Establishment of a risk model based on immunogenic cell death-related genes and its value in predicting the prognosis and tumor microenvironment characteristics of hepatocellular carcinoma
- VernacularTitle:免疫原性细胞死亡基因风险模型的建立及其对肝细胞癌预后和肿瘤微环境特征的预测价值
- Author:
Jun LIU
1
;
Ling WANG
1
;
Yuhuan JIANG
1
;
Jingzhi WANG
2
;
Huiming LI
1
Author Information
- Publication Type:Journal Article
- Keywords: Carcinoma， Hepatocellular; Immunogenic Cell Death; Prognosis; Tumor Microenvironment
- From: Journal of Clinical Hepatology 2024;40(12):2473-2483
- CountryChina
- Language:Chinese
- Abstract: ObjectiveTo identify immunogenic cell death （ICD）-related genes in hepatocellular carcinoma （HCC）， and to establish a scoring model based on these genes for predicting the prognosis and tumor microenvironment characteristics of HCC. MethodsThe Cancer Genome Atlas database was used to obtain HCC datasets， and heatmaps were used to display the expression of 57 ICD-related genes in HCC. A cluster analysis was conducted based on the expression of ICD-related genes， and two ICD subtypes （low and high ICD expression groups） were analyzed in terms of gene ontology enrichment analysis， Kyoto Encyclopedia of Genes and Genomes （KEGG） enrichment analysis， somatic mutation， and immune cell infiltration. The LASSO Cox regression risk model was constructed to evaluate its clinical application value， and a nomogram model was established to predict the 1-， 3-， and 5-year survival rates of patients. In addition， qRT-PCR was used to validate the expression levels of key genes in the model. The independent-samples t test was used for comparison between two groups， and the univariate and multivariate Cox regression analyses were used to determine prognostic factors among clinicopathological features. The Kaplan-Meier survival curve was used for prognostic analysis， and the Spearman rank correlation test was used for correlation analysis. ResultsThe low ICD expression group had a poorer prognosis， while the high ICD expression group had relatively favorable clinical outcomes （P=0.004）. Further analysis showed that the high ICD expression group was associated with an immune-active microenvironment， and the genes were mainly enriched in immune-related pathways such as immunoglobulin receptor binding， hematopoietic cell lineage， and B cell receptor. The results of somatic mutation analysis showed that the high ICD expression group had higher expression levels of CD274， CTLA4， HAVCR2， TIGIT， PDCD1， and PDCD1LG2 （all P<0.05）. A risk prediction model was established using 8 ICD-related genes， i.e.， HSP90AA1， ATG5， BAX， PPIA， HSPA4， TLR2， TREM1， and LY96， and this model showed a good predictive value across different clinical characteristics. The univariate and multivariate Cox regression analyses showed that age and risk score were independent prognostic factors for overall survival in the training set （both P<0.05）. The results of qRT-PCR showed that the relative expression levels of HSPA4 and REM1 in HCC tumor samples were significantly higher than those in adjacent tissue samples （both P<0.001）. For the patients with an increase in ICD risk score， the ICD risk score was negatively correlated with γδT cells （r=-0.29， P<0.05）， plasma cells （r=-0.3， P<0.05）， and CD8+T lymphocytes （r=-0.37， P<0.05） and was positively correlated with memory B cells （r=0.38， P<0.05）， resting dendritic cells （r=0.47， P<0.05）， and M0 macrophages （r=0.49， P<0.05）. ConclusionThis study identifies the ICD-related genes that are associated with the prognosis of HCC， which provides insights into the immune characteristics of different ICD expression profiles. The risk model and the nomogram model established in this study have a significant value for predicting the prognosis of HCC patients and guiding immunotherapy for HCC patients.
