Background The progression of gastric cancer (GC) is closely linked to macrophage polarization and protein lactylation; however, its underlying mechanisms remain poorly understood. This study aimed to elucidate the molecular mechanisms of GC using transcriptomic analysis.Methods Candidate genes were identified by intersecting differentially expressed genes with key module genes associated with protein lactylation and macrophage polarization. Protein-protein interaction analysis was performed to uncover interacting genes. Prognostic genes were determined using univariate Cox regression and machine learning techniques, with model accuracy assessed via training and validation datasets. Further, enrichment analysis, immune infiltration profiling, gene mutation analysis, and drug sensitivity assessments were conducted for high- and low-risk groups. Chromosomal localization, gene-gene interaction network analysis, and expression validation of prognostic genes were also performed.Results Two prognostic genes, ERCC6L and MYB, were identified as significant markers of prognosis through comprehensive analyses. A risk model based on these genes accurately predicted survival in patients with GC. Enrichment analysis revealed pathways such as the muscle myosin complex and adipogenesis as significantly involved in GC. Immune infiltration analysis identified 13 immune cell types, including monocytes, with strong associations to the prognostic genes. TTN, TP53, and MUC16 exhibited the highest mutation rates in both risk groups. Drug sensitivity analysis highlighted AZD.0530, CCT007093, DMOG, JNJ.26854165, and LFM.A13 as promising therapeutic candidates. ERCC6L is located on chromosome X, while MYB is located on chromosome 6. Gene-gene interaction network analysis revealed interactions between prognostic genes and other key genes. In both datasets, expression of prognostic genes was significantly higher in the GC cohort.Conclusion This study identified ERCC6L and MYB as key prognostic genes, facilitating the development of a risk model that offers novel insights into potential therapeutic strategies for GC.