Identification and Validation of a Prognostic Model Based on Five Copper Metabolism-related Genes in Hepatocellular Carcinoma

Abstract

Abstract objective Hepatocellular carcinoma (HCC) is a prevalent malignancy, with high mortality and easy recurrence. Copper metabolism regulates tumor’s development and progression via several biological pathway and has significant clinical value in HCC. Thus, we identified potential gene biomarkers related to copper metabolism to establish a novel predictive model to predict the survival of HCC patients.Methods Our research utilized various statistical analysis methods to construct a new model to predict the prognosis of HCC. The LASSO-COX algorithm shrank the coefficients of the predictive factor. The construction of the model was in Cancer Genome Atlas (TCGA), and the validation was in International Cancer Genome Consortium (ICGC) cohort. We performed GO and KEGG analyses to enrich the function annotations of the selected genes. The nomogram and receiver operating characteristic (ROC) curve analysis were used to evaluate the model's performance.Results A risk-predictive scoring model of 5 copper metabolism-related genes (AOC1, LOX, STEAP4, MAPT, and LCAT) was constructed by the data from TCGA after LASSO-COX regression analysis and validated by the data from ICGC. Moreover, GO and KEGG analyses of the predictive signature revealed that the signature was mainly associated with copper ion binding, metal ion, oxidoreductase activity acting on the CH-NH2 group of donors, and tryptophan metabolism. The OS of the high-risk group was significantly lower than that of the low-risk group. Time-dependent ROC evaluated the performance of this model with AUC values for 1, 2, and 3 years of 0.749, 0.741, and 0.723 in OS, respectively. The nomogram, combining the risk score and clinical features, showed a solid prognostic ability in HCC.Conclusion A reliable predictive score model was constructed, which could be a valuable prognostic indicator and a clinical treatment selection guiding in HCC with 5 CMRGs.