Feng Bi, Yang Siqi, He Zhiqiang, Dai Yushi, Zou Ruiqi, Hu Yafei, Hu Haijie, Li Fuyu
Division of Biliary Tract Surgery, Department of General Surgery, West China Hospital of Sichuan University, Chengdu, 610041, China.
Oncol Res. 2025 Aug 28;33(9):2353-2377. doi: 10.32604/or.2025.061422. eCollection 2025.
Hepatocellular carcinoma (HCC) is among the most frequently occurring malignant tumors of the digestive tract and is associated with an increased mortality rate worldwide. This study aimed to develop and validate a prognostic model based on immunogenic cell death (ICD)-related genes to predict patient survival and guide individualized treatment strategies for HCC.
ICD-related genes were identified from the GeneCards database using a relevance score threshold of >10. A combination of least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox analysis was used to screen prognostic genes and construct a risk score model. Immune cell infiltration was evaluated through single-sample gene set enrichment analysis (ssGSEA) and cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) algorithms. Associations between risk groups and the tumor microenvironment (TME), N6-methyladenosine (m6A) regulators, and immune checkpoint expression were analyzed. Drug sensitivity was predicted based on the risk stratification. The reliability of the model was validated in internal cohorts and further confirmed by quantitative reverse transcription polymerase chain reaction (qRT-PCR) and immunohistochemistry (IHC).
A six-gene signature (CFHR3, G6PD, IGHM, KPNA2, PON1, and SERPINE1) was identified and used to calculate the risk scores. This study found that high-risk patients exhibited significantly poorer overall survival in both the training and validation datasets. The nomogram integrating the risk score and clinical factors showed strong predictive performance. High-risk patients demonstrated reduced immune cell infiltration, altered expression of immune checkpoints and immunosuppressive factors, and a distinct m6A modification pattern, suggesting a higher likelihood of immune escape. This study also revealed that the risk model effectively predicted sensitivity to multiple anticancer drugs.
This study developed a robust ICD-related six-gene prognostic model for HCC that can accurately stratify patient risk, reflect the tumor immune landscape, and provide guidance for immunotherapy and personalized treatment strategies.
肝细胞癌(HCC)是消化道最常见的恶性肿瘤之一,在全球范围内死亡率呈上升趋势。本研究旨在开发并验证一种基于免疫原性细胞死亡(ICD)相关基因的预后模型,以预测HCC患者的生存情况并指导个体化治疗策略。
使用相关性得分阈值>10从GeneCards数据库中鉴定ICD相关基因。采用最小绝对收缩和选择算子(LASSO)回归与多变量Cox分析相结合的方法筛选预后基因并构建风险评分模型。通过单样本基因集富集分析(ssGSEA)和基于RNA转录本相对子集估计的细胞类型鉴定(CIBERSORT)算法评估免疫细胞浸润情况。分析风险组与肿瘤微环境(TME)、N6-甲基腺苷(m6A)调节剂以及免疫检查点表达之间的关联。基于风险分层预测药物敏感性。该模型的可靠性在内部队列中得到验证,并通过定量逆转录聚合酶链反应(qRT-PCR)和免疫组织化学(IHC)进一步确认。
鉴定出一个六基因特征(CFHR3、G6PD、IGHM、KPNA2、PON1和SERPINE1)并用于计算风险评分。本研究发现,在训练集和验证集中,高危患者的总生存期均显著较差。整合风险评分和临床因素的列线图显示出强大的数据预测性能。高危患者表现出免疫细胞浸润减少、免疫检查点和免疫抑制因子表达改变以及独特的m6A修饰模式,提示免疫逃逸的可能性更高。本研究还表明,该风险模型能够有效预测对多种抗癌药物的敏感性。
本研究开发了一种强大的HCC ICD相关六基因预后模型,该模型可以准确地对患者风险进行分层,反映肿瘤免疫格局,并为免疫治疗和个体化治疗策略提供指导。