Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, 210029, Jiangsu Province, China.
Nanjing University of Chinese Medicine, Nanjing, 210029, Jiangsu Province, China.
Sci Rep. 2022 May 24;12(1):8785. doi: 10.1038/s41598-022-12800-6.
The study is to explore the role of ferroptosis-related genes (FRGs) in the occurrence and development of gastric cancer (GC), and to construct a new prognosis signature to predict the prognosis in GC. Clinical information and corresponding RNA data of GC patients were downloaded from TCGA and GEO databases. Consensus clustering was performed to identify new molecular subgroups. ESTIMATE, CIBERSORT, McpCounter and TIMER algorithm were used to analyze the infiltration of immune cells in two molecular subgroups. LASSO algorithm and multivariate Cox analysis were used to construct a prognostic risk signature. Functional analysis was conducted to elucidate the underlying mechanisms. Finally, the FRPGs were verified by Quantitative Real-Time PCR. We obtained 16 FRGs and divided GC patients into two subgroups by consistent clustering. Cluster C1 with a higher abundance of immune cell infiltration but lower probability in response to immunotherapy, it was reasonable to speculate that Cluster C1 was in accordance with the immune rejection type. Functional analysis showed that the biological process of DEGs in training cohort mainly included immune globulin, and human immune response mediated by circulating immune globulin. GSEA analysis showed that compared with Cluster C2, Cluster C1 showed lower expression in lipid metabolism. The nomogram combined with risk signature and clinical features can accurately predict the prognosis of GC patients. We identified two molecular subtypes, Clusters C1 and C2. In Cluster C1, patients with poor prognosis present with a hyperimmune status and low lipid metabolism, and we speculate that Cluster C1 was in accordance with the immune rejection type. The risk model based on FRPGs can accurately predict the prognosis of GC. These results indicated that ferroptosis is associated with TIME, and deserved considerable attention in determining immunotherapy treatment strategy for GC patients.
该研究旨在探讨铁死亡相关基因（FRGs）在胃癌（GC）发生发展中的作用，并构建新的预后标志物预测 GC 患者的预后。从 TCGA 和 GEO 数据库中下载 GC 患者的临床信息和相应的 RNA 数据。通过一致性聚类识别新的分子亚群。使用 ESTIMATE、CIBERSORT、McpCounter 和 TIMER 算法分析两个分子亚群中免疫细胞的浸润情况。使用 LASSO 算法和多变量 Cox 分析构建预后风险特征。进行功能分析以阐明潜在的机制。最后，通过定量实时 PCR 验证 FRPGs。我们获得了 16 个 FRGs，并通过一致聚类将 GC 患者分为两个亚组。亚组 C1 中免疫细胞浸润的丰度较高，但对免疫治疗的反应可能性较低，因此可以合理地推测亚组 C1 符合免疫排斥类型。功能分析显示，训练队列中 DEGs 的生物学过程主要包括免疫球蛋白和循环免疫球蛋白介导的人类免疫反应。GSEA 分析表明，与亚组 C2 相比，亚组 C1 中脂质代谢的表达较低。结合风险特征和临床特征的列线图可以准确预测 GC 患者的预后。我们确定了两个分子亚型，C1 簇和 C2 簇。在 C1 簇中，预后不良的患者表现出高免疫状态和低脂质代谢，我们推测 C1 簇符合免疫排斥类型。基于 FRPGs 的风险模型可以准确预测 GC 的预后。这些结果表明，铁死亡与 TIME 相关，值得在确定 GC 患者的免疫治疗策略时给予充分重视。
