Wang Huaxiang, Yang Chengkai, Jiang Yi, Hu Huanzhang, Fang Jian, Yang Fang
Department of Hepatobiliary and Pancreatic Surgery, Taihe Hospital, Affiliated Hospital of Hubei University of Medicine Shiyan, Hubei 442000, China.
The Fuzong Clinical Medical College of Fujian Medical University Fuzhou 350025, Fujian, China.
Am J Cancer Res. 2022 May 15;12(5):1995-2011. eCollection 2022.
High recurrence rate in HCC is the primary cause of the poor prognosis after hepatectomy. Therefore, in this study, we aimed to construct a gene signature for predicting the recurrence rate in HCC. The mRNA expression profiles and clinical information of HCC patients from GEO and TCGA databases were used, and ferroptosis-related gene list was obtained from the FerrDb database. We identified 39 ferroptosis-related genes (FDEGs) that were differentially expressed between HCC samples and normal tissues from the GSE14520 dataset. The univariate and multivariate Cox regression analyses were employed to construct a prognostic signature. Seven FDEGs (MAPK9, SLC1A4, PCK2, ACSL3, STMN1, CDO1, and CXCL2) were included to construct a risk model, which was validated in the TCGA dataset. Patients in high-risk groups exhibited a significantly poor prognosis compared with patients in low-risk groups in both the training set (GSE14520 cohort) and the validation set (TCGA cohort). Multivariate cox regression analyses demonstrated that the 7-gene signature was an independent risk factor for RFS in HCC patients. KEGG analysis showed that FDEGs were mainly enriched in Ferroptosis, Hepatocellular carcinoma pathway, and MAPK signaling pathway. GSEA analysis suggested that the high-risk group was correlated with multiple oncogenic signatures and invasive-related pathways. These results indicated that this risk model can accurately predict recurrence after hepatectomy and offer novel research directions for personalized treatment in HCC patients.
肝癌的高复发率是肝切除术后预后不良的主要原因。因此，在本研究中，我们旨在构建一个用于预测肝癌复发率的基因特征。我们使用了来自GEO和TCGA数据库的肝癌患者的mRNA表达谱和临床信息，并从FerrDb数据库中获得了铁死亡相关基因列表。我们从GSE14520数据集中鉴定出39个在肝癌样本和正常组织之间差异表达的铁死亡相关基因（FDEGs）。采用单变量和多变量Cox回归分析来构建一个预后特征。纳入7个FDEGs（MAPK9、SLC1A4、PCK2、ACSL3、STMN1、CDO1和CXCL2）构建风险模型，并在TCGA数据集中进行验证。在训练集（GSE14520队列）和验证集（TCGA队列）中，高风险组患者的预后均明显差于低风险组患者。多变量Cox回归分析表明，7基因特征是肝癌患者无复发生存期的独立危险因素。KEGG分析表明，FDEGs主要富集在铁死亡、肝细胞癌通路和MAPK信号通路中。GSEA分析表明，高风险组与多个致癌特征和侵袭相关通路相关。这些结果表明，该风险模型可以准确预测肝切除术后的复发情况，并为肝癌患者的个性化治疗提供新的研究方向。
