A Novel Pyroptosis-related Prognostic Model for Hepatocellular Carcinoma

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.

Identification and Validation of Two Heterogeneous Molecular Subtypes and a Prognosis Predictive Model for Hepatocellular Carcinoma Based on Pyroptosis

“…Besides, we constructed a novel multigene predictive model for HCC patients and validated the model genes' expression differences through several different analyses. Recently, some similar studies about different PRG research in HCC subtype identification [40][41][42][43][44][45][46] and predictive model construction [40][41][42][43][44][45][46][47][48][49] have been published. However, in all the previous subtype identification studies, the cluster analyses were conducted only in one cohort while we also tested in a validation cohort.…”

“…Moreover, the association between the pyroptosis pattern and other features of the subtypes were not elucidated. In the predictive model construction studies, most of them did not perform expression validation for the model genes [41][42][43][44][45][46][47][48]. In a word, we have conducted in-depth analyses for the molecular subtypes and validated the mode genes' expression by various analyses which makes our results more reliable and practical in HCC clinical application as well as its future research.…”

Comprehensive genomic signature of pyroptosis-related genes and relevant characterization in hepatocellular carcinoma

“…We found that PRGS consistently maintained superior performance compared to other published pyroptosis-related signatures ( Figs. 4G and S5 , Table S4 ) ( Fu & Song, 2021 ; Jin et al, 2022 ; Liu et al, 2021 ; Wu et al, 2021b ). These results suggested that PRGS was an extremely reliable prognostic prediction model and outperformed other pyroptosis-related signatures.…”

N7-Methylguanosine Regulatory Genes Profoundly Affect the Prognosis, Progression, and Antitumor Immune Response of Hepatocellular Carcinoma

“…Given that there have been some studies constructing signatures for HCC clinical assessments, we analyzed the similarities and differences between five existing signatures and ours ( 24 – 28 ) ( Table 3 ). It was not difficult to perceive that our novel m7G model exhibited several strengths.…”

“…Second, our risk signature (AUC = 0.733) presented a moderate preponderance in the predictive accuracy compared to the signatures of Wu et al . (AUC = 0.698) ( 28 ), Gao et al . (AUC = 0.614) ( 25 ), and He et al (AUC = 0.705) ( 26 ).…”