摘要

  背景

  自噬是一种程序化的细胞降解机制，与多种生理和病理生理过程有关，包括恶性肿瘤。已提出自噬的不当诱导在肝细胞癌 (HCC) 的进展中起关键作用。

   方法

   对总生存期 (OS) 进行单变量 Cox 回归分析，以确定来自癌症基因组图谱 (TCGA) 的 HCC 数据集中的风险相关自噬相关基因 (ARG)。然后进行多变量 cox 回归以开发 370 名 HCC 患者预后的风险预测模型。多目标受    试者工作特征（ROC）曲线用于确定模型的准确性。此外，还检查了药物敏感性和 ARGs 表达之间的关系。

  结果 

在 HCC 患者中共鉴定出 62 个差异表达的 ARG。单变量和多变量回归确定了与 HCC 患者的 OS 相关的五个风险相关的 ARG（HDAC1、RHEB、ATIC、SPNS1 和 SQSTM1）。重要的是，风险相关的 ARG 是多变量风险模型中的独立风险因素，包括临床参数，如恶性阶段（HR = 1.433，95% CI = 1.293–1.589，P < 0.001）。此外，预后风险模型的曲线下面积为 0.747，表明该模型在预测 HCC 结局方面的准确性很高。有趣的是，风险相关的 ARG 也与 HCC 细胞系的药物敏感性相关。

Background

Autophagy is a programmed cell degradation mechanism that has been associated with several physiological and pathophysiological processes, including malignancy. Improper induction of autophagy has been proposed to play a pivotal role in the progression of hepatocellular carcinoma (HCC).

Methods
Univariate Cox regression analysis of overall survival (OS) was performed to identify risk-associated autophagy-related genes (ARGs) in HCC data set from The Cancer Genome Atlas (TCGA). Multivariate cox regression was then performed to develop a risk prediction model for the prognosis of 370 HCC patients. The multi-target receiver operating characteristic (ROC) curve was used to determine the model’s accuracy. Besides, the relationship between drug sensitivity and ARGs expression was also examined.

Results
A total of 62 differentially expressed ARGs were identified in HCC patients. Univariate and multivariate regression identified five risk-associated ARGs (HDAC1, RHEB, ATIC, SPNS1 and SQSTM1) that were correlated with OS in HCC patients. Of importance, the risk-associated ARGs were independent risk factors in the multivariate risk model including clinical parameters such as malignant stage (HR = 1.433, 95% CI = 1.293–1.589, P < 0.001). In addition, the area under curve for the prognostic risk model was 0.747, which indicates the high accuracy of the model in prediction of HCC outcomes. Interestingly, the risk-associated ARGs were also correlated with drug sensitivity in HCC cell lines.

   https://link.springer.com/article/10.1186/s12885-020-07277-3