Identification of an autophagy-related gene signature that can improve prognosis of hepatocellular carcinoma patients.

backgroundAutophagy 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).

methodsUnivariate 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.

resultsA 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.

conclusionWe developed a novel prognostic risk model by integrating the molecular signature and clinical parameters of HCC, which can effectively predict the outcomes of HCC patients.