PANoptosis-associated genes exhibit significant potential in the diagnosis of hepatocellular carcinoma.

BACKGROUND

To date, little research has been conducted on whether PANoptosis-related genes can be used to predict the prognosis of hepatocellular carcinoma (HCC), despite their effect on several biological processes in cancer. This study sought to establish a dependable gene signature related to PANoptosis that can identify various HCC subtypes and predict their outcomes.

METHODS

A dataset containing RNA sequencing and clinical information was obtained from The Cancer Genome Atlas (TCGA) database. Important PANoptosis-related HCC genes were selected for the bioinformatic analysis. The HCC tumors were classified by a consistent cluster analysis, and the prognosis was studied in connection with a PANoptosis-related HCC model.

RESULTS

The univariate Cox analysis of TCGA-HCC data identified 4,354 genes linked to patient prognosis. The Venn diagram intersection analysis showed that 95 genes were associated with PANoptosis. Using consensus clustering, TCGA-HCC patients were categorized into two subtypes based on these 95 genes, and a stability analysis and principal component analysis (PCA) confirmed significant subtype differences. The low-risk subtype had significantly better overall survival (OS) than the high-risk subtype. The Gene Ontology (GO) analysis of the genes in the high-risk cluster 1 (C1) group revealed that the upregulated genes were associated with mitosis, chromosome segregation, and cell cycle checkpoints, while the downregulated genes were associated with alcohol and steroid metabolism pathways. The Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis indicated that the upregulated genes were involved in the cell cycle and DNA replication pathways, while the downregulated genes were mainly involved in drug metabolism and chemical carcinogenesis. The least absolute shrinkage and selection operator (LASSO) and Cox regression analyses of the 95 PANoptosis-related genes identified 36 prognostic markers. Patients were then allocated to low- and high-risk groups, and the low-risk group had significantly better OS than the high-risk group. The prognostic accuracy of the model was validated by a receiver operating characteristic (ROC) curve analysis, yielding area under the curve (AUC) values of 0.826, 0.865, and 0.854 for 1-, 3-, and 5-year survival, respectively.

CONCLUSIONS

PANoptosis-related genes are strongly associated with tumor classification in HCC. The PANoptosis-related gene signatures showed robust performance in predicting HCC prognosis, and thus could be used as new approaches for HCC diagnosis and therapy.