Wen Jiahua, Wen Kai, Tao Meng, Zhou Zhenyu, He Xing, Wang Weidong, Huang Zian, Lin Qiaohong, Li Huoming, Liu Haohan, Yan Yongcong, Xiao Zhiyu
Department of Hepatobiliary Surgery, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, 510120, China.
Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, China.
Cancer Cell Int. 2025 Mar 18;25(1):101. doi: 10.1186/s12935-025-03743-9.
The development of immunotherapy has enriched the treatment of hepatocellular carcinoma (HCC), but the efficacy is not as expected, which may be due to immune evasion. Immune evasion is related to the immune microenvironment of HCC, but there is little research on it.
We employed unsupervised clustering analysis to categorize patients from TCGA based on 182 immune evasion-related genes (IEGs). We utilized single-sample gene set enrichment analysis (ssGSEA) and CIBERSORT to calculate differences in immune cell infiltration between clusters. The differences in immune cells and immune-related pathways were assessed using GSEA. We constructed an immune escape prognosis signature (IEPS) using univariate Cox and LASSO Cox algorithms and evaluated the predictive performance of IEPS with receiver operating characteristic (ROC) curves and survival curves. Additionally, we established a nomogram for clinical application based on IEPS. IHC validated the expression of Carbamoyl phosphate synthetase 2, Aspartate transcarbamylase, and Dihydroorotase (CAD) and Phosphatidylinositol Glycan Anchor Biosynthesis Class U (PIGU) in HCC. We transfected liver cancer cell lines with siRNA and overexpression plasmids, and confirmed the relationship between CAD, PIGU, and the potential downstream TGF-β1 in HCC using qRT-PCR and Western blot. Finally, we validated the tumor response of CAD overexpression using an animal model.
Unsupervised clustering analysis based on IEGs divided HCC patients from TCGA into two groups. There were significant differences in prognosis and immune characteristics between the two groups of patients. Scoring of TCGA patients using IEPS revealed that higher scores were associated with poorer overall survival (OS). Validation was performed using the ICGC database. TIME analysis indicated that patients in the high-IEPS group were in an immunosuppressive state, possibly due to a significant increase in Treg infiltration. Compared to normal liver cells, HCC cells expressed higher levels of CAD and PIGU. Cellular experimental results showed a positive correlation between CAD, PIGU and the potential downstream TGF-β1 expression. Animal experiments demonstrated that CAD significantly promoted tumor progression, with an increase in Treg infiltration.
IEPS has strong prognostic value for HCC patients, and CAD and PIGU provide perspectives on new biomarkers and therapeutic targets for HCC.
免疫疗法的发展丰富了肝细胞癌（HCC）的治疗手段，但疗效未达预期，这可能与免疫逃逸有关。免疫逃逸与HCC的免疫微环境相关，但对此研究较少。
我们采用无监督聚类分析，基于182个免疫逃逸相关基因（IEGs）对来自TCGA的患者进行分类。我们利用单样本基因集富集分析（ssGSEA）和CIBERSORT计算各聚类间免疫细胞浸润的差异。使用基因集富集分析（GSEA）评估免疫细胞和免疫相关通路的差异。我们使用单变量Cox和LASSO Cox算法构建免疫逃逸预后特征（IEPS），并通过受试者工作特征（ROC）曲线和生存曲线评估IEPS的预测性能。此外，我们基于IEPS建立了用于临床应用的列线图。免疫组化验证了氨甲酰磷酸合成酶2、天冬氨酸转氨甲酰酶和二氢乳清酸酶（CAD）以及磷脂酰肌醇聚糖锚定生物合成U类（PIGU）在HCC中的表达。我们用小干扰RNA（siRNA）和过表达质粒转染肝癌细胞系，并使用定量逆转录聚合酶链反应（qRT-PCR）和蛋白质免疫印迹法（Western blot）证实了CAD、PIGU与HCC中潜在下游转化生长因子-β1（TGF-β1）之间的关系。最后，我们使用动物模型验证了CAD过表达的肿瘤反应。
基于IEGs的无监督聚类分析将来自TCGA的HCC患者分为两组。两组患者在预后和免疫特征方面存在显著差异。使用IEPS对TCGA患者进行评分显示，得分越高，总生存期（OS）越差。使用国际癌症基因组联盟（ICGC）数据库进行了验证。肿瘤免疫微环境（TIME）分析表明，高IEPS组患者处于免疫抑制状态，可能是由于调节性T细胞（Treg）浸润显著增加。与正常肝细胞相比，HCC细胞中CAD和PIGU的表达水平更高。细胞实验结果显示CAD、PIGU与潜在下游TGF-β1表达呈正相关。动物实验表明，CAD显著促进肿瘤进展，Treg浸润增加。
IEPS对HCC患者具有很强的预后价值，CAD和PIGU为HCC的新生物标志物和治疗靶点提供了思路。
