Cuproptosis-Related genes in the prognosis of colorectal cancer and their correlation with the tumor microenvironment

Abstract

Colorectal cancer (CRC) is a common tumor disease of the digestive system with high incidence and mortality. Cuproptosis has recently been found to be a new form of cell death. The clinical significance of cuproptosis-related genes (CRGs) in CRC is not clear. In this study, The Cancer Genome Atlas Colon and Rectal Cancer dataset was used to analyze the relationship between CRGs and clinical characteristics of CRC by differential expression analysis and Kaplan–Meier survival (K-M) analysis. Based on CRGs, prognosis model and risk score of CRC was constructed in COADREAD by multivariate Cox analysis. Receiver operating curves (ROC) analysis, K-M analysis and calibration analysis in GDC TCGA Colon Cancer dataset were applied to validating model. Subsequently, the relationship between risk score of CRC and immune microenvironment was analyzed by multiple immune score algorithms. Finally, we found that most CRGs were differentially expressed between tumors and normal tissues. Some CRGs were differentially expressed among different clinical characteristics. K-M analysis showed that the CRGs were related to overall survival (OS), disease-specific survival, and progression-free survival. Subsequently, DLAT and CDKN2A were identified as risk factors for OS in CRC by multivariate Cox analysis, and the risk score was established. K–M analysis showed that there was a significant difference in OS between the high-risk and low-risk groups, which were grouped by risk score median. ROC analysis showed that the risk score performs well in predicting the 1-year, 3-year and 5-year OS. Enrichment analysis showed that the differentially expressed genes between the high- and low-risk groups were enriched in immune-related signaling pathways. Further analysis showed that there were significant differences in the levels of immune cells and stromal cells between the high- and low-risk groups. The high-risk group had higher levels of immune cells and interstitial cells. At the same time, the high-risk group had a higher immune escape ability, and the predicted immune treatment response in the high-risk group was poor. In conclusion, CRGs can be used as prognostic factors in CRC and are closely related to the levels of immune cells and stromal cells in the tumor microenvironment.

Characterization of a cuproptosis-related signature to evaluate immune features and predict prognosis in colorectal cancer

“…Recently, several papers have been published on the use of a model based on cuproptosis-associated genes for predicting prognosis (32)(33)(34)(35). However, these papers suffer from several limitations, including a small number of genes in their signature, relatively low prognostic power, and illogical combination with other cell death types like ferroptosis.…”

Identification of cuproptosis‐related subtypes and tumor microenvironment characteristics in colon cancer

“…The value of CRGs in patients with COAD was further corroborated in related articles. In Wu's study, 53 they established a model using multivariate Cox regression analysis and constructed a prognostic risk score accordingly and the clinical and immunological differences in patients with different risk scores were further investigated. Similarly in Xu's study, 54 they screened out the modeled genes from clustering results which were based on CRGs, and a total of 11 genes were finally generated.…”

Cuproptosis in cancer: biological implications and therapeutic opportunities

“…Lower CRIRS, contrarily, suggested a more favorable response to chemotherapy/targeted drugs and immunotherapy [182]. Moreover, several studies established the CRG risk score signature based on cuproptosis-associated gene expression and confirmed that the CRG risk score is a strong prognostic marker and could contribute to guiding more effective treatment regimens [183][184][185][186]. Compared with a single gene expression level, this risk scoreassociated assessment based on machine deep learning is more accurate in reflecting the role of cuproptosis in tumorigenesis and treatment because cuproptosis is a multiplegene-regulated biological process.…”