Liver hepatocellular carcinoma (LIHC) is one of the most common malignant tumors, which is difficult to be diagnosed at an early stage due to its poor prognosis. Despite the fact that PANoptosis is important in the occurrence and development of tumors, no bioinformatic explanation related to PANoptosis in LIHC can be found. A bioinformatics analysis on the data of LIHC patients in TCGA database was carried out on the basis of previously identified PANoptosis-related genes (PRGs). LIHC patients were divided into two PRG clusters whose gene characteristics of differentially expressed genes (DEGs) were discussed. According to DEGs, the patients were further divided into two DEG clusters, and prognostic-related DEGs (PRDEGs) were applied to risk score calculation, the latter of which turned out to be practical in identifying the relationship among risk score, patient prognosis, and immune landscape. The results suggested that PRGs and relevant clusters were bound up with the survival and immunity of patients. Moreover, the prognostic value based on two PRDEGs was evaluated, the risk scoring model was constructed, and the nomogram model for predicting the survival rate of patients was further developed. Therefore, it was found that the prognosis of the high-risk subgroup was poor. Additionally, three factors, namely, the abundance of immune cells, the expression of immune checkpoints, and immunotherapy and chemotherapy were considered to be associated with the risk score. RT-qPCR results indicated higher positive expression of CD8A and CXCL6 in both LIHC tissues and most human liver cancer cell lines. In summary, the results suggested that PANoptosis was bound up with LIHC-related survival and immunity. Two PRDEGs were identified as potential markers. Thus, the understanding of PANoptosis in LIHC was enriched, with some strategies provided for the clinical therapy of LIHC.