铜死亡相关基因在肝细胞癌中的表达及其临床意义

背景与目的：肝细胞癌（HCC）是一种全球常见的恶性肿瘤,具有高复发率和高病死率。铜死亡是一种新型的程序性细胞死亡,涉及肿瘤细胞的增殖和生长、血管生成和转移。因此,本研究探讨铜死亡相关基因（CRGs）在HCC中的表达与预后的关系,并建立预后相关的列线图模型以及分析CRGs与HCC免疫细胞浸润的关系。方法：使用R语言“limma”包对TCGA数据库下载的HCC组织与正常组织的数据中CRGs进行差异表达分析;“clusterProfiler”包进行GO和KEGG分析;单因素Cox回归分析筛选与预后相关的CRGs,LassoCox回归分析构建HCC中CRGs相关预后评分模型;“ggsurvplot”包以总生存（OS）为结局绘制Kaplan-Meier生存曲线;“survival ROC”包绘制ROC曲线评估预后评分的准确性;“regplot”和“rms”包绘制列线图和校准曲线;利用TIMER数据库分析CRGs的表达与6种免疫细胞丰度之间的关系。结果：与正常组织相比,HCC组织19个CRGs中的16个有差异表达（上调：PDHB、PDHA1、MTF1、LIPT1、LIPT2、LIAS、GLS、DL...

Expressions of cuproptosis-related genes in hepatocellular carcinoma and their clinical significance

Background and Aims Hepatocellular carcinoma (HCC) is a common malignancy with a high recurrence and mortality rate. Cuproptosis is a new type of programmed cell death involved in tumor cells'' proliferation, growth, angiogenesis, and metastasis. Therefore, this study aims to investigate the relationship between the expression of cuproptosis-related genes (CRGs) and the prognosis in HCC, establish a prognosis-related nomogram model, and analyze the association of CRGs with the immune cell infiltration in HCC.Methods Differential expression analysis of CRGs in the TCGA database was performed using the R language "limma" package; the "clusterProfiler" package was used for GO and KEGG analysis; the prognostic CRGs were screened by univariate Cox regression analysis; the prognostic scoring model based on CRGs for HCC was constructed by Lasso-Cox regression analysis; the "ggsurvplot" package drew the Kaplan-Meier survival curve draws using overall survival (OS) as the outcome variable; the "survival ROC" package created the ROC curve for assessing the accuracy of the prognostic score; the nomogram and the calibration curves were drawn by the ''regplot'' and ''rms'' packages; the associations between the expression of CRGs and the abundance of six immune cells were analyzed using the TIMER database.Results Among the 19 CRGs, there were 16 differentially expressed ones in HCC tissue compared with normal tissue (up-regulation: PDHB, PDHA1, MTF1, LIPT1, LIPT2, LIAS, GLS, DLD, DLST, DLAT, CDKN2A, and ATP7A; down-regulation: SLC31A1, GCSH, DBT, and NLRP3), and NLRP 2 had the highest mutation frequency of 12%. GO, and KEGG analyses showed that CRGs were enriched in signaling pathways such as the tricarboxylic acid cycle, carbon metabolism, pyruvate metabolism, glycolysis/gluconeogenesis, and platinum drug resistance. Three CRGs (CDKN2A, GLS, and DLAT) that affected the OS were screened by univariate Cox regression analysis and LASSO Cox regression analysis for the construction of the prognostic model, and the prognostic score was constructed using regression coefficient: risk score=0.22×DLAT (expression level) + 0.11×CDKN2A (expression level) + 0.03×GLS (expression level). The Kaplan-Meier curve analysis showed that the HCC patients with high-risk scores had a poor prognosis (P<0.05), and the model prediction performance was evaluated by the time-dependent ROC curve of the risk model, and the AUC at 1, 3, and 5 years was 0.741,0.657 and 0.633, respectively. The nomogram was constructed by incorporating age, sex, T stage, N stage, M stage, pathological classification, CDKN2A, GLS, and DLAT. The calibration map showed good consistency between the nomogram prediction and the actual observation. There were positive correlations of GLS, DLAT, and CDKN2A with HCC immune cell infiltration and a significant correlation with immune checkpoints PDCD 1, CD274, and HAVCR2 (all P<0.05). Further analysis indicated that the higher CDKN2A, GLS, and DLAT expression in HCC tissue, the later the Barcelona pathological stage, the worse the histological grade in patients (all P<0.05).Conclusion Gene signatures associated with cuproptosis can be used as potential prognostic predictors for HCC patients and may provide new insights into the treatment of HCC.