Construction of a novel disulfidptosis-related prognostic model for patients with hepatocellular carcinoma based on bioinformatics analysis
- VernacularTitle:基于生物信息学分析构建肝细胞癌患者新型双硫死亡相关预后模型
- Author:
Zheng SONG
1
;
Wei LUO
2
;
Xiujuan CHANG
3
;
Yongping YANG
1
Author Information
- Publication Type:Journal Article
- Keywords: Carcinoma， Hepatocellular; Disulfides; Computational Biology; Proportional Hazards Models
- From: Journal of Clinical Hepatology 2024;40(9):1822-1832
- CountryChina
- Language:Chinese
- Abstract: ObjectiveTo investigate the expression of disulfidptosis-related genes in hepatocellular carcinoma （HCC） and the prognostic value of disulfidptosis in HCC， to construct a prognostic model， and to analyze its impact on the biological processes of HCC and sorafenib resistance. MethodsThe TCGA-LIHC database was used to collect the mRNA expression profiles and corresponding clinical data of HCC patients， and the LASSO-Cox regression algorithm was used to construct a four-gene predictive model for prognosis in the TCGA cohort. The external datasets ICGC and GSE14520 were used to validate the prognostic efficacy of the model， and the Cancer Drug Sensitivity Genomics （GDSC） data were used to investigate the value of the disulfidptosis model in predicting sorafenib treatment response， and gene ontology （GO） and Kyoto Encyclopedia of Genes and Genomes （KEGG） analyses were used to investigate the biological functions of disulfidptosis-related genes. The independent-samples t test was used for comparison of continuous data between two groups， and the chi-square test was used for comparison of categorical data between two groups. The Kaplan-Meier curve and the log-rank test were used to evaluate the difference in prognosis， and univariate and multivariate Cox regression analyses were used to investigate whether risk score was an independent influencing factor for patient prognosis. ResultsThe univariate Cox regression analysis in the TCGA cohort showed that seven known disulfidptosis-related genes were significantly associated with overall survival （OS） in HCC （all P<0.05）. The LASSO-Cox regression analysis was used to construct a prognostic model based on disulfidptosis-related genes （DRG）， and the risk score RS-DRG was calculated as RS-DRG=（0.061 6）×GYS1 expression level+（0.152 8）×LRPPRC expression level+（0.268 3）×RPN1 expression level+（0.183 5）×SLC7A11 expression level. The log-rank test showed that the patients with a high risk score based on the disulfidptosis model had a significantly lower OS than those with a low risk score （P<0.001）. Based on the results of the multivariate Cox regression analysis， risk score was an independent predictive factor for OS in both TCGA and ICGC cohorts （TCGA： hazard ratio ［HR］=1.869， P=0.002； ICGC： HR=3.469， P=0.004）. The Spearman correlation analysis showed that RS-DRG was significantly positively correlated with the infiltration level of various immune cells （including B lymphocytes， CD4+ T lymphocytes， neutrophils， macrophages， and dendritic cells） in tumor microenvironment （all P<0.05）. The patients in the high-risk score group had a significantly lower IC50 value of sorafenib and were more sensitive to sorafenib （P<0.001）. The KEGG/GO enrichment analysis showed that the differentially expressed disulfidptosis-related genes were significantly enriched in various mitosis-related molecular functions. ConclusionThis study constructed a novel prognostic model based on disulfidptosis-related genes， which has a potential clinical value in predicting the prognosis of HCC， and targeting disulfidptosis-related genes may provide a promising approach for HCC treatment.
