Development and validation of a survival model based on autophagy-associated genes for predicting prognosis of hepatocellular carcinoma

Abstract

Background: Hepatocellular carcinoma (HCC) is one of the devastating tumors with increasing incidence. Autophagy-associated genes (ARGs) are widely participated in the cellular processes of HCC. This study proposed to identify the novel prognostic gene signature based on ARGs in HCC. Methods: We downloaded the RNA sequencing data and clinical information of HCC and normal tissues from The Cancer Genome Atlas (TCGA) database. The differentially expressed ARGs were screened by the Wilcoxon signed-rank test. Functional enrichment analyses were conducted to explore the biological implications and mechanisms of ARGs in HCC. Cox regression analysis and Lasso regression analysis were performed to screen the ARGs which related to overall survival (OS). The OS-related ARGs were then used to establish a prognostic prediction model. Kaplan-Meier curves and receiver operating characteristic (ROC) curves were both applied to evaluate the accuracy of the model. GSE14520 dataset was downloaded as the testing cohort to validate the prognostic risk model in TCGA. A nomogram based on the clinical features and risk signature was established to predict the 3-year and 5-year survival rate of HCC patients. Results: Totally 27 differentially expressed ARGs were screened in this study. Then, 3 OS-related ARGs (SQSTM1, HSPB8, and BIRC5) were identified via the Cox regression and Lasso regression analyses. Based on these 3 ARGs, a prognostic prediction model was constructed. HCC patients in high-risk group presented poorer prognosis than those with low risk score in TCGA cohort (3-year OS, 53.7% vs 70.2%; 5-year OS, 42.0 % vs 55.2%; P=4.478e-04) and in the testing group (3-year OS, 57.7% vs 73.5%; 5-year OS, 43.2% vs 63.0%; P=1.274e-03). The risk score curve showed a well feasibility in predicting the patients’ survival both in TCGA and GEO cohort with the area under the ROC curve (AUC) of 0.756 and 0.672, respectively. Besides, the calibration curves and C-index indicated that the clinical nomogram performs well to predict the 3-year and 5-year survival rate in HCC patients. Conclusions: The survival model based on the ARGs may be a promising tool to predict the prognosis in HCC patients.

High Expression of p62 and ALDH1A3 Is Associated With Poor Prognosis in Luminal B Breast Cancer

“…p62 (also known as sequestosome 1) is a multifunctional adapter protein that is involved in various physiological functions (29), including nuclear factor-ĸB signaling (30)(31)(32)(33), antioxidant response (34) and autophagy (35)(36)(37). p62 is overexpressed (38)(39)(40)(41)(42)(43)(44)(45)(46)(47)(48) in several cancer types, and high p62 expression is associated with poor prognosis (41,44,46,(49)(50)(51)(52) in several types of cancer. In breast cancer, p62 gene and protein are overexpressed (53)(54)(55) and high p62 protein expression is associated with poor prognosis of overall and disease-free survival (54,56).…”

Identification and Validation of a Tumor Microenvironment-Related Gene Signature in Hepatocellular Carcinoma Prognosis

“…BIRC5 expression could reflect aggressive histological and clinical behavior of HCC, correlating with poorer OS (Kapiris et al, 2019). Present studies have shown that BIRC5 is closely related with autophagy, and several survival indexes containing BIRC5 were proved to have promising predictive value (Yang et al, 2020;Qin et al, 2021;Wang et al, 2021). Jiao et al once reported that YAP promoted sorafenib resistance in HCC by upregulating BIRC5 (Sun et al, 2021).…”

Construction of a Prognostic Model for Hepatocellular Carcinoma Based on Immunoautophagy-Related Genes and Tumor Microenvironment

“… 12 In the meanwhile, several studies also constructed prognostic signatures for HCC overall survival prediction based on autophagy-associated genes. 13 , 14 These prognostic models could serve as predictor for survival and play key roles in tumor microenvironment. In order to identify potential biomarkers, we used the TCGA database to analyze differences in immunoautophagy-related gene expression between HCC and normal tissues in our study.…”

“…10 Thus, the combination of autophagy and immunotherapy may provide us with a promising strategy for tumor treatment, requires further study. However, few previous studies have established some prognosis model of HCC based on immune-related genes 11,12 or autophagy-related genes, 13,14 but no studies have explored the relationship between immunoautophagy-related genes and investigate its prognosis of HCC. With the currently available autophagy-related genes (ARGs) and immune-related genes (IRGs), and the accumulative data deposited in public databases, it is hypothesized that a prognostic signature based on the immune response and autophagy might help to identify HCC patients' survival profile.…”