Construction of a prognostic model for hepatocellular carcinoma based on pyroptosis-related genes.
10.3760/cma.j.cn501113-20220223-00086
- Author:
He Chen RONG
1
;
Wei Feng ZHAO
1
;
Nan ZHENG
1
;
Zhong Hong GUO
1
;
Yi Wei WANG
1
;
Xiao Ping HUANG
1
Author Information
1. Department of Infectious Diseases, the First Affiliated Hospital of Soochow University, Suzhou 215006, China.
- Publication Type:Journal Article
- Keywords:
Hepatocellular carcinoma;
Immune infiltration;
Least absolute selection and shrinkage operator regression analysis;
Prognostic model;
Pyroptosis related genes
- MeSH:
Humans;
Carcinoma, Hepatocellular/genetics*;
Prognosis;
Pyroptosis;
Liver Neoplasms/genetics*;
Risk Factors
- From:
Chinese Journal of Hepatology
2023;31(5):509-517
- CountryChina
- Language:Chinese
-
Abstract:
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.
