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目的:通过生物信息学调研和分析,探讨对肝癌预后产生影响的独立危险因子及其与肝癌靶向药索拉非尼作用靶点的相互关联特征。方法:从TCGA数据库收集的248例肝癌患者中选取79例死亡患者的基因表达量、生存时间、生存状态数据,以中位生存时间360 d为界将这些死亡患者分为2组,用DEseq算法对这2组患者的基因表达量数据进行差异基因筛选。不考虑其他混杂因素,仅考虑差异基因表达单个因素与患者生存时间相关,用Kaplan-Meier法对差异基因进行单因素分析。将分析得到的差异基因表达量和患者生存时间、生存状态整合的矩阵用COX回归模型作生存分析,寻找对肝癌预后产生影响的独立危险因子。以STRING数据库为基础,将得到的差异基因与肝癌靶向药索拉非尼的作用靶点结合构建蛋白互联网络,探寻索拉菲尼作用靶点与差异基因的关系。结果:DEseq算法初筛得到52个有可能对肝癌预后产生影响的差异基因。Kaplan-Meier法分析得到26个与患者生存时间相关的差异基因。COX分析最终确认3个差异基因(SQSTM1、ANXA10和STMN1)为影响肝癌预后的独立危险因子。其中ANXA10为负向影响因素,而STMN1与SQSTM1为肝癌患者预后的正向影响因素。蛋白质相互作用网络显示,肝癌靶向药索拉非尼的作用靶点与多个差异基因密切相关。ANXA10参与钙离子结合和钙依赖性磷脂结合,STMN1和SQSTM1在多种信号通路中起重要作用。结论:ANXA10、STMN1和SQSTM1可能是对肝癌预后产生影响的独立危险因子,可作为未来开发靶向药物的作用靶点或患者预后预测指标。
OBJECTIVE: To investigate the correlation between independent risk factors influencing prognosis of hepatocellular carcinoma and its target of sorafenib targeting hepatocellular carcinoma through bioinformatics investigation and analysis. Methods: The gene expression, survival time and survival status of 79 patients with liver cancer were collected from TCGA database. The patients were divided into two groups according to the median survival time of 360 days. The DEseq algorithm Differential gene screening was performed on the gene expression data of these two groups of patients. Regardless of other confounding factors, only a single factor for differential gene expression was considered in relation to patient survival, and a univariate analysis of the differential genes was performed using the Kaplan-Meier method. The analysis of the differential gene expression and patient survival time and survival status of the matrix integrated with COX regression model for survival analysis to find an independent risk factor for the prognosis of liver cancer. Based on the STRING database, the differentially expressed genes were combined with the target of liver cancer targeted drug sorafenib to construct a protein Internet, and to explore the relationship between sorafenib target genes and differential genes. Results: There are 52 differentially expressed genes that may affect the prognosis of liver cancer by DESEQ algorithm. Kaplan-Meier analysis of 26 patients with the survival time-related differences in the gene. COX analysis finally confirmed that three differential genes (SQSTM1, ANXA10 and STMN1) were independent risk factors affecting the prognosis of HCC. Among them, ANXA10 is a negative influence factor, while STMN1 and SQSTM1 are positive factors of prognosis of HCC patients. The protein interaction network showed that the target of liver cancer targeting drug sorafenib was closely related to multiple differential genes. ANXA10 is involved in calcium-binding and calcium-dependent phospholipid binding, and STMN1 and SQSTM1 play important roles in various signaling pathways. CONCLUSIONS: ANXA10, STMN1 and SQSTM1 may be independent risk factors for the prognosis of hepatocellular carcinoma and can be used as the target of future development of targeted drugs or predictors of prognosis of patients.