Hepatocellular carcinoma (HCC) is a particularly heterogeneous tumor. It has a very poor prognosis. Pyroptosis has been demonstrated in recent years to be an inflammatory form of programmed cell death. However, the relationship between the expression of pyroptosis related genes (PRGs) and prognosis of HCC is still unclear. The development of a specific PRGs prognostic model is important if we want to improve therapeutic effect of tumor. In this study, we identified 42 PRGs that were differentially expressed between HCC and peripheral normal tissues and exhibited the mutation frequency, classification, the location of copy number variation (CNV) alteration and the CNV variation frequency of PRGs. Two clusters were distinguished by the consensus clustering analysis based on the 42 differentially expressed genes (DEGs). The result show that there were significant differences in clinical features (including T stage, grade, gender, stage) among different clusters. KM curve analysis show that cluster 1 had a better prognosis than cluster 2. The prognostic value of PRGs for survival was evaluated to construct a multigene signature using The Cancer Genome Atlas (TCGA) cohort. By applying the univariate analysis and multivariate analysis method, a 10-gene signature was built and all HCC patients in the TCGA cohort were divided into low-risk group and high-risk group. HCC patients in the high-risk group showed significantly lower survival possibilities than those in the low-risk group (P<0.001). Utilizing the median risk score from the TCGA cohort, HCC patients from Gene Expression Omnibus (GEO) cohort (GSE14520) were divided into two risk subgroups. The result showed that overall survival (OS) time was decreased in the high-risk group (P=0.027). Combined with the clinical characteristics, the risk score was found to be an independent factor for predicting the OS of HCC patients. Then, for evaluating the prognostic prediction value of the model, ROC curve and survival analysis were performed. Finally, we constructed a PRGs clinical characteristics nomogram to furtherly predict HCC patient survival probability. There were significant differences in immune cell infiltration, GSEA enrichment pathway, IC50 of chemotherapeutics, PRGs mutation frequency, GO and KEGG analysis between high-risk group and low-risk group. This work suggests PRGs signature plays a crucial role in HCC. The exploration may assist in identifying novel biomarkers and assist HCC patients in predicting their prognosis, clinical diagnosis, and management.
