Identification and verification of a prognostic autophagy-related gene signature in hepatocellular carcinoma

Abstract

AbstractThis study aimed to investigate the potential of autophagy-related genes (ATGs) as a prognostic signature for HCC and explore their relationships with immune cells and immune checkpoint molecules. A total of 483 samples were collected from the GEO database (n = 115) and The Cancer Genome Atlas (TCGA) database (n = 368). The GEO dataset was used as the training set, while the TCGA dataset was used for validation. The list of ATGs was obtained from the human autophagy database (HADB). Using Cox regression and LASSO regression methods, a prognostic signature based on ATGs was established. The independent use of this prognostic signature was tested through subgroup analysis. Additionally, the predictive value of this signature for immune-related profiles was explored. Following selection through univariate Cox regression analysis and iterative LASSO Cox analysis, a total of 11 ATGs were used in the GEO dataset to establish a prognostic signature that stratified patients into high- and low-risk groups based on survival. The robustness of this prognostic signature was validated using an external dataset. This signature remained a prognostic factor even in subgroups with different clinical features. Analysis of immune profiles revealed that patients in the high-risk group exhibited immunosuppressive states characterized by lower immune scores and ESTIMATE scores, greater tumour purity, and increased expression of immune checkpoint molecules. Furthermore, this signature was found to be correlated with the infiltration of different immune cell subpopulations. The results suggest that the ATG-based signature can be utilized to evaluate the prognosis of HCC patients and predict the immune status within the tumour microenvironment (TME). However, it is important to note that this study represents a preliminary attempt to use ATGs as prognostic indicators for HCC, and further validation is necessary to determine the predictive power of this signature.