Background: Early detection of hepatocellular carcinoma (HCC) is significantly effective in clinical management. This study aimed to identify potential HCC biomarkers.
Methods: Analysis of expression profiles in HCC clinical samples downloaded from the cancer genome atlas (TCGA) and the gene expression omnibus (GEO) datasets was performed to identify differentially expressed genes (DEGs) using R packages. The epigenetic differentially expressed genes (epiDEGs) were obtained after intersections of genes between DEGs and epigenetic factors (EFs). The biological functions of epiDEGs were annotated by gene ontology (GO) and kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis. Protein-protein interaction and expression correlation were performed to investigate the interactions among epiDEGs by the STRING online tool and R packages. The epiDEGs associated with overall survival (OS) were identified as patient prognosis using the Cox regression analysis. The levels of gene expression were validated by RT-qPCR and Western blot between HCC cell lines, (HepG2, and Huh-7) and normal cell lines (THLE-2).
Results: Thirty-five epiDEGs were obtained, including 25 upregulated genes and 10 downregulated genes. Functional enrichment and PPI analysis indicated the development of HCC is a complicated process involving various genes and proteins. Survival analysis showed nine epiDEGs associated with the OS of patients and these might be the independent prognostic biomarkers for HCC. The expressions of most epiDEGs were significantly higher in HCC patients with stage II and III compared with stage I. Furthermore, the expression of these epiDEGs between HCC cell lines with normal cell lines was shown to be consistent with the TCGA and GEO datasets except PBK.
Conclusions: Eight hub epiDEGs, including EZH2, CDK1, CENPA, RAD54L, HELLS, HJURP, AURKA, and AURKB, were associated with the overall survival of HCC patients and could be potential biomarkers to predict prognosis.


中文翻译：

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表观遗传相关基因对肝细胞癌差异基因表达及预后的综合分析

背景：早期发现肝细胞癌（HCC）在临床管理中显着有效。本研究旨在确定潜在的 HCC 生物标志物。
方法：使用 R 包对从癌症基因组图谱 (TCGA) 和基因表达综合 (GEO) 数据集下载的 HCC 临床样本中的表达谱进行分析，以识别差异表达基因 (DEG)。表观遗传差异表达基因（epiDEGs）是在DEGs和表观遗传因子（EFs）之间的基因交叉后获得的。通过基因本体论（GO）和京都基因和基因组百科全书（KEGG）富集分析对epiDEGs的生物学功能进行了注释。通过STRING在线工具和R包进行蛋白质-蛋白质相互作用和表达相关性以研究epiDEG之间的相互作用。使用 Cox 回归分析将与总生存期 (OS) 相关的 epiDEG 确定为患者预后。基因表达水平通过 RT-q HCC 细胞系（HepG2 和 Huh-7）和正常细胞系（THLE-2）之间的 PCR 和蛋白质印迹。
结果：共获得35个epiDEG，其中上调基因25个，下调基因10个。功能富集和PPI分析表明HCC的发展是一个涉及多种基因和蛋白质的复杂过程。生存分析显示九个与患者 OS 相关的表观 DEG，这些可能是 HCC 的独立预后生物标志物。与 I 期相比，II 期和 III 期 HCC 患者中大多数 EpiDEG 的表达显着高于 I 期。此外，这些 EpiDEG 在 HCC 细胞系与正常细胞系之间的表达显示与 TCGA 和 GEO 数据集一致，除了 PBK。
结论：包括 EZH2、CDK1、CENPA、RAD54L、HELLS、HJURP、AURKA 和 AURKB 在内的 8 个枢纽 epiDEG 与 HCC 患者的总生存期相关，并且可能是预测预后的潜在生物标志物。