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A Novel Risk Score Model Based on Pyroptosis-Related Genes for Predicting Survival and Immunogenic Landscape in Hepatocellular Carcinoma

AGING-US(2023)

Hebei Normal Univ

Cited 3|Views20
Abstract
Background: Hepatocellular carcinoma (HCC) is the third leading cause of cancer worldwide, with high incidence and mortality. Pyroptosis, a form of inflammatory-regulated cell death, is closely associated with oncogenesis. Methods: Expression profiles of HCC were downloaded from the TCGA database and validated using the ICGC and GEO databases. Consensus clustering analysis was used to determine distinct clusters. The pyroptosis-related genes (PRGs) included in the pyroptosis-related signature were selected by univariate Cox regression and LASSO regression analysis. Kaplan-Meier and receiver operating characteristic (ROC) analyses were performed to estimate the prognostic potential of the model. The characteristics of infiltration of immune cells between different groups of HCC were explored. Results: Two independent clusters were identified according to PRG expression. Cluster 2 showed upregulated expression, poor prognosis, increased immune cell infiltration and worse immunotherapy response than cluster 1. A prognostic risk signature consisting of five genes (GSDME, NOD1, PLCG1, NLRP6 and NLRC4) was identified. In the high-risk score group, HCC patients showed decreased survival rates. In particular, multiple clinicopathological characteristics and immune cell infiltration were significantly associated with the risk score. Notably, the 5 PRGs in the risk score have been implicated in carcinogenesis, immunological pathways and drug sensitivity. Conclusions: A prognostic signature comprising five PRGs can be used as a potential prognostic factor for HCC. The PRG-related signature provides an in-depth understanding of the association between pyroptosis and chemotherapy or immunotherapy for HCC patients.
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hepatocellular carcinoma,pyroptosis,molecular subtypes,immunotherapy,drug sensitivity
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要点】:本研究构建了一种基于细胞焦亡相关基因的新型预后评分模型,可用于预测肝细胞癌患者的生存率和免疫景观,发现了与细胞焦亡相关的基因在肝细胞癌发生、发展和治疗中的关键作用。

方法】:通过TCGA数据库下载肝细胞癌的表达谱,并使用ICGC和GEO数据库进行验证,利用共识聚类分析确定不同聚类,通过单变量Cox回归和套索回归分析筛选出细胞焦亡相关基因,并采用Kaplan-Meier和ROC分析评估模型的预后潜力,探索不同肝细胞癌群体间免疫细胞浸润的特征。

实验】:在TCGA、ICGC和GEO数据库中进行分析,确定了两个独立的聚类,发现了一个包含五个基因(GSDME、NOD1、PLCG1、NLRP6和NLRC4)的预后风险签名,高风险评分组的肝细胞癌患者显示出较低的生存率,并且多个临床病理特征和免疫细胞浸润与风险评分显著相关。