Identification of cuproptosis and ferroptosis-related subtypes and development of a prognostic signature in colon cancer

Colon cancer, as a highly prevalent malignant tumor globally, poses a significant threat to human health. In recent years, ferroptosis and cuproptosis, as two novel forms of cell death, have attracted widespread attention for their potential roles in the development and treatment of colon cancer. However, the investigation into the subtypes and their impact on the survival of colon cancer patients remains understudied. In this study, utilizing data from TCGA and GEO databases, we examined the expression differences of ferroptosis and cuproptosis-related genes in colon cancer and identified two subtypes. Through functional analysis and bioinformatics methods, we elucidated pathway differences and biological characteristics between these two subtypes. By leveraging differential genes between the two subtypes, we constructed a prognostic model using univariate Cox regression and multivariate Cox regression analysis as well as LASSO regression analysis. Further survival analysis and receiver operating characteristic curve analysis demonstrated the models high accuracy. To enhance its clinical utility, we evaluated the clinical significance of the model and constructed a nomogram, significantly improving the predictive ability of the model and providing a new tool for prognostic assessment of colon cancer patients. Subsequently, through immune-related analysis, we revealed differences in immune cell infiltration and immune function between high- and low-risk groups. Further analysis of the relationship between the model and immune cells and functions revealed potential therapeutic targets. Drug sensitivity analysis revealed associations between the expression of model-related genes and drug sensitivity, suggesting their involvement in tumor resistance through certain mechanisms. AZD8055_1059, Bortezomib_1191, Dihydrorotenone_1827, and MG-132_1862 were more sensitive in the high-risk group. Finally, we analyzed differential expression of model-related genes between tumor tissues and normal tissues, validated through real-time quantitative PCR. In summary, our study provides a relatively accurate prognostic tool for colon cancer patients, offering guidance for treatment selection and indicating the potential of immunotherapy in colon cancer.