Shi Laner, Shang Xin, Nie Kechao, Lin Zhiqin, Zheng Meisi, Wang Miao, Yuan Haoyu, Zhu Zhangzhi
Department of Endocrinology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China.
Department of Endocrinology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
J Clin Pathol. 2021 Aug;74(8):504-512. doi: 10.1136/jclinpath-2020-206979. Epub 2020 Oct 1.
Liver hepatocellular carcinoma (LIHC) is the main manifestation of primary liver cancer, with low survival rate and poor prognosis. Medical decision-making process of LIHC is so complex that new biomarkers for diagnosis and prognosis have yet to be explored, this study aimed to identify the genes involved in the pathophysiology of LIHC and biomarkers that can be used to predict the prognosis of LIHC.
Six Gene Expression Omnibus (GEO) datasets selected from GEO were screened and integrated to find out the differential expression genes (DEGs) obtained from LIHC and normal hepatic tissues. The Gene Ontology and Kyoto Encyclopaedia of Genes and Genomes pathway enrichment analysis of DEGs was implemented by DAVID. The Protein-protein interaction network was performed via STRING. In addition, Cox regression model was used to construct a gene prognostic signature.
We ascertained 10 hub genes, nine of them (CDK1, CDC20, CCNB1, Thymidylate synthetase, Nuclear division cycle80, NUF2, MAD2L1, CCNA2 and BIRC5) as biomarkers of progression in LIHC patients. We also build a six gene prognosis signature (SOCS2, GAS2L3, NLRP5, TAF3, UTP11 and GAGE2A), which can be implemented to predict over survival effectively.
We revealed promising genes that may participate in the pathophysiology of LIHC, and found available biomarkers for LIHC prognosis prediction, which were significant for researchers to further understand the molecular basis of LIHC and direct the synthesis medicine of LIHC.
肝肝细胞癌（LIHC）是原发性肝癌的主要表现形式，生存率低且预后较差。LIHC的医学决策过程非常复杂，尚未探索出新的诊断和预后生物标志物，本研究旨在鉴定参与LIHC病理生理学的基因以及可用于预测LIHC预后的生物标志物。
从基因表达综合数据库（GEO）中筛选并整合六个数据集，以找出从LIHC和正常肝组织中获得的差异表达基因（DEG）。通过DAVID对DEG进行基因本体论和京都基因与基因组百科全书通路富集分析。通过STRING构建蛋白质-蛋白质相互作用网络。此外，使用Cox回归模型构建基因预后特征。
我们确定了10个核心基因，其中9个（细胞周期蛋白依赖性激酶1、细胞分裂周期蛋白20、细胞周期蛋白B1、胸苷酸合成酶、核分裂周期80、核仁蛋白2、有丝分裂后期促进复合物2、细胞周期蛋白A2和杆状病毒IAP重复序列5）作为LIHC患者病情进展的生物标志物。我们还构建了一个六基因预后特征（细胞因子信号转导抑制因子2、生长停滞特异性蛋白2样3、NOD样受体家族蛋白5、TATA盒结合蛋白相关因子3、小核仁RNA U3假尿嘧啶合成酶11和肿瘤睾丸抗原GAGE家族成员2A），可有效用于预测生存期。
我们揭示了可能参与LIHC病理生理学的有前景的基因，并发现了可用于LIHC预后预测的生物标志物，这对于研究人员进一步了解LIHC的分子基础和指导LIHC的合成药物具有重要意义。
