Cuproptosis-related genes to establish prognostic model and predict the immunotherapy response in cervical cancer

Abstract

Abstract Background Cuproptosis is a new type of cell death recently discovered. Some studies have found that cuproptosis- related lncRNA is related to the prognosis of cervical cancer. However, there are few studies on the relationship between cuproptosis-related genes and immune infiltration and prognosis of cervical cancer. The objective of this study was to investigate the prognosis of cervical cancer and cuproptosis-related genes and the response to immunotherapy. Methods The clinical data of cervical cancer patients were downloaded from TCGA database, and randomly divided into training group and validation group. The cuproptosis -related genes was screened out by univariate Cox, Lasso-Cox and multivariate Cox analysis, and the risk model was constructed. The OS of two subgroups and the whole cohort were analyzed by Kaplan-Meier curve, and the prognostic value of the model was verified by ROC curve and PCA. The independent prognostic value of clinical features and risk score was analyzed by univariate and multivariate analysis. Then, the gene ontology (GO), Kyoto Gene and Genome Encyclopedia of Genes and Genomes (KEGG) were analyzed to illustrate the difference genes of the biological characteristics of the two risk groups, and the drug sensitivity between the two subgroups was also analyzed. Results We constructed five genes related to the death of copper (FDX1, ARF1, APP, HSF1, MT1A). From the survival curve of risk score, whether it is training group, validation group or overall OS, the total survival time of low-risk group is far longer than that of high winds and obstacles, and it has a good prognosis (P < 0.001). In addition, by univariate and multivariate Cox analysis, risk score and T (tumor size) were independent prognostic factors (P < 0.001). According to the receiver operating characteristic curve (ROC) and PCA, the prognosis model of this study can achieve high accuracy, with the area under the curve (AUC) of 1 year, 3 years and 5 years being 0.751, 0.736 and 0.748 respectively. We also use classification ROC analysis to evaluate the sensitivity and specificity of risk score and other clinical features such as age, grade and stage. The results of enrichment analysis showed that extracellular matrix, extracellular structure and external packaging structure were closely related to genes. We also found that the sensitivity of 24 drugs is closely related to the risk score, which can provide accurate therapeutic drugs for different groups of patients in time. Conclusion Our study established a prognostic risk model composed of 5-cuproptosis related genes, and proved that the model has obvious relationship with the prognosis of patients, which can accurately predict the overall survival of patients. And patients with low risk score are more likely to benefit from immunotherapy and the enrichment of immune cells is higher.