Table_4_Prognostic prediction and immune infiltration analysis based on ferroptosis and EMT state in hepatocellular carcinoma.docx

BackgroundFerroptosis is one of the main mechanisms of sorafenib against hepatocellular carcinoma (HCC). Epithelial-mesenchymal transition (EMT) plays an important role in the heterogeneity, tumor metastasis, immunosuppressive microenvironment, and drug resistance of HCC. However, there are few studies looking into the relationship between ferroptosis and EMT and how they may affect the prognosis of HCC collectively.
MethodsWe downloaded gene expression and clinical data of HCC patients from the Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases for prognostic model construction and validation respectively. The Least absolute shrinkage and selection operator (LASSO) Cox regression was used for model construction. The predictive ability of the model was assessed by Kaplan–Meier survival analysis and receiver operating characteristic (ROC) curve. We performed the expression profiles analysis to evaluate the ferroptosis and EMT state. CIBERSORT and single-sample Gene Set Enrichment Analysis (ssGSEA) methods were used for immune infiltration analysis.
ResultsA total of thirteen crucial genes were identified for ferroptosis-related and EMT-related prognostic model (FEPM) stratifying patients into two risk groups. The high-FEPM group had shorter overall survivals than the low-FEPM group (p<0.0001 in the TCGA cohort and p<0.05 in the ICGC cohort). The FEPM score was proved to be an independent prognostic risk factor (HR>1, p<0.01). Furthermore, the expression profiles analysis suggested that the high-FEPM group appeared to have a more suppressive ferroptosis status and a more active EMT status than the low- FEPM group. Immune infiltration analysis showed that the myeloid-derived suppressor cells (MDSCs), and regulatory T cells (Tregs) were highly enriched in the high-FEPM group. Finally, a nomogram enrolling FEPM score and TNM stage was constructed showing outstanding predictive capacity for the prognosis of patients in the two cohorts.
ConclusionIn conclusion, we developed a ferroptosis-related and EMT-related prognostic model, which could help predict overall survival for HCC patients. It might provide a new idea for predicting the response to targeted therapies and immunotherapies in HCC patients.

背景：铁死亡（ferroptosis）是索拉非尼对抗肝细胞癌（hepatocellular carcinoma, HCC）的主要机制之一。上皮间质转化（epithelial-mesenchymal transition, EMT）在肝细胞癌的异质性、肿瘤转移、免疫抑制微环境以及耐药性中发挥重要作用。然而，目前针对铁死亡与上皮间质转化之间的关联，以及二者如何共同影响肝细胞癌预后的研究尚少。
方法：我们分别从癌症基因组图谱（The Cancer Genome Atlas, TCGA）以及国际癌症基因组联盟（International Cancer Genome Consortium, ICGC）数据库下载肝细胞癌患者的基因表达与临床数据，分别用于预后模型的构建与验证。采用最小绝对收缩和选择算子（Least absolute shrinkage and selection operator, LASSO）Cox回归进行模型构建。通过卡普兰-迈耶（Kaplan-Meier）生存分析以及受试者工作特征（receiver operating characteristic, ROC）曲线评估模型的预测能力。我们开展表达谱分析以评估铁死亡与上皮间质转化状态。采用CIBERSORT与单样本基因集富集分析（single-sample Gene Set Enrichment Analysis, ssGSEA）方法进行免疫浸润分析。
结果：本研究共筛选出13个关键基因，用于构建铁死亡相关及上皮间质转化相关预后模型（ferroptosis-related and EMT-related prognostic model, FEPM），并将患者划分为两个风险组。高FEPM风险组患者的总生存期短于低FEPM风险组（TCGA队列中p<0.0001，ICGC队列中p<0.05）。FEPM评分被证实为独立的预后风险因素（风险比HR>1，p<0.01）。此外，表达谱分析结果显示，相较于低FEPM风险组，高FEPM风险组的铁死亡状态更受抑制，而上皮间质转化状态更为活跃。免疫浸润分析结果显示，髓系来源抑制细胞（myeloid-derived suppressor cells, MDSCs）与调节性T细胞（regulatory T cells, Tregs）在高FEPM风险组中显著富集。最后，我们构建了纳入FEPM评分与TNM分期的列线图，该列线图在两个队列中均展现出优异的患者预后预测能力。
结论：综上，本研究构建了铁死亡相关及上皮间质转化相关预后模型，可用于预测肝细胞癌患者的总生存期，或可为肝细胞癌患者的靶向治疗与免疫治疗应答预测提供新思路。