These genes were then incorporated into LASSO regression analysis and a random forest model, leading to the selection of 11 genes in the metabolism-related prognostic signature, including SERPINE1, MEF2B, S100Z, AXIN2, IGFBP1, GRP, ADH4, APOH, KRT15, ADTRP, and ADRA1B. The model based on these genes was found to be both effective and robust in different patient cohorts, and multivariate Cox regression showed that the model could serve as an independent predictor of prognosis in GC patients. This evidence concerns the gene IGFBP1 and gastric cancer.