Bioinformatics analysis-based construction of a prognostic prediction model to investigate the relationship between specific solute carrier protein (SLC)-related genes and gastric cancer (GC) in immunity and prognosis. RNA sequencing and clinical data were downloaded from the cancer genome atlas (TCGA) and gene expression omnibus (GEO) databases. Differential expression of SLC-related genes was extracted using R software. Univariate and multivariate Cox regression analyses were employed to construct a risk prediction model for GC prognosis based on SLC-related genes closely associated with survival. The prognostic performance of the model was validated through Kaplan-Meier survival analysis, regression analysis, and receiver operating characteristic (ROC) curves. Correlations between risk scores and immune-infiltrating cells, tumor immune microenvironment, and clinical features were assessed. The impact of SLC43A3 knockdown on the proliferation and migration of BGC-823 cells was evaluated via CCK-8, scratch wound healing, and Transwell assays. A total of 115 differentially expressed SLC-related genes were identified, with 14 genes linked to prognosis. Eight genes were selected to establish the prognostic risk scoring model. The SLC-related gene prognostic model demonstrates high predictive value and is significantly associated with heterogeneity in immune cell infiltration, providing important references for personalized medication in GC patients. Functional experiments revealed that SLC43A3 significantly promotes the proliferation, migration, and invasion of gastric cancer cells. This study may suggest novel therapeutic targets for GC targeted therapy research.