Significance of Identifying Key Genes Involved in HBV-Related Hepatocellular Carcinoma for Primary Care Surveillance of Patients with Cirrhosis.
Significance of Identifying Key Genes Involved in HBV-Related Hepatocellular Carcinoma for Primary Care Surveillance of Patients with Cirrhosis.
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DOI：
10.3390/genes13122331
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发表时间：
2022-12-10
期刊：
影响因子：
3.5
通讯作者：
Wang, Bin
中科院分区：
文献类型：
--
作者：
Li, Yaqun;Li, Jianhua;He, Tianye;Song, Yun;Wu, Jian;Wang, Bin
关键词：
肝硬化通常是肝细胞癌（HCC）发展之前的疾病的最后阶段，并且是HCC的危险因素之一。对肝硬化患者进行早期HCC的预防性监测有利于实现HCC的早期预防和诊断，从而改善患者预后并降低死亡率。然而，没有高度敏感的诊断标志物用于肝硬化患者的HCC的临床监测，这显著限制了其在HCC的初级护理中的使用。为了提高疾病诊断的准确性，研究与HCC发病相关的有效和敏感的遗传生物标志物至关重要。在这项研究中，在GSE 121248数据集中鉴定了一组120个显著差异表达基因（DEG）。在DEG之间构建蛋白质-蛋白质相互作用（PPI）网络，并使用Cytoscape从网络中提取hub基因。在TCGA数据库中，验证了枢纽基因的表达水平、相关性分析和预测性能。总共有15个枢纽基因表现出表达增加，它们的正相关性范围为0.80至0.90，表明它们可能参与控制HBV相关肝癌的相同信号通路。GSE 10143、GSE 25097、GSE 54236和GSE 17548数据集用于研究这些枢纽基因在从肝硬化到HCC的进展中的表达模式。利用考克斯回归分析，建立了预测模型。ROC曲线、DCA和校准分析表明该模型具有上级疾病预测准确性。此外，使用蛋白质组学分析，我们研究了这些关键枢纽基因是否与HBV编码的癌基因X蛋白（HBx）相互作用，HBx是HCC中的致癌蛋白。我们构建了稳定表达HBx的LO 2-HBx和Huh-7-HBx细胞系。免疫共沉淀结合质谱（Co-IP/MS）结果表明，CDK 1，RRM 2，ANLN和HMMR特异性相互作用与HBx在两种细胞模型。重要的是，我们研究了参与HBV感染向HCC转化过程的15个潜在关键基因（CCNB 1、CDK 1、BUB 1B、ECT 2、RACGAP 1、ANLN、PBK、TOP 2A、ASPM、RRM 2、NEK 2、PRC 1、SPP 1、HMMR和DTL），其中4个枢纽基因（CDK 1、RRM 2、ANLN和HMMR）可能作为潜在的致癌HBx下游靶分子。本研究结果为肝硬化患者原发性肝癌的基层监测中HBV相关性肝癌的诊断基因检测提供了有价值的研究方向。
Cirrhosis is frequently the final stage of disease preceding the development of hepatocellular carcinoma (HCC) and is one of the risk factors for HCC. Preventive surveillance for early HCC in patients with cirrhosis is advantageous for achieving early HCC prevention and diagnosis, thereby enhancing patient prognosis and reducing mortality. However, there is no highly sensitive diagnostic marker for the clinical surveillance of HCC in patients with cirrhosis, which significantly restricts its use in primary care for HCC. To increase the accuracy of illness diagnosis, the study of the effective and sensitive genetic biomarkers involved in HCC incidence is crucial. In this study, a set of 120 significantly differentially expressed genes (DEGs) was identified in the GSE121248 dataset. A protein–protein interaction (PPI) network was constructed among the DEGs, and Cytoscape was used to extract hub genes from the network. In TCGA database, the expression levels, correlation analysis, and predictive performance of hub genes were validated. In total, 15 hub genes showed increased expression, and their positive correlation ranged from 0.80 to 0.90, suggesting they may be involved in the same signaling pathway governing HBV-related HCC. The GSE10143, GSE25097, GSE54236, and GSE17548 datasets were used to investigate the expression pattern of these hub genes in the progression from cirrhosis to HCC. Using Cox regression analysis, a prediction model was then developed. The ROC curves, DCA, and calibration analysis demonstrated the superior disease prediction accuracy of this model. In addition, using proteomic analysis, we investigated whether these key hub genes interact with the HBV-encoded oncogene X protein (HBx), the oncogenic protein in HCC. We constructed stable HBx-expressing LO2-HBx and Huh-7-HBx cell lines. Co-immunoprecipitation coupled with mass spectrometry (Co-IP/MS) results demonstrated that CDK1, RRM2, ANLN, and HMMR interacted specifically with HBx in both cell models. Importantly, we investigated 15 potential key genes (CCNB1, CDK1, BUB1B, ECT2, RACGAP1, ANLN, PBK, TOP2A, ASPM, RRM2, NEK2, PRC1, SPP1, HMMR, and DTL) participating in the transformation process of HBV infection to HCC, of which 4 hub genes (CDK1, RRM2, ANLN, and HMMR) probably serve as potential oncogenic HBx downstream target molecules. All these findings of our study provided valuable research direction for the diagnostic gene detection of HBV-related HCC in primary care surveillance for HCC in patients with cirrhosis.
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DOI：
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