Department of Gastroenterology, The Third Affiliated Hospital of Sun Yat-Sen University, 600 Tianhe Road, Guangzhou, 510630, China.
Guangdong Provincial Key Laboratory of Liver Disease Research, 600 Tianhe Road, Guangzhou, 510630, China.
Hepatol Int. 2022 Feb;16(1):112-124. doi: 10.1007/s12072-021-10248-w. Epub 2021 Aug 27.
Considering the increase in the number of HCC patients, it is critical to predict the survival of patients. Although ferroptosis is closely related to HCC progression, predicting the survival of HCC patients through ferroptosis-related genes is challenging.
RNA-seq and clinical data of HCC in the TCGA database were analyzed to establish a prognostic model, and ICGC and GSE14520 data were used for validation. Risk score was constructed with 5 genes identified by univariate and LASSO Cox regression analysis. Risk score, TNM stage and cirrhosis were incorporated to construct a nomogram through univariate and multivariate Cox regression analysis.
Five genes identified from 70 ferroptosis-related DEGs were used to construct a gene signature that predicts survival of HCC patients in the TCGA cohort. PCA and heatmap showed clear differences between patients in different score groups. Next, risk score, TNM stage and cirrhosis were combined in a nomogram for overall survival prediction. Survival analysis indicated that the overall survival of the low-risk group was significantly higher than that of the high-risk group. The data from the GSE14520 cohort confirmed satisfactory nomogram performance. Furthermore, KEGG and GO functional enrichment analyses indicated that the difference in overall survival between risk groups was closely related to immune-related pathways. Further analyses implied that an immune-suppressive tumor microenvironment might contribute to the difference in the prognosis between risk groups.
The nomogram based on ferroptosis-related genes showed good performance for predicting the prognosis of HCC patients. The model may provide a reference for the evaluation of HCC patients by targeting ferroptosis.
考虑到 HCC 患者数量的增加，预测患者的生存率至关重要。尽管铁死亡与 HCC 的进展密切相关，但通过铁死亡相关基因预测 HCC 患者的生存率具有挑战性。
分析 TCGA 数据库中 HCC 的 RNA-seq 和临床数据，建立预后模型，并验证 ICGC 和 GSE14520 数据。使用单变量和 LASSO Cox 回归分析鉴定的 5 个基因构建风险评分。通过单变量和多变量 Cox 回归分析，将风险评分、TNM 分期和肝硬化纳入构建列线图。
从 70 个铁死亡相关差异表达基因中鉴定出 5 个基因，构建了一个基因签名，可预测 TCGA 队列中 HCC 患者的生存情况。PCA 和热图显示不同评分组患者之间存在明显差异。接下来，将风险评分、TNM 分期和肝硬化结合到一个列线图中，用于预测总生存期。生存分析表明，低风险组的总生存率明显高于高风险组。GSE14520 队列的数据证实了列线图具有令人满意的性能。此外，KEGG 和 GO 功能富集分析表明，风险组之间总生存期的差异与免疫相关途径密切相关。进一步的分析表明，免疫抑制性肿瘤微环境可能导致风险组之间预后的差异。
基于铁死亡相关基因的列线图在预测 HCC 患者的预后方面表现良好。该模型可能为针对铁死亡评估 HCC 患者提供参考。
