BackgroundThe aim of this study was to construct a prognostic model of colon cancer based on demethylation-related genes. An in-depth understanding of the relationship between the set of demethylated genes and colon cancer not only assists in revealing the pathogenesis of colon cancer but also provides strong support for future therapeutic strategies and individualized medicine. MethodsData were obtained from the TCGA database and the GEO-GSE39582 cohort. A risk score model for demethylation-related genes was developed using univariate Cox regression analysis and LASSO regression analysis. The accuracy and reliability of the model were confirmed using K–M survival analysis and ROC curve analysis. Additionally, a nomogram was created by integrating the risk score and clinicopathological variables. Finally, the biological function of the RCOR2 gene was verified by performing qPCR, MTT, colony formation, Transwell, and subcutaneous tumor formation assays in nude mice. ResultsWe constructed a risk score model containing 30 demethylation-related genes for predicting the survival risk of patients with colon cancer. COAD patients were categorized into high-risk and low-risk groups, and Kaplan–Meier (KM) curve analysis revealed that the high-risk group was associated with a worse prognosis. Univariate and multivariate Cox regression analyses validated the risk score as an independent prognostic factor for COAD. We also analyzed the differences in the sensitivity to nine chemotherapeutic agents and small molecule targeted drugs between the high-risk and low-risk groups. Moreover, we performed experiments in COAD cell lines and nude mice to verify that RCOR2 was differentially expressed between tumor tissues and normal tissues and that high RCOR2 expression promoted a malignant phenotype of colon cancer. ConclusionThis study demonstrated the potential roles of demethylation-related genes in colon cancer by conducting a comprehensive analysis and constructing a risk score. These findings also highlight the ability of these genes to indicate patient prognosis and tumor immune microenvironment. Furthermore, this study provides a reliable predictive tool that can assist in guiding the treatment and management of colon cancer patients.