DataSheet_1_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.

肝细胞癌是一种预后不良、治疗效果欠佳且缺乏有效生物标志物的常见恶性肿瘤。本研究采用生物信息学分析方法，对肝细胞癌的免疫相关基因进行研究，构建了一个能够预测患者预后的多基因联合标志物。肝细胞癌的RNA表达数据来源于癌症基因组图谱（TCGA）数据库，免疫相关基因则来源于IMMPORT数据库。通过Wilcox检验进行差异分析，以获得差异表达基因。通过单因素Cox回归分析、lasso回归分析和多因素Cox回归分析，建立了免疫基因的预后模型，共识别出5个基因（HDAC1、BIRC5、SPP1、STC2、NR6A1）用于构建模型。肝细胞癌组织中5个基因的表达水平与癌旁组织中的表达水平存在显著差异。Kaplan-Meier生存曲线表明，根据预后模型计算出的风险评分与肝细胞癌的总生存期（OS）显著相关。受试者工作特征（ROC）曲线证实了预后模型具有高准确性。进行独立预后分析以证实风险值可以作为独立的预后因素。随后，利用国际癌症基因组联盟（ICGC）数据库中肝细胞癌的基因表达数据作为验证数据集以验证上述步骤。此外，我们使用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含量具有一定的相关性。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的信号通路，且该通路受到预后模型中某些基因的正向调控。总之，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，并就免疫基因与肝细胞癌潜在通路之间的相关性提供了新的见解。