Hu Yunzhao, Hao Chenchen, Dong Chengyuan, Tao Ping, Wang Jia, Lu Qun
Department of Laboratory Medicine, Shanghai Traditional Chinese Medicine-Integrated Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
Department of Laboratory Medicine, Changzheng Hospital, Second Military Medical University, Shanghai, China.
Discov Oncol. 2025 Jul 10;16(1):1298. doi: 10.1007/s12672-025-03123-9.
Hepatocellular carcinoma (HCC) continues to be a major factor associated with cancer incidence and mortality. Traditional treatments used for HCC have limited efficacy. Ferroptosis plays a key role in cancer occurrence and development. Therefore, the present work focused on screening ferroptosis-related genes (FRGs) and using these FRGs to establish a prognosis prediction model. Sixty-seven FRGs were screened through a differential analysis of the TCGA-LIHC data, and 10 core genes were identified through univariate and multivariate Cox regression analyses along with LASSO regression. These findings were further validated using the ICGC-LIHC cohort as an independent validation dataset. All included patients were classified into Low or High groups according to their risk score, and the prognostic efficacy was evaluated based on time-dependent ROC curves. The AUC value of the 10 FRGs was 0.991, indicating high predictive ability. A prognostic nomogram was also constructed by incorporating FRGs and patient clinical factors. According to the results of the clinical analysis, High group had unfavorable survival. Tumor microenvironment analysis revealed significant differences in the immune scores and stromal scores between the two groups. Drug sensitivity analyses revealed that the High group presented increased sensitivity to drugs such as sorafenib. Gene landscape and mutation analysis revealed that High group had an increased frequency of TP53 mutation, whereas Low group had an increased frequency of CTNNB1 mutation. In summary, a prognostic prediction signature was established based on the 10 FRGs and evaluated for its potential application value in HCC prognosis for the investigation of the tumor microenvironment, drug sensitivity, and the gene mutation landscape.
肝细胞癌（HCC）仍然是与癌症发病率和死亡率相关的主要因素。用于HCC的传统治疗方法疗效有限。铁死亡在癌症的发生和发展中起关键作用。因此，本研究聚焦于筛选铁死亡相关基因（FRGs），并利用这些基因建立预后预测模型。通过对TCGA-LIHC数据进行差异分析筛选出67个FRGs，并通过单变量和多变量Cox回归分析以及LASSO回归确定了10个核心基因。使用ICGC-LIHC队列作为独立验证数据集对这些发现进行了进一步验证。根据风险评分将所有纳入患者分为低风险组或高风险组，并基于时间依赖性ROC曲线评估预后效果。10个FRGs的AUC值为0.991，表明具有较高的预测能力。还通过纳入FRGs和患者临床因素构建了预后列线图。根据临床分析结果，高风险组的生存情况不佳。肿瘤微环境分析显示两组之间的免疫评分和基质评分存在显著差异。药物敏感性分析显示，高风险组对索拉非尼等药物的敏感性增加。基因图谱和突变分析显示，高风险组TP53突变频率增加，而低风险组CTNNB1突变频率增加。总之，基于10个FRGs建立了预后预测特征，并评估了其在HCC预后中的潜在应用价值，以研究肿瘤微环境、药物敏感性和基因突变图谱。
