model of Non-Small Cell Lung Cancer (NSCLC) lung cancer and used a combination of Ginsenoside H dripping pills (GH) and vinorelbine (NVB) to treat the recurrence of lung cancer. The results showed that the inhibition rate of the combined treatment of GH and NVB is 74.81% which is significantly higher than the therapeutic effect of separate use. We also used GC-TOF/MS-based metabolomics to identify differentially abundant metabolites in relapse models and explore biomarker trends. We found that there are 12 metabolite differences in the abundance of metabolites between the treatment groups (GH group, NVB group and GH-NVB group) and the model group, such as glucose 6-phosphate, palmitoleic acid, linoleic acid, guanine, allantoic acid. The differences in these metabolites involve glucose and lipid metabolism, amino acid metabolism, and purine metabolism. We further analyzed the changes in the content of these metabolites and found that the combined use of GH and NVB can regulate purine metabolism, folate synthesis, and thiamine metabolism, ultimately reducing the abnormal increase in alkaline phosphatase (AP). This study provides a method for the treatment of lung cancer and some biomarkers for the detection of lung cancer.