DataSheet_3_Identification, Verification and Pathway Enrichment Analysis of Prognosis-Related Immune Genes in Patients With Hepatocellular Carcinoma.xlsx

Hepatocellular carcinoma is a common malignant tumor with poor prognosis, poor treatment effect, and lack of effective biomarkers. In this study, bioinformatics analysis of immune-related genes of hepatocellular carcinoma was used to construct a multi-gene combined marker that can predict the prognosis of patients. The RNA expression data of hepatocellular carcinoma were downloaded from The Cancer Genome Atlas (TCGA) database, and immune-related genes were obtained from the IMMPORT database. Differential analysis was performed by Wilcox test to obtain differentially expressed genes. Univariate Cox regression analysis, lasso regression analysis and multivariate Cox regression analysis were performed to establish a prognostic model of immune genes, a total of 5 genes (HDAC1, BIRC5, SPP1, STC2, NR6A1) were identified to construct the models. The expression levels of 5 genes in HCC tissues were significantly different from those in paracancerous tissues. The Kaplan-Meier survival curve showed that the risk score calculated according to the prognostic model was significantly related to the overall survival (OS) of HCC. The receiver operating characteristic (ROC) curve confirmed that the prognostic model had high accuracy. Independent prognostic analysis was performed to prove that the risk value can be used as an independent prognostic factor. Then, the gene expression data of hepatocellular carcinoma in the ICGC database was used as a validation data set for the verification of the above steps. In addition, we used the CIBERSORT software and TIMER database to conduct immune infiltration research, and the results showed that the five genes of the model and the risk score have a certain correlation with the content of immune cells. Moreover, through Gene Set Enrichment Analysis (GSEA) and the construction of protein interaction networks, we found that the p53-mediated signal transduction pathway is a potentially important signal pathway for hepatocellular carcinoma and is positively regulated by certain genes in the prognostic model. In conclusion, this study provides potential targets for predicting the prognosis and treatment of hepatocellular carcinoma patients, and also provides new ideas about the correlation between immune genes and potential pathways of hepatocellular carcinoma.

肝细胞癌作为一种预后不良、治疗反应欠佳且缺乏有效生物标志物的常见恶性肿瘤，本研究通过生物信息学分析肝细胞癌的免疫相关基因，构建了一个多基因联合标记物，用以预测患者的预后。从癌症基因组图谱（TCGA）数据库中下载了肝细胞癌的RNA表达数据，并从IMMPORT数据库中获取了免疫相关基因。通过Wilcox检验进行差异分析，以获得差异表达基因。通过单变量Cox回归分析、lasso回归分析和多变量Cox回归分析建立免疫基因的预后模型，共确定了5个基因（HDAC1、BIRC5、SPP1、STC2、NR6A1）用于构建模型。在肝细胞癌组织和邻近癌组织中，这5个基因的表达水平存在显著差异。Kaplan-Meier生存曲线显示，根据预后模型计算出的风险评分与肝细胞癌的总体生存（OS）显著相关。接收者操作特征（ROC）曲线证实了该预后模型具有较高的准确性。通过独立预后分析证明，风险值可作为独立的预后因素。随后，利用ICGC数据库中肝细胞癌的基因表达数据作为验证数据集对上述步骤进行验证。此外，我们运用CIBERSORT软件和TIMER数据库进行了免疫浸润研究，结果显示模型中的5个基因和风险评分与免疫细胞的内容具有一定相关性。进一步通过基因集富集分析（GSEA）和蛋白质相互作用网络的构建，我们发现p53介导的信号转导通路是肝细胞癌的一个潜在重要信号通路，且由预后模型中的某些基因正调控。综上所述，本研究为预测肝细胞癌患者的预后和治疗提供了潜在靶点，同时也为探讨免疫基因与肝细胞癌潜在途径之间的关系提供了新的见解。