Autophagy-Related Gene Pairs Signature for the Prognosis of Hepatocellular Carcinoma

Abstract

Hepatocellular carcinoma (HCC) has been recognized as the third leading cause of cancer-related deaths worldwide. There is increasing evidence that the abnormal expression of autophagy-related genes plays an important role in the occurrence and development of HCC. Therefore, the study of autophagy-related genes can further elucidate the genetic drivers of cancer and provide valuable therapeutic targets for clinical treatment. In this study, we used 232 autophagy-related genes extracted from the Human Autophagy Database (HADb) and Molecular Signatures Database (MSigDB) to construct 1884 autophagy-related gene pairs. On this basis, we developed a prognostic model based on autophagy-related gene pairs using least absolute shrinkage and selection operator (LASSO) Cox regression to evaluate the prognosis of patients after liver cancer resection. We then used 845 liver cancer samples from three different databases to test the reliability of the risk signature through survival analysis, receiver operating characteristic (ROC) curve analysis, univariate and multivariate analysis. To further explore the underlying biological mechanisms, we conducted an enrichment analysis of autophagy-related genes. Finally, we combined the signature with independent prognostic factors to construct a nomogram. Based on the autophagy-related gene pair (ARGP) signature, we can divide patients into high- or low-risk groups. Survival analysis and ROC curve analysis verified the validity of the signature (AUC: 0.786—0.828). Multivariate Cox regression showed that the risk score can be used as an independent predictor of the clinical outcomes of liver cancer patients. Notably, this model has a more accurate predictive effect than most prognostic models for hepatocellular carcinoma. Moreover, our model is a powerful supplement to the HCC staging indicator, and a nomogram comprising both indicators can provide a better prognostic effect. Based on pairs of multiple autophagy-related genes, we proposed a prognostic model for predicting the overall survival rate of HCC patients after surgery, which is a promising prognostic indicator. This study confirms the importance of autophagy in the occurrence and development of HCC, and also provides potential biomarkers for targeted treatments.

A Multi-Omics Analysis of NASH-Related Prognostic Biomarkers Associated with Drug Sensitivity and Immune Infiltration in Hepatocellular Carcinoma

“…In the test set, the high- and low-risk groups exhibited significantly different survival probabilities ( p = 0.008) with an AUC of 0.780, 0.726, and 0.699 in a time-dependent ROC at one, three, and five years ( Figure 4 D–F). In addition, the prognostic model constructed based on our risk score was superior to the single gene ( Figure 4 G–I) and other models that have been published [ 21 , 22 , 23 , 24 ].…”

Developing and Validating an Autophagy Gene-Set-Based Prognostic Signature in Hepatocellular Carcinoma Patients

“…In one study, the researchers constructed a novel ARG-based prognostic model; however, the model developed by Luo et al predicted an AUC value of 0.828 in the 3-year OS of the training cohort. 49 In this study, the 3-year OS prediction rate of the AUC in the training cohort was 0.865. Moreover, the RNA sequencing samples used in the training cohort were obtained from HCC patients admitted to the hospital, which could be verified from the tumor tissue level of modeled patients to a certain extent, which was an advantage they did not have.…”

Bioinformatics analysis and experimental validation of a novel autophagy-related signature relevant to immune infiltration for recurrence prediction after curative hepatectomy

“…The increasing number of research validated that autophagy plays a prominent role in the tumorigenesis and progression of HCC [23][24][25]. Abnormal expression of ARGs has also been confirmed to be related to the overall survival of HCC [26,27], but there has been rare research focus on the role of multiple ARGs in RFS.…”