Zhu Zhipeng, Song Mengyu, Li Wenhao, Li Mengying, Chen Sihan, Chen Bo
Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
Department of Clinical Medicine, The First Clinical College, Anhui Medical University, Hefei, China.
Front Oncol. 2021 Sep 20;11:695001. doi: 10.3389/fonc.2021.695001. eCollection 2021.
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 () 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.
肝细胞癌是一种常见的恶性肿瘤,预后较差,治疗效果不佳,且缺乏有效的生物标志物。在本研究中,利用肝细胞癌免疫相关基因的生物信息学分析构建了一个可预测患者预后的多基因联合标志物。从癌症基因组图谱(TCGA)数据库下载肝细胞癌的RNA表达数据,并从IMMPORT数据库获取免疫相关基因。通过Wilcox检验进行差异分析以获得差异表达基因。进行单因素Cox回归分析、lasso回归分析和多因素Cox回归分析以建立免疫基因的预后模型,共鉴定出5个基因()用于构建模型。这5个基因在肝癌组织中的表达水平与癌旁组织中的表达水平有显著差异。Kaplan-Meier生存曲线显示,根据预后模型计算的风险评分与肝癌患者的总生存期(OS)显著相关。受试者工作特征(ROC)曲线证实该预后模型具有较高的准确性。进行独立预后分析以证明风险值可作为独立的预后因素。然后,将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用作验证数据集,对上述步骤进行验证。此外,我们使用CIBERSORT软件和TIMER数据库进行免疫浸润研究,结果表明模型的5个基因和风险评分与免疫细胞含量有一定相关性。此外,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潜在的重要信号通路,且在预后模型中受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,也为免疫基因与肝细胞癌潜在通路之间的相关性提供了新思路。