背景:硒,一种具有营养和毒理学特性的天然微量元素,与肿瘤发生和进展交织在一起。然而,尚不完全了解硒代谢如何影响免疫反应和癌症生物学。
方法:我们通过基因集富集分析(GSEA)评估硒代谢,以使用癌症基因组图谱(TCGA)描绘硒代谢景观,基因型-组织表达(GTEx),癌细胞系百科全书(CCLE)和整合的泛癌症单细胞数据集。我们系统地探讨了硒代谢和硒相关调节模式的预后意义。通过机器学习探索了硒代谢的治疗价值,并在几个免疫治疗队列中进行了检查。通过单细胞水平的细胞-细胞通讯分析研究了硒代谢的异质性和潜在机制。
结果:基于86个基因的GSEA分析用于评估硒代谢景观。硒代谢评分在预测较低的死亡风险方面表现出预后价值,可能是由于它与多种癌症标志有关,与补体呈正相关(R=0.761,P<0.001),炎症反应(R=0.663,P<0.001),细胞凋亡(R=0.626,P<0.001),缺氧(R=0.587,P<0.001),活性氧(ROS)(R=0.558,P<0.001),和干扰素γ反应(R=0.539,P<0.001)。我们还观察到不同癌症中硒代谢与免疫力之间关系的异质性。基于硒相关基因,我们构建了一个ROC曲线下面积(AUC)为0.82的机器学习模型,用于预测基于免疫检查点抑制剂(ICI)的免疫治疗反应.单细胞硒代谢定量显示,与正常组织相比,癌旁组织和肿瘤组织具有更高的硒代谢,尤其是在上皮细胞中,成纤维细胞和巨噬细胞。高硒上皮细胞与高硒成纤维细胞之间的交流明显高于其他细胞,特别是在细胞因子中,趋化因子,胶原蛋白,Wnt,VEGF,IGF和FGF途径。
结论:我们的研究提供了不同癌症中硒代谢水平和不同调控模式的全面图景,加深对硒在肿瘤发生和免疫中作用的认识。
BACKGROUND: Selenium, a natural microelement with both nutritional and toxicological properties, is intertwined with tumorigenesis and progression. However, it is not fully understood how selenium metabolism affects immune response and cancer biology.
METHODS: We estimated selenium metabolism by Gene Set Enrichment Analysis (GSEA) to delineate the selenium metabolism landscape using The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), Cancer Cell Line Encyclopedia (CCLE) and a integrated pan-cancer single-cell dataset. We systematically explored the prognostic implications of selenium metabolism and selenium-related regulatory patterns. The therapeutic value of selenium metabolism was explored through machine learning and examined in several immunotherapy cohorts. The heterogeneity and underlying mechanism of selenium metabolism were investigated by cell‒cell communication analysis at the single-cell level.
RESULTS: A GSEA analysis based on 86 genes was used to evaluate the selenium metabolism landscape. The selenium metabolism score exhibited prognostic value in predicting the lower risk of mortality, possibly due to its correlation with multiple cancer hallmarks, including a positive correlation with complement (R = 0.761, P < 0.001), inflammatory response (R = 0.663, P < 0.001), apoptosis (R = 0.626, P < 0.001), hypoxia (R = 0.587, P < 0.001), reactive oxygen species (ROS) (R = 0.558, P < 0.001), and interferon gamma response (R = 0.539, P < 0.001). We also observed heterogeneity in the relationship between selenium metabolism and immunity across different cancers. Based on selenium-related genes, we constructed a machine learning model with area under the ROC curve (AUC) of 0.82 in predicting immune checkpoint inhibitor (ICI)-based immunotherapy response. Single-cell selenium metabolism quantification revealed that adjacent and tumor tissues had higher selenium metabolism compared with normal tissues, especially in epithelial cells, fibroblasts and macrophages. The communication between high-selenium epithelium and high-selenium fibroblast was significantly higher than other cells, especially in cytokines, chemokines, collagen, Wnt, VEGF, IGF and FGF pathways.
CONCLUSIONS: Our study provides a comprehensive landscape of selenium metabolism levels and diverse regulatory patterns in different cancers, deepening the understanding of selenium\'s roles in tumorigenesis and immunity.