A novel prognostic prediction model of cuprotosis-related genes signature in hepatocellular carcinoma

Abstract

Background: Cuprotosis is a recently discovered copper-dependent cell death mechanism that relies on mitochondrial respiration. However, the role of cuprotosis-related genes (CRGs) in hepatocellular carcinoma (HCC) and their prognostic significances remain unknown.Methods: Based on the recently published CRGs, the LASSO Cox regression analysis was applied to construct a CRGs risk model using the gene expression data from the International Cancer Genome Consortium as a training set, followed by validation with datasets from The Cancer Genome Atlas and the Gene Expression Omnibus (GSE14520). Functional enrichment analysis of the CRGs was performed by single-sample gene set enrichment analysis.Results: Five of the 13 previously published CRGs were identified to be associated with prognosis in HCC. Kaplan-Meier analysis suggested that patients with high-risk scores have a shorter overall survival time than patients with low-risk scores. ROC curves indicated that the average AUC was more than 0.7, even at 4 years, and at least 0.5 at 5 years. Moreover, addition of this CRG risk score can significantly improve the efficiency of predicting overall survival compared to using traditional factors alone. Functional analysis demonstrated increased presence of Treg cells in patients with high-risk scores, suggesting a suppressed immune state in these patients. Finally, we point to the possibility that novel immunotherapies such as inhibitors of PDCD1, TIGIT, IDO1, CD274, CTLA4, and LAG3 may have potential benefits in high-risk patients.Conclusion: We constructed a better prognostic model for liver cancer by using CRGs. The CRG risk score established in this study can serve as a potentially valuable tool for predicting clinical outcome of patients with HCC.

Identification of ASF1A and HJURP by global H3–H4 histone chaperone analysis as a prognostic two-gene model in hepatocellular carcinoma

“…4 ). We compared our two-gene model with other previous HCC prognostic models 28 , 29 , and the performance of our model is comparable with previously published models by comparing the areas of under the curve (AUCs) among models to predict 1-, 3-, and 5-year survival in HCC patients. In addition, we used prognostic information-containing tissue microarray data as a validation set to verify the model at the protein level.…”