Metabolomic Insights into the Predictive Landscape of Neoadjuvant Immunochemotherapy in Gastric Cancer: Towards Precision Medicine with Machine Learning

Neoadjuvant Immunochemotherapy (NAIC) shows promising application prospects in the treatment of (Gastric Cancer, GC) patients. However, the differences between individual patients and the issue of treatment resistance significantly affect whether patients could truly benefit from this treatment. This study conducted metabolomic analysis of 369 plasma samples from 108 gastric cancer patients following NAIC treatment to characterize their metabolic profiles for more accurate therapeutic efficacy prediction. Machine learning was used to build a GC treatment response prediction model 21-PM based on the expression levels of baseline metabolites. Two efficacy monitoring models, 11-MMI and 13-MMP, were developed using R-specific metabolites based on imaging and histology outcomes, respectively. In conclusion, the outcomes of this study offered strong evidence for the advancement of precision medicine in GC by exposing the metabolic landscape of GC patients after NAIC treatment and efficiently creating models that could independently predict prediction and monitor treatment.