Wang Xianmo, Yi Huawei, Tu Jiancheng, Fan Wen, Wu Jiahao, Wang Li, Li Xiang, Yan Jinrong, Huang Huali, Huang Rong
Clinical Laboratory, The First People's Hospital of Jingzhou, The First Affiliated Hospital of Yangtze University, Jingzhou, China.
Clinical Laboratory, The Second Clinical College of Wuhan University, Wuhan, China.
Front Oncol. 2022 Feb 24;12:838845. doi: 10.3389/fonc.2022.838845. eCollection 2022.
Hepatitis B (HBV)-infected hepatocellular carcinoma is one of the most common cancers, and it has high incidence and mortality rates worldwide. The incidence of hepatocellular carcinoma has been increasing in recent years, and existing treatment modalities do not significantly improve prognosis. Therefore, it is important to find a biomarker that can accurately predict prognosis.
This study was analyzed using the The Cancer Genome Atlas (TCGA) database and validated by the International Cancer Genome Consortium (ICGC) database. The STRING database was used to construct a gene co-expression network and visualize its functional clustering using Cytoscape. A prognostic signature model was constructed to observe high and low risk with prognosis, and independent prognostic factors for HBV-infected hepatocellular carcinoma were identified by Cox regression analysis. The independent prognostic factors were then analyzed for expression and survival, and their pathway enrichment was analyzed using gene set enrichment analysis (GSEA).
805 differentially expressed genes (DEGs) were obtained by differential analysis. Protein-protein interaction (PPI) showed that DEGs were mostly clustered in functional modules, such as cellular matrix response, cell differentiation, and tissue development. Prognostic characterization models showed that the high-risk group was associated with poor prognosis, while Cox regression analysis identified ASF1B as the only independent prognostic factor. As verified by expression and prognosis, ASF1B was highly expressed in HBV-infected hepatocellular carcinoma and led to a poor prognosis. GSEA showed that high ASF1B expression was involved in cell cycle-related signaling pathways.
Bioinformatic analysis identified ASF1B as an independent prognostic factor in HBV-infected hepatocellular carcinoma, and its high expression led to a poor prognosis. Furthermore, it may promote hepatocellular carcinoma progression by affecting cell cycle-related signaling pathways.
乙型肝炎(HBV)感染相关的肝细胞癌是最常见的癌症之一,在全球范围内发病率和死亡率都很高。近年来肝细胞癌的发病率一直在上升,而现有的治疗方式并不能显著改善预后。因此,找到一种能够准确预测预后的生物标志物很重要。
本研究使用癌症基因组图谱(TCGA)数据库进行分析,并通过国际癌症基因组联盟(ICGC)数据库进行验证。利用STRING数据库构建基因共表达网络,并使用Cytoscape对其功能聚类进行可视化。构建预后特征模型以观察预后的高风险和低风险,并通过Cox回归分析确定HBV感染相关肝细胞癌的独立预后因素。然后对独立预后因素进行表达和生存分析,并使用基因集富集分析(GSEA)分析其通路富集情况。
通过差异分析获得了805个差异表达基因(DEG)。蛋白质-蛋白质相互作用(PPI)表明,DEG大多聚集在功能模块中,如细胞基质反应、细胞分化和组织发育。预后特征模型显示,高风险组与预后不良相关,而Cox回归分析确定ASF1B为唯一的独立预后因素。经表达和预后验证,ASF1B在HBV感染的肝细胞癌中高表达,并导致预后不良。GSEA显示,ASF1B高表达参与细胞周期相关信号通路。
生物信息学分析确定ASF1B为HBV感染相关肝细胞癌的独立预后因素,其高表达导致预后不良。此外,它可能通过影响细胞周期相关信号通路促进肝细胞癌进展。