Development of a colon adenocarcinoma prognostic model based on cuproptosis-related genes and analysis of immune...

Cuproptosis is a novel form of cell death that plays a crucial role in tumor proliferation, invasion, and metastasis, and is closely associated with the prognosis of various cancers. However, its specific role and underlying mechanisms in colon adenocarcinoma (COAD) remain unclear. This study aims to identify cuproptosis-related genes (CRGs) associated with the prognosis of COAD and construct a prognostic risk model based on the expression characteristics of these genes. A total of 541 COAD samples were collected from public databases and subjected to multi-omics analysis. Based on the expression levels of CRGs, consensus clustering was employed to classify COAD patients into two subtypes, followed by the identification of differentially expressed genes (DEGs). Subsequently, weighted gene co-expression network analysis (WGCNA) was used to integrate relevant genes, and a second round of clustering analysis was performed to refine COAD patients into three subtypes, enhancing both biological and clinical relevance. LASSO and Cox regression analyses were utilized to construct a prognostic risk model for COAD. The prognostic performance of the risk model was validated using receiver operating characteristic (ROC) curves. SEC61G, SCP2, LENG8, MTUS1 and MEGF6 were identified as core genes significantly associated with patient prognosis. Comprehensive analyses using CIBERSORT, ESTIMATE, MSIsensor, and GDSC revealed distinct patterns between high-risk and low-risk subgroups of COAD. Significant differences were observed in immune cell infiltration, tumor microenvironment (TME) characteristics, tumor mutation burden (TMB), stemness features, microsatellite instability (MSI), and drug sensitivity (all P < 0.05). Notably, pharmacogenomic analysis indicated that high-risk patients exhibited increased sensitivity to targeted drugs such as AMG.706 (a multi-kinase inhibitor) and PHA.665752 (a c-MET inhibitor), highlighting the potential clinical application value of this model in guiding personalized treatment strategies for COAD.