【摘要】 结合一种新颖的差异共表达分析方法,应用生物信息学工具,在多个肝组织数据集中获取与肝癌相关的基因模块和枢纽基因(Hub gene)。传统的差异表达分析方法只关注平均表达水平的差异,忽略了基因共同表达的信息。与传统差异表达分析方法相比,本方法可以有效地从基因相互作用角度识别出肝癌的关键基因。关键基因主要参与了p53、细胞周期、癌症和Wnt等重要的信号通路,其中HDAC1、APOB、UBE2D1、SOCS1和ELAVL1基因既没有显著的差异表达,也没有参与重要的癌症通路,但经鉴定这些基因与肝癌的发生和发展密切相关。生存曲线分析表明,有7个基因差异表达使得肝癌患者总体生存率显著降低(p<0.01)。这些结果可以作为差异表达分析研究结论的补充,为肝癌的诊断和治疗靶点选择及预后判断提供参考。更多还原

【Abstract】 A novel differential coexpression analysis method is proposed by using bioinformatics tools to obtain gene modules and hub genes related to liver cancer from multiple liver tissue data sets. The traditional differential expression analysis method only pays attention to the difference in average expression level and ignores the information of gene coexpression. Compared with traditional differential expression analysis method, this method can effectively identify the key genes of liver cancer from the perspective of gene interaction. Key genes are mainly involved in important signaling pathways such as p53, cell cycle, cancer, Wnt, and so on. Among them, genes HDAC1, APOB, UBE2 D1, SOCS1, ELAVL1 neither have significant differential expression nor participate in important cancer pathways, but these genes have been identified as closely related to the occurrence and development of liver cancer. Survival curve analysis shows that the differential expression of 7 genes significantly reduces the overall survival rate of liver cancer patients(p<0.01). These results can be used as a supplement to the research conclusions of differential expression analysis, and provide references for the diagnosis and treatment of liver cancer, as well as for prognostic judgment.更多还原