Department of Oncology, The Second Affiliated Hospital of Soochow University, San Xiang Road No.1055, Suzhou, Jiangsu Province, 215004, People's Republic of China.
BMC Genomics. 2023 Jun 27;24(1):357. doi: 10.1186/s12864-023-09367-5.
Hepatocellular carcinoma (HCC), which has a complex pathogenesis and poor prognosis, is one of the most common malignancies worldwide. Hepatitis virus B infection is the most common cause of HCC in Asian patients. Autophagy is the process of digestion and degradation, and studies have shown that autophagy-associated effects are closely related to the development of HCC. In this study, we aimed to construct a prognostic model based on autophagy-related genes (ARGs) for the Asian HCC population to provide new ideas for the clinical management of HCC in the Asian population.
The clinical information and transcriptome data of Asian patients with HCC were downloaded from The Cancer Genome Atlas (TCGA) database, and 206 ARGs were downloaded from the human autophagy database (HADB). We performed differential and Cox regression analyses to construct a risk score model. The accuracy of the model was validated by using the Kaplan-Meier (K-M) survival curve, receiver operating characteristic (ROC) curve, and univariate and multivariate Cox independent prognostic analyses. The results Thirteen ARGs that were significantly associated with prognosis were finally identified by univariate and multivariate Cox regression analyses. The K-M survival curves showed that the survival rate of the low-risk group was significantly higher than that of the high-risk group (p < 0.001), and the multi-indicator ROC curves further demonstrated the predictive ability of the model (AUC = 0.877).
The risk score model based on ARGs was effective in predicting the prognosis of Asian patients with HCC.
肝细胞癌（HCC）发病机制复杂，预后不良，是全球最常见的恶性肿瘤之一。乙型肝炎病毒感染是亚洲患者 HCC 的最常见病因。自噬是消化和降解的过程，研究表明自噬相关效应与 HCC 的发生发展密切相关。本研究旨在构建基于自噬相关基因（ARGs）的亚洲 HCC 人群预后模型，为亚洲人群 HCC 的临床管理提供新思路。
从癌症基因组图谱（TCGA）数据库中下载亚洲 HCC 患者的临床信息和转录组数据，并从人类自噬数据库（HADB）下载 206 个 ARGs。我们进行差异和 Cox 回归分析构建风险评分模型。通过 Kaplan-Meier（K-M）生存曲线、受试者工作特征（ROC）曲线、单因素和多因素 Cox 独立预后分析验证模型的准确性。
通过单因素和多因素 Cox 回归分析，最终确定了 13 个与预后显著相关的 ARGs。K-M 生存曲线显示，低风险组的生存率明显高于高风险组（p<0.001），多指标 ROC 曲线进一步证明了该模型的预测能力（AUC=0.877）。
基于 ARGs 的风险评分模型可有效预测亚洲 HCC 患者的预后。
