Abstract
Hepatocellular carcinoma (HCC) is the second most lethal malignant tumor because of its significant heterogeneity and complicated molecular pathogenesis. Novel prognostic biomarkers are urgently needed because no effective and reliable prognostic biomarkers currently exist for HCC patients. Increasing evidence has revealed that pyroptosis plays a role in the occurrence and progression of malignant tumors. However, the relationship between pyroptosis-related genes (PRGs) and HCC patient prognosis remains unclear. In this study, 57 PRGs were obtained from previous studies and GeneCards. The gene expression profiles and clinical data of HCC patients were acquired from public data portals. Least absolute shrinkage and selection operator (LASSO) Cox regression analysis was performed to establish a risk model using TCGA data. Additionally, the risk model was further validated in an independent ICGC dataset. Our results showed that 39 PRGs were significantly differentially expressed between tumor and normal liver tissues in the TCGA cohort. Functional analysis confirmed that these PRGs were enriched in pyroptosis-related pathways. According to univariate Cox regression analysis, 14 differentially expressed PRGs were correlated with the prognosis of HCC patients in the TCGA cohort. A risk model integrating two PRGs was constructed to classify the patients into different risk groups. Poor overall survival was observed in the high-risk group of both TCGA (p < 0.001) and ICGC (p < 0.001) patients. Receiver operating characteristic curves demonstrated the accuracy of the model. Furthermore, the risk score was confirmed as an independent prognostic indicator via multivariate Cox regression analysis (TCGA cohort: HR = 3.346, p < 0.001; ICGC cohort: HR = 3.699, p < 0.001). Moreover, the single-sample gene set enrichment analysis revealed different immune statuses between high- and low-risk groups. In conclusion, our new pyroptosis-related risk model has potential application in predicting the prognosis of HCC patients.
Highlights
Worldwide, primary liver cancer is the sixth most prevalent malignancy and the fourth leading cause of cancer-related death (Villanueva, 2019)
The results of Gene ontology (GO) functional analysis revealed that the positive regulation of cytokine production, inflammasome complex, and endopeptidase activity involved in apoptotic processes were the most enriched GO terms in biological process, cellular component, and molecular function categories, respectively (Figure 1B)
Predicting the overall survival (OS) of Hepatocellular carcinoma (HCC) patients is of great importance for clinical decision making
Summary
Primary liver cancer is the sixth most prevalent malignancy and the fourth leading cause of cancer-related death (Villanueva, 2019). Hepatocellular carcinoma (HCC), the most common type of primary liver cancer, usually develops after long-term chronic hepatitis, liver fibrosis, and cirrhosis (Sia et al, 2017). The 5years survival rate of HCC is only 18%, ranking second after pancreatic cancer in terms of lethality (Jemal et al, 2017) (Siegel et al, 2018). The prognostication of HCC is mainly based on clinicopathological staging systems, multiple marker features, such as mutations in the well-known TP53 gene and the expression of cellular proliferation-related genes, have been identified to predict survival (Li et al, 2018) (Schulze et al, 2015) (Totoki et al, 2014). Novel prognostic biomarkers are urgently needed to predict survival and outline individualized treatment plans for HCC patients
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