Chen Kunlun, Zhu Pengfei, Liao Yuan, Yan Lei, Feng Ruo, Zhai Wenlong
Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, 450052, People's Republic of China.
Department of Emergency Surgery, Henan Oilfield General Hospital, Nanyang, Henan, 473000, People's Republic of China.
Onco Targets Ther. 2021 Mar 2;14:1589-1604. doi: 10.2147/OTT.S293610. eCollection 2021.
Hepatocellular carcinoma (HCC) is a major public health burden worldwide owing to high incidence and poor prognosis. Although numerous apoptotic genes were disclosed in HCC, the prognostic value and clinical utility of the genes remained unclear.
The differentially expressed genes (DEGs) were identified in the microarray and RNA sequencing data from public databases. The apoptosis-related differentially expressed genes (AR-DEGs) were selected to construct a Lasso-penalized Cox regression model. The signature including five apoptotic genes was used to calculate risk score. Then, the receiver operating characteristic (ROC) and survival analysis were conducted based on the signature. A nomogram containing the signature and clinical characteristics was plotted to visualized the prognosis prediction. Finally, the enrichment analysis was performed in the Gene Ontology (GO) to investigate the potential mechanism.
Patients with high risk scores were related to worse overall survival than those with low risk. The 3-year and 5-year area under curve (AUC) values of the signature were above 0.7 in databases. And the nomogram presented reliable net benefits for the survival prediction. The nomogram was also tested by probability calibration curves and Decision Curve Analysis (DCA). Furthermore, the five differentially expressed genes were verified again in the HCC clinical specimens with real-time PCR and Western Blot.
Collectively, the present study formed a novel signature based on five apoptotic genes, and this possibly predicted prognosis and strengthened the communication with HCC patients about the likely treatment.
由于肝细胞癌（HCC）发病率高且预后差，它是全球主要的公共卫生负担。尽管在HCC中发现了许多凋亡基因，但这些基因的预后价值和临床实用性仍不清楚。
从公共数据库的微阵列和RNA测序数据中鉴定差异表达基因（DEG）。选择凋亡相关差异表达基因（AR-DEG）构建套索惩罚Cox回归模型。使用包含五个凋亡基因的特征来计算风险评分。然后，基于该特征进行受试者工作特征（ROC）和生存分析。绘制包含该特征和临床特征的列线图以可视化预后预测。最后，在基因本体论（GO）中进行富集分析以研究潜在机制。
高风险评分患者的总生存期比低风险患者差。该特征在数据库中的3年和5年曲线下面积（AUC）值均高于0.7。并且列线图在生存预测方面显示出可靠的净效益。列线图还通过概率校准曲线和决策曲线分析（DCA）进行了测试。此外，通过实时PCR和蛋白质印迹在HCC临床标本中再次验证了这五个差异表达基因。
总体而言，本研究基于五个凋亡基因形成了一种新的特征，这可能预测预后并加强与HCC患者关于可能治疗的沟通。
