An inflammation-associated ferroptosis signature can optimize the diagnosis, prognosis evaluation and immunotherapy options in hepatocellular carcinoma

Background: Inflammation and ferroptosis crosstalk complexly with immune microenvironment of hepatocellular carcinoma (HCC), thus affecting the efficacy of immunotherapy. Herein, our aim was to identify the inflammation associated ferroptosis (IAF)- biomarkers for contributing the immunotherapy of HCC. Methods: The train cohort from The Cancer Genome Atlas (TCGA) was clustered into three subtypes (C1, C2, and C3) based on the genes related to inflammation and ferroptosis. Intersecting differentially expressed genes (DEGs) among the subtypes were set as IAF-genes and enrolled into construction of a risk model following by LASSO, univariate Cox and multivariate COX regression analysis. The effectiveness of risk model on prognosis evaluation, immunological signature recognition and immunotherapy options was analyzed by ESTIMATE, CIBERSORT, and TIDE algorithm. The practicability of risk model in HCC tissues (N = 40) from our research group were detected by performing qRT-PCR, immunohistochemical (IHC) analysis and reviewing of clinical characters. Results: A total of 224 intersecting DEGs identified from different inflammation- and ferroptosis-subtypes were set as IAF-genes. Seven of them including ADH4, APOA5, CFHR3, CXCL8, FTCD, G6PD and PON1 were used for construction of a risk model which classified HCC patients into two groups (high and low risk). HCC patients in high risk group exhibited shorter survival rate and higher immune score, and were predicted to have higher respond rate in immune checkpoint inhibition (ICI) therapy. qRT-PCR results demonstrated that levels of the seven genes were significantly changed in HCC tissues in comparison to adjacent tissues. After inserting the gene expression into the risk model, we found that the risk model exhibited the higher diagnostic value for distinguish HCC tissues compared each single gene. Furthermore, through performing reviewing clinical characters and IHC analysis, we found HCC tissues from our research group with high risk score exhibited more cases of microsatellite instability (MSI), heavier tumor mutational burden (TMB), higher expression level of PDL1 and cells with CD8. Conclusions: The signature constructed by seven IAF-genes including ADH4, APOA5, CFHR3, CXCL8, FTCD, G6PD and PON1 can act as a biomarker for optimizing the diagnosis, prognosis evaluation and immunotherapy options in HCC patients. <br>

背景：炎症与铁死亡（ferroptosis）同肝细胞癌（hepatocellular carcinoma, HCC）的免疫微环境存在复杂的交互调控关系，进而影响免疫治疗的临床疗效。本研究旨在筛选炎症相关铁死亡（inflammation associated ferroptosis, IAF）生物标志物，以助力肝细胞癌的免疫治疗。
方法：从癌症基因组图谱（The Cancer Genome Atlas, TCGA）获取训练队列，基于炎症及铁死亡相关基因将其划分为C1、C2、C3三个亚型。提取各亚型间的交集差异表达基因（differentially expressed genes, DEGs）并定义为IAF基因，随后通过LASSO回归、单因素Cox回归及多因素Cox回归分析构建风险模型。采用ESTIMATE、CIBERSORT及TIDE算法，评估该风险模型在预后评估、免疫特征识别及免疫治疗方案选择中的应用效能。通过实时定量聚合酶链反应（quantitative real-time polymerase chain reaction, qRT-PCR）、免疫组化（immunohistochemical, IHC）分析及临床特征回顾，验证本研究团队收集的40例肝癌组织样本中该风险模型的实用性。
结果：从不同炎症及铁死亡亚型中共筛选得到224个交集DEGs，将其定为IAF基因。其中7个基因（ADH4、APOA5、CFHR3、CXCL8、FTCD、G6PD及PON1）用于构建风险模型，将肝癌患者划分为高风险组与低风险组。高风险组肝癌患者的生存率更低、免疫评分更高，且被预测在免疫检查点抑制剂（immune checkpoint inhibition, ICI）治疗中具有更高的应答率。qRT-PCR结果显示，相较于癌旁正常组织，肝癌组织中这7个基因的表达水平存在显著差异。将基因表达数据纳入风险模型后发现，相较于单个基因，该风险模型具有更高的诊断区分效能。此外，通过临床特征回顾及IHC分析发现，本研究团队收集的高风险评分肝癌组织更多表现为微卫星不稳定（microsatellite instability, MSI）、更高的肿瘤突变负荷（tumor mutational burden, TMB），且PD-L1及CD8阳性细胞的表达水平更高。
结论：由ADH4、APOA5、CFHR3、CXCL8、FTCD、G6PD及PON1这7个IAF基因构建的特征标签，可作为优化肝细胞癌患者诊断、预后评估及免疫治疗方案选择的生物标志物。