Abstract：
BACKGROUND: This study aimed to identify potential subtypes of hepatocellular carcinoma (HCC) associated with cirrhosis and to investigate key markers using bioinformatic analysis of gene expression datasets-0.
METHODS: Three data sets (GSE17548, GSE56140, and GSE87630) were extracted from the Gene Expression Omnibus (GEO) database and normalized using the Limma package in R. Principal component analysis (PCA) and cluster analysis was performed to examine data distribution and identify subtypes. Differential gene expression analysis was performed using the Limma software package. Protein-protein interaction analysis and functional annotation were performed using the STRING database and Cytoscape software. Important signaling pathways and processes were identified using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway Analysis.
RESULTS: The analysis revealed different subtypes of HCC associated with cirrhosis and identified several key genes, including CCNB2, MCM4, and CDC20, with strong binding power and prognostic value. Functional annotation indicated involvement in cell cycle regulation and metabolic pathways. ROC analysis showed high sensitivity and specificity of these genes in predicting HCC prognosis.
CONCLUSIONS: These results suggest that CCNB2, MCM4, and CDC20 may serve as potential biomarkers for predicting HCC prognosis in patients with cirrhosis and provide insights into the molecular mechanisms of HCC progression.
METHODS: Three data sets (GSE17548, GSE56140, and GSE87630) were extracted from the Gene Expression Omnibus (GEO) database and normalized using the Limma package in R. Principal component analysis (PCA) and cluster analysis was performed to examine data distribution and identify subtypes. Differential gene expression analysis was performed using the Limma software package. Protein-protein interaction analysis and functional annotation were performed using the STRING database and Cytoscape software. Important signaling pathways and processes were identified using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) Pathway Analysis.
RESULTS: The analysis revealed different subtypes of HCC associated with cirrhosis and identified several key genes, including CCNB2, MCM4, and CDC20, with strong binding power and prognostic value. Functional annotation indicated involvement in cell cycle regulation and metabolic pathways. ROC analysis showed high sensitivity and specificity of these genes in predicting HCC prognosis.
CONCLUSIONS: These results suggest that CCNB2, MCM4, and CDC20 may serve as potential biomarkers for predicting HCC prognosis in patients with cirrhosis and provide insights into the molecular mechanisms of HCC progression.
摘要：
背景：这项研究旨在确定与肝硬化相关的肝细胞癌（HCC）的潜在亚型，并使用基因表达数据集的生物信息学分析研究关键标志物-0。
方法：从基因表达综合（GEO）数据库中提取三个数据集（GSE17548，GSE56140和GSE87630），并使用R中的Limma包进行标准化。进行主成分分析（PCA）和聚类分析以检查数据分布并识别亚型。使用Limma软件包进行差异基因表达分析。使用STRING数据库和Cytoscape软件进行蛋白质-蛋白质相互作用分析和功能注释。使用基因本体论(GO)和京都基因和基因组百科全书(KEGG)途径分析鉴定了重要的信号传导途径和过程。
结果：分析揭示了与肝硬化相关的不同肝癌亚型，并确定了几个关键基因，包括CCNB2、MCM4和CDC20，具有较强的结合力和预后价值。功能注释表明参与细胞周期调节和代谢途径。ROC分析显示这些基因在预测HCC预后方面具有较高的敏感性和特异性。
结论：这些结果表明，CCNB2，MCM4和CDC20可能作为预测肝硬化患者HCC预后的潜在生物标志物，并提供对HCC进展的分子机制的见解。
方法：从基因表达综合（GEO）数据库中提取三个数据集（GSE17548，GSE56140和GSE87630），并使用R中的Limma包进行标准化。进行主成分分析（PCA）和聚类分析以检查数据分布并识别亚型。使用Limma软件包进行差异基因表达分析。使用STRING数据库和Cytoscape软件进行蛋白质-蛋白质相互作用分析和功能注释。使用基因本体论(GO)和京都基因和基因组百科全书(KEGG)途径分析鉴定了重要的信号传导途径和过程。
结果：分析揭示了与肝硬化相关的不同肝癌亚型，并确定了几个关键基因，包括CCNB2、MCM4和CDC20，具有较强的结合力和预后价值。功能注释表明参与细胞周期调节和代谢途径。ROC分析显示这些基因在预测HCC预后方面具有较高的敏感性和特异性。
结论：这些结果表明，CCNB2，MCM4和CDC20可能作为预测肝硬化患者HCC预后的潜在生物标志物，并提供对HCC进展的分子机制的见解。
