| Autor: |
Liang Q; Department of Epidemiology and Statistics, School of Public Health, Guangxi Medical University, Nanning, China., Tan C; Department of Epidemiology and Statistics, School of Public Health, Guangxi Medical University, Nanning, China; Department of Epidemiology and Statistics, School of Public Health, Guilin Medical University, Guilin, China., Xiao F; Department of Epidemiology and Biostatistics, University of South Carolina, Columbia, SC, United States., Yin F; Life Sciences Institute, Guangxi Medical University, Nanning, China; Key Laboratory of High-Incidence-Tumor Prevention and Treatment, Guangxi Medical University, Ministry of Education, Nanning, China., Liu M; Department of Epidemiology and Statistics, School of Public Health, Guangxi Medical University, Nanning, China., Lei L; Department of Epidemiology and Statistics, School of Public Health, Guangxi Medical University, Nanning, China., Wu L; Hospital Infection Management Department, Liuzhou Workers' hospital, Liuzhou, China., Yang Y; Department of Epidemiology and Statistics, School of Public Health, Guangxi Medical University, Nanning, China., Tan HJJ; Life Sciences Institute, Guangxi Medical University, Nanning, China., Liu S; Department of Epidemiology and Statistics, School of Public Health, Guangxi Medical University, Nanning, China., Zeng X; Department of Epidemiology and Statistics, School of Public Health, Guangxi Medical University, Nanning, China; Key Laboratory of High-Incidence-Tumor Prevention and Treatment, Guangxi Medical University, Ministry of Education, Nanning, China. |
| Abstrakt: |
Hepatocellular carcinoma (HCC) is a highly malignant tumor. In this study, we sought to identify a novel biomarker for HCC by analyzing transcriptome and clinical data. The R software was used to analyze the differentially expressed genes (DEGs) in the datasets GSE74656 and GSE84598 downloaded from the Gene Expression Omnibus database, followed by a functional annotation. A total of 138 shared DEGs were screened from two datasets. They were mainly enriched in the "Metabolic pathways" pathway (Padj = 8.21E-08) and involved in the carboxylic acid metabolic process (Padj = 0.0004). The top 10 hub genes were found by protein-protein interaction analysis and were upregulated in HCC tissues compared to normal tissues in The Cancer Genome Atlas database. Survival analysis distinguished 8 hub genes CENPE, SPDL1, Hyaluronan-mediated motility receptor, Rac GTPase activating protein 1, Thyroid hormone receptor interactor 13, cytoskeleton-associated protein (CKAP) 2, CKAP5, and Integrin subunit beta 3 binding protein (ITGB3BP) were considered as prognostic hub genes. Multivariate cox regression analysis indicated that all the prognostic hub genes were independent prognostic factors for HCC. Furthermore, the receiver operating characteristic curve revealed that the 8-hub genes model had better prediction performance for overall survival compared to the T stage (p = 0.008) and significantly improved the prediction value of the T stage (p = 0.002). The Human Protein Atlas showed that the protein expression of ITGB3BP was upregulated in HCC, so the expression of ITGB3BP was further verified in our cohort. The results showed that ITGB3BP was upregulated in HCC tissues and was significantly associated with lymph node metastasis. |
