Department of Liver Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
J Cell Mol Med. 2018 Dec;22(12):5928-5938. doi: 10.1111/jcmm.13863. Epub 2018 Sep 24.
With the development of new advances in hepatocellular carcinoma (HCC) management and noninvasive radiological techniques, high-risk patient groups such as those with hepatitis virus are closely monitored. HCC is increasingly diagnosed early, and treatment may be successful. In spite of this progress, most patients who undergo a hepatectomy will eventually relapse, and the outcomes of HCC patients remain unsatisfactory. In our study, we aimed to identify potential gene biomarkers based on RNA sequencing data to predict and improve HCC patient survival. The gene expression data and clinical information were acquired from The Cancer Genome Atlas (TCGA) database. A total of 339 differentially expressed genes (DEGs) were obtained between the HCC (n = 374) and normal tissues (n = 50). Four genes (CENPA, SPP1, MAGEB6 and HOXD9) were screened by univariate, Lasso and multivariate Cox regression analyses to develop the prognostic model. Further analysis revealed the independent prognostic capacity of the prognostic model in relation to other clinical characteristics. The receiver operating characteristic (ROC) curve analysis confirmed the good performance of the prognostic model. Then, the prognostic model and the expression levels of the four genes were validated using the Gene Expression Omnibus (GEO) dataset. A nomogram comprising the prognostic model to predict the overall survival was established, and internal validation in the TCGA cohort was performed. The predictive model and the nomogram will enable patients with HCC to be more accurately managed in trials testing new drugs and in clinical practice.
随着肝细胞癌 (HCC) 管理和非侵入性放射学技术的新进展，高危患者群体（如乙型肝炎病毒感染者）得到了密切监测。HCC 的诊断越来越早，治疗可能会成功。尽管取得了这些进展，但大多数接受肝切除术的患者最终仍会复发，HCC 患者的预后仍不令人满意。在我们的研究中，我们旨在基于 RNA 测序数据识别潜在的基因生物标志物，以预测和改善 HCC 患者的生存。基因表达数据和临床信息从癌症基因组图谱 (TCGA) 数据库中获取。在 HCC（n=374）和正常组织（n=50）之间获得了 339 个差异表达基因 (DEG)。通过单变量、Lasso 和多变量 Cox 回归分析筛选出 4 个基因（CENPA、SPP1、MAGEB6 和 HOXD9），以建立预后模型。进一步分析显示，该预后模型与其他临床特征相关，具有独立的预后能力。接受者操作特征 (ROC) 曲线分析证实了预后模型的良好性能。然后，使用基因表达综合数据库 (GEO) 数据集验证了预后模型和 4 个基因的表达水平。建立了一个包含预后模型的列线图，以预测总生存期，并在 TCGA 队列中进行了内部验证。预测模型和列线图将使 HCC 患者能够在测试新药的临床试验和临床实践中得到更准确的管理。
