Research Paper Volume 15, Issue 7 pp 2610—2630

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

Huaxiang Wang1,2,3, *, , Chengkai Yang1,2, *, , Dong Li4, *, , Ruling Wang3, , Yanbing Li3, , Lizhi Lv1,2, &, ,

  • 1 Department of Hepatobiliary Surgery, Fuzong Clinical Medical College of Fujian Medical University, Fuzhou, Fujian 350025, China
  • 2 Department of Hepatobiliary Surgery, 900 Hospital of the Joint Logistic Team, Fuzhou, Fujian 350025, China
  • 3 Department of Hepatobiliary and pancreatic Surgery, Taihe Hospital, Affiliated Hospital of Hubei University of Medicine, Shiyan, Hubei 442000, China
  • 4 Department of Anesthesiology, Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan, Hubei 430061, China
* Equal contribution

Received: January 31, 2023       Accepted: March 15, 2023       Published: April 3, 2023
How to Cite

Copyright: © 2023 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Hepatocellular carcinoma (HCC) remains imposing an enormous economic and healthcare burden worldwide. In this present study, we constructed and validated a novel autophagy-related gene signature to predict the recurrence of HCC patients. A total of 29 autophagy-related differentially expressed genes were identified. A five-gene signature (CLN3, HGF, TRIM22, SNRPD1, and SNRPE) was constructed for HCC recurrence prediction. Patients in high-risk groups exhibited a significantly poor prognosis compared with low-risk patients both in the training set (GSE14520 dataset) and the validation set (TCGA and GSE76427 dataset). Multivariate cox regression analysis demonstrated that the 5-gene signature was an independent risk factor for recurrence-free survival (RFS) in HCC patients. The nomograms incorporating 5-gene signature and clinical prognostic risk factors were able to effectively predict RFS. KEGG and GSEA analysis revealed that the high-risk group was enriched with multiple oncology characteristics and invasive-related pathways. Besides, the high-risk group had a higher level of immune cells and higher levels of immune checkpoint-related gene expression in the tumor microenvironment, suggesting that they might be more likely to benefit from immunotherapy. Finally, the immunohistochemistry and cell experiments confirmed the role of SNRPE, the most significant gene in the gene signature. SNRPE was significantly overexpressed in HCC. After SNRPE knockdown, the proliferation, migration and invasion ability of the HepG2 cell line were significantly inhibited. Our study established a novel five-gene signature and nomogram to predict RFS of HCC, which may help in clinical decision-making for individual treatment.


HCC: hepatocellular carcinoma; RFS: recurrence-free survival; OS: overall survival; ARGs: autophagy-related genes; HADb: Human Autophagy Database; ATG7: autophagy-related 7; ROS: reactive oxygen species; TCGA: The Cancer Genome Atlas; DCA: decision curve analysis; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; GSEA: Gene set enrichment analyses; IHC: Immunohistochemistry; LIHC: The liver hepatocellular carcinoma; ROC: Receiver operating characteristic; AFP: Alpha-fetoprotein; C-index: concordance index; FDR: false discovery rate; AUC: Area under the curve; TNM: tumor-node-metastasis; HR: Hazard ratio; CI: Confidence interval.