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

Yiming Luo; Furong Liu; Shenqi Han; Yongqiang Qi; Xinsheng Hu; Chenyang Zhou; Huifang Liang; Zhiwei Zhang

doi:10.3389/fmolb.2021.670241

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

Front Mol Biosci. 2021 May 20:8:670241. doi: 10.3389/fmolb.2021.670241. eCollection 2021.

Authors

Yiming Luo^{1

2}, Furong Liu^{1

2}, Shenqi Han^{1

2}, Yongqiang Qi^{1

2}, Xinsheng Hu^{1

2}, Chenyang Zhou^{1

2}, Huifang Liang^{1

2}, Zhiwei Zhang^{1

2

3}

Affiliations

¹ Hepatic Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
² Hubei Key Laboratory of Hepato-Pancreato-Biliary Diseases, Wuhan, China.
³ Key Laboratory of Organ Transplantation, Ministry of Education, NHC Key Laboratory of Organ Transplantation, Key Laboratory of Organ Transplantation, Chinese Academy of Medical Sciences, Wuhan, China.

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.

Keywords: autophagy-related gene; gene pairs; hepatocellular carcinoma; nomogram; prognosis signature.