Background: Metabolic reprogramming provides a new perspective for understanding cancer. The targeting of dysregulated metabolic pathways may help to reprogram the immune status of the tumor microenvironment (TME), thereby increasing the effectiveness of immune checkpoint therapy. Colorectal cancer (CRC), especially colon adenocarcinoma (COAD), is associated with poor patient survival. The aim of the present study was to identify novel pathways involved in the development and prognosis of COAD, and to explore whether these pathways could be used as targets to improve the efficacy of immunotherapy. Methods: Metabolism-related differentially expressed genes (MRDEGs) between tumor and normal tissues were identified using The Cancer Genome Atlas (TCGA) dataset, together with metabolism-related prognostic genes (MRPGs). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed separately for the MRDEGs and MRPGs. Gene Set Variation Analysis (GSVA) was also performed to explore the role of purine metabolism in COAD tumorigenesis. Consensus clustering of purine metabolism genes with the overall survival (OS) of patients and with anti-tumor immunity was also performed. Pearson correlation analysis was used to identify potential targets that correlated strongly with the expression of immune checkpoints. Results: A 6-gene signature that had independent prognostic significance for COAD was identified, together with a predictive model for risk stratification and prognosis. The most significantly enriched pathway amongst MRDEGs and MRPGs was purine metabolism. Differentially expressed purine metabolism genes could divide patients into two clusters with distinct prognosis and anti-tumor immunity. Further analysis suggested that purine metabolism was involved in anti-tumor immunity. Conclusions: This study confirmed the importance of metabolism-related pathways and in particular purine metabolism in the tumorigenesis, prognosis and anti-tumor immunity of COAD. We identified a 6-gene prognostic signature comprised of EPHX2, GPX3, PTGDS, NAT2, ACOX1 and CPT2. In addition, four potential immune-metabolic checkpoints (GUCY1A1, GUCY1B1, PDE1A and PDE5A) were identified, which could be used to improve the efficacy of immunotherapy in COAD.