Comprehensive Analysis Identified ASF1B as an Independent Prognostic Factor for HBV-Infected Hepatocellular Carcinoma

Xianmo Wang; Huawei Yi; Jiancheng Tu; Wen Fan; Jiahao Wu; Li Wang; Xiang Li; Jinrong Yan; Huali Huang; Rong Huang

doi:10.3389/fonc.2022.838845

Comprehensive Analysis Identified ASF1B as an Independent Prognostic Factor for HBV-Infected Hepatocellular Carcinoma

Front Oncol. 2022 Feb 24:12:838845. doi: 10.3389/fonc.2022.838845. eCollection 2022.

Authors

Xianmo Wang¹, Huawei Yi¹, Jiancheng Tu², Wen Fan¹, Jiahao Wu¹, Li Wang¹, Xiang Li¹, Jinrong Yan¹, Huali Huang^{3

4}, Rong Huang¹

Affiliations

¹ Clinical Laboratory, The First People's Hospital of Jingzhou, The First Affiliated Hospital of Yangtze University, Jingzhou, China.
² Clinical Laboratory, The Second Clinical College of Wuhan University, Wuhan, China.
³ Department of Neurology, The Fifth Affiliated Hospital of Guangxi Medical University, Nanning, China.
⁴ Department of Neurology, The First People's Hospital of Nanning, Nanning, China.

Abstract

Purpose: 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.

Methods: 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).

Results: 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.

Conclusion: 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.

Keywords: ASF1B; ASF1B overexpression; HBV; lung cancer; prognosis.