Objective: To study the construction of a prognostic model for hepatocellular carcinoma (HCC) based on pyroptosis-related genes (PRGs). Methods: HCC patient datasets were obtained from the Cancer Genome Atlas (TCGA) database, and a prognostic model was constructed by applying univariate Cox and least absolute shrinkages and selection operator (LASSO) regression analysis. According to the median risk score, HCC patients in the TCGA dataset were divided into high-risk and low-risk groups. Kaplan-Meier survival analysis, receiver operating characteristic (ROC) curves, univariate and multivariate Cox analysis, and nomograms were used to evaluate the predictive ability of the prognostic models. Functional enrichment analysis and immune infiltration analysis were performed on differentially expressed genes between the two groups. Finally, two HCC datasets (GSE76427 and GSE54236) from the Gene Expression Omnibus database were used to externally validate the prognostic value of the model. Univariate and multivariate Cox regression analysis or Wilcoxon tests were performed on the data. Results: A total of 366 HCC patients were included after screening the HCC patient dataset obtained from the TCGA database. A prognostic model related to HCC was established using univariate Cox regression analysis, LASSO regression analysis, and seven genes (CASP8, GPX4, GSDME, NLRC4, NLRP6, NOD2, and SCAF11). 366 cases were evenly divided into high-risk and low-risk groups based on the median risk score. Kaplan-Meier survival analysis showed that there were statistically significant differences in the survival time between patients in the high-risk and low-risk groups in the TCGA, GSE76427, and GSE54236 datasets (median overall survival time was 1 149 d vs. 2 131 d, 4.8 years vs. 6.3 years, and 20 months vs. 28 months, with P = 0.000 8, 0.034 0, and 0.0018, respectively). ROC curves showed good survival predictive value in both the TCGA dataset and two externally validated datasets. The areas under the ROC curves of 1, 2, and 3 years were 0.719, 0.65, and 0.657, respectively. Multivariate Cox regression analysis showed that the risk score of the prognostic model was an independent predictor of overall survival time in HCC patients. The risk model score accurately predicted the survival probability of HCC patients according to the established nomogram. Functional enrichment analysis and immune infiltration analysis showed that the immune status of the high-risk group was significantly decreased. Conclusion: The prognostic model constructed in this study based on seven PRGs accurately predicts the prognosis of HCC patients.
目的： 研究基于细胞焦亡相关基因（PRGs）的肝细胞癌（HCC）预后模型的构建。 方法： 从癌症基因组图谱（TCGA）数据库中获取HCC患者数据集，通过应用单变量Cox和最小绝对值选择与收缩算子（LASSO）回归分析构建预后模型。根据中位风险评分，将TCGA数据集中HCC患者分为高风险组和低风险组。Kaplan-Meier生存分析、受试者操作特征（ROC）曲线、单变量和多变量Cox分析、列线图用于评估预后模型的预测能力。并对两组间差异表达基因进行功能富集分析和免疫浸润分析。最后，应用基因表达综合数据库中2个HCC数据集（GSE76427和GSE54236）对模型的预后价值进行外部验证。对数据进行单变量和多变量Cox回归分析或Wilcoxon检验。 结果： 从TCGA数据库中获取的HCC患者数据集经过筛选后，共纳入366例HCC患者。通过单变量Cox回归分析和LASSO回归分析，建立了一个7个基因（CASP8、GPX4、GSDME、NLRC4、NLRP6、NOD2和SCAF11）相关的HCC预后模型。并根据中位风险评分，可将366例患者平均分为高风险组和低风险组。Kaplan-Meier生存分析显示TCGA数据集、GSE76427和GSE54236数据集中高风险组与低风险组患者的生存时间差异存在统计学意义（中位总生存时间分别为1 149 d与2 131 d、4.8年与6.3年和20个月与28个月，P值分别为0.000 8、0.034 0和0.001 8）。ROC曲线在TCGA数据集及2个外部验证数据集中均显示出良好的生存预测价值。1、2年和3年ROC曲线下面积分别为0.719、0.650和0.657。多变量Cox回归分析表明，预后模型的风险评分是HCC患者总生存时间独立的预测因素。根据模型风险评分建立的列线图可有效地预测HCC患者的生存概率。功能富集分析和免疫浸润分析表明高风险组免疫状态下降明显。 结论： 基于7个PRGs建立的预后模型可有效预测HCC患者的预后。.
Keywords: Hepatocellular carcinoma; Immune infiltration; Least absolute selection and shrinkage operator regression analysis; Prognostic model; Pyroptosis related genes.