硒是一种具有营养和毒理学特性的天然微量元素，与肿瘤发生和进展紧密相关。然而，目前对于硒代谢如何影响免疫反应和癌症生物学还不完全了解。我们利用癌症基因组图谱（TCGA）、基因型组织表达（GTEx）、癌细胞系百科全书（CCLE）和综合全癌单细胞数据集估计了硒代谢，并通过基因集富集分析（GSEA）描绘了硒代谢的全景。我们系统地探索了硒代谢的预后意义和硒相关的调控模式。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验。通过单细胞间细胞-细胞通信分析，研究了硒代谢的异质性和潜在机制。 我们使用包含86个基因的GSEA分析评估了硒代谢的全景。硒代谢评分在预测死亡风险较低方面具有预测价值，可能与多种癌症标志物相关，包括与互补系统呈正相关（R = 0.761，P <0.001）、炎症反应呈正相关（R = 0.663，P <0.001）、凋亡呈正相关（R = 0.626，P <0.001）、缺氧呈正相关（R = 0.587，P <0.001）、活性氧物种（ROS）呈正相关（R = 0.558，P <0.001）和干扰素γ反应呈正相关（R = 0.539，P <0.001）。我们还观察到硒代谢与免疫之间在不同癌症类型之间存在异质性。基于硒相关基因，我们建立了一个机器学习模型，在预测免疫检查点抑制剂（ICI）治疗反应方面，ROC曲线下面积（AUC）为0.82。单细胞硒代谢定量表明，邻近和肿瘤组织的硒代谢水平较正常组织高，尤其是上皮细胞、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通信显著高于其他细胞，尤其是在细胞因子、趋化因子、胶原蛋白、Wnt、VEGF、IGF和FGF通路中。 本研究全面描述了不同癌症中硒代谢水平和多样的调控模式，深化了对硒在肿瘤发生和免疫中作用的理解。 © 2023. 该作者及唯一授权给德国斯普林格自然出版集团的版权所有。

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.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.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.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.© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.