Yang Lei, Yin Weilong, Liu Xuechen, Li Fangcun, Ma Li, Wang Dong, Li Hongxing
Department of Histology and Embryology, Binzhou Medical University, Yantai, Shandong, China.
PeerJ. 2021 Apr 28;9:e11273. doi: 10.7717/peerj.11273. eCollection 2021.
Hepatocellular carcinoma (HCC) is considered to be a malignant tumor with a high incidence and a high mortality. Accurate prognostic models are urgently needed. The present study was aimed at screening the critical genes for prognosis of HCC.
The GSE25097, GSE14520, GSE36376 and GSE76427 datasets were obtained from Gene Expression Omnibus (GEO). We used GEO2R to screen differentially expressed genes (DEGs). A protein-protein interaction network of the DEGs was constructed by Cytoscape in order to find hub genes by module analysis. The Metascape was performed to discover biological functions and pathway enrichment of DEGs. MCODE components were calculated to construct a module complex of DEGs. Then, gene set enrichment analysis (GSEA) was used for gene enrichment analysis. ONCOMINE was employed to assess the mRNA expression levels of key genes in HCC, and the survival analysis was conducted using the array from The Cancer Genome Atlas (TCGA) of HCC. Then, the LASSO Cox regression model was performed to establish and identify the prognostic gene signature. We validated the prognostic value of the gene signature in the TCGA cohort.
We screened out 10 hub genes which were all up-regulated in HCC tissue. They mainly enrich in mitotic cell cycle process. The GSEA results showed that these data sets had good enrichment score and significance in the cell cycle pathway. Each candidate gene may be an indicator of prognostic factors in the development of HCC. However, hub genes expression was weekly associated with overall survival in HCC patients. LASSO Cox regression analysis validated a five-gene signature (including CDC20, CCNB2, NCAPG, ASPM and NUSAP1). These results suggest that five-gene signature model may provide clues for clinical prognostic biomarker of HCC.
肝细胞癌（HCC）被认为是一种发病率和死亡率都很高的恶性肿瘤。迫切需要准确的预后模型。本研究旨在筛选HCC预后的关键基因。
从基因表达综合数据库（GEO）中获取GSE25097、GSE14520、GSE36376和GSE76427数据集。我们使用GEO2R筛选差异表达基因（DEG）。通过Cytoscape构建DEG的蛋白质-蛋白质相互作用网络，以便通过模块分析找到枢纽基因。进行Metascape以发现DEG的生物学功能和通路富集。计算MCODE组件以构建DEG的模块复合体。然后，使用基因集富集分析（GSEA）进行基因富集分析。利用ONCOMINE评估HCC中关键基因的mRNA表达水平，并使用HCC的癌症基因组图谱（TCGA）阵列进行生存分析。然后，进行LASSO Cox回归模型以建立和识别预后基因特征。我们在TCGA队列中验证了基因特征的预后价值。
我们筛选出10个在HCC组织中均上调的枢纽基因。它们主要富集在有丝分裂细胞周期过程中。GSEA结果表明，这些数据集在细胞周期通路中具有良好的富集分数和显著性。每个候选基因可能是HCC发生发展中预后因素的一个指标。然而，枢纽基因表达与HCC患者的总生存期相关性较弱。LASSO Cox回归分析验证了一个五基因特征（包括CDC20、CCNB2、NCAPG、ASPM和NUSAP1）。这些结果表明，五基因特征模型可能为HCC的临床预后生物标志物提供线索。
