Multi-omics analysis combining pyroptosis-related signatures for building a prognosis prediction model in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a major cause of cancer-related death worldwide and has a poor prognosis. Pyroptosis, which is programmed cell necrosis mediated by gasdermin, participates in the progression of tumors. Recently, multiple omics analysis has been applied frequently to provide comprehensive and more precise conclusions. However, multiomics analysis combining pyroptosis-related signatures in HCC and their correlations with prognosis remain unclear. Here, we identified 42 pyroptosis genes that were differentially expressed between HCC and normal hepatocellular tissues. According to these differentially expressed genes (DEGs), all HCC cases could be divided into two heterogeneous subtypes. Then, we evaluated the prognostic value of differential pyroptosis-related genes to construct a multigene model using The Cancer Genome Atlas (TCGA) cohort. A 22-gene model was built and classified HCC patients in the TCGA cohort into low-risk and high-risk groups by the least absolute shrinkage and selection operator (LASSO) Cox regression method. HCC patients belonging to the low-risk group had significantly higher survival possibilities than those belonging to the high-risk group (p<0.001). Furthermore, the related genes and two groups were analyzed with multiple omics in different molecular layers. The pyroptosis-related gene model was validated with HCC patients from the Gene Expression Omnibus (GEO) cohort, and the low-risk group in GEO showed increased overall survival (OS) time (P=0.018). The risk score was an independent factor for predicting the OS of HCC patients. In conclusion, pyroptosis-related genes in HCC are correlated with tumor immunity and could be used to predict the prognosis of HCC patients.