Prognostic Analysis of Cuproptosis-related Genes in Gastric Cancer

Abstract

Background Gastric cancer (GC) is a serious malignant tumor with high morbidity and mortality and poor prognosis worldwide. Cuproptosis is a new type of cell death that can induce proteotoxic stress and ultimately lead to cell death, which is associated with tumor progression, prognosis and immune response. In this study, the expression of cuproptosis-related genes (CRGs) was analyzed to predict the prognosis of GC patients. Methods We analyzed the expression and mutation status of CRGs in 407 GC patients from TCGA database and 433 GC patients from GEO database, and correlated them with clinical prognosis. The R software package was used for classification. The relationship between different groups and prognosis, risk genes and immune microenvironment was further analyzed. LASSO cox algorithm was used to construct a cuproptosis risk model according to 8 risk genes. Finally, we constructed nomogram and calibration curve to predict the survival probability of patients and performed antitumor drug sensitivity analysis. Results Based on the analysis of TCGA and GEO databases, there were significant differences in the expression level and prognosis of CRGs in GC. We used consensus clustering algorithm to classify CRGs, and found 2 clusters of CRGs characterized by immune cell infiltration, and obtained 195 differentially expressed genes. We further obtained 8 risk genes by multivariate Cox regression analysis and constructed a cuproptosis risk model. Receiver operating characteristic curve (ROC) and principal component analysis (PCA) show that the model has accurate prediction ability. Risk score is an independent prognostic factor for GC patients. In addition, patients with low CRGs score have higher tumor mutation burden and immune activation level, and better survival prognosis. However, patients with high CRGs score showed poor survival and immunosuppression. Conclusion CRGs are involved in the occurrence and development of GC. Our cuproptosis risk model provides a new research strategy for predicting the prognosis of GC patients. Meanwhile, the results of drug sensitivity analysis can provide valuable drug candidate clues for clinical treatment of GC.