代谢重编程对于建立肿瘤微环境至关重要。谷氨酰胺已被认为在癌症代谢中具有重要作用，但其在透明细胞肾癌（ccRCC）中的作用尚未得到确认。从癌症基因组图谱（TCGA，539个ccRCC样本和59个正常样本）数据库和GSE152938（5个ccRCC样本）中获取了患有ccRCC的病人的转录组数据和单细胞RNA测序（scRNA-seq）数据。从MSigDB数据库获取了与谷氨酰胺代谢相关的不同表达基因（GRGs）。共识聚类分析区分了与代谢相关的ccRCC亚型。使用LASSO-Cox回归分析构建了代谢相关的预后模型。ssGSEA和ESTIMATE算法评估了TME中免疫细胞浸润的水平，TIDE则获得了免疫治疗敏感性评分。使用细胞间通信分析观察了目标基因在细胞亚群中的分布和作用。使用图像特征提取和机器学习算法构建了图像基因组模型。结果：确定了14个GRGs。与群集1相比，代谢群集2的总生存率和无进展生存率更低。在C1中，矩阵/ESTIMATE /免疫得分降低，但在C2中，肿瘤纯度增加。高风险组的免疫细胞更加活跃，其中CD8 + T细胞，滤泡辅助T细胞，Th1细胞和Th2细胞显着高于低风险组。 免疫检查点的表达水平在两组之间也存在显着差异。RIMKL主要出现在单细胞分析的上皮细胞中，ARHGAP11B则分布稀疏。图像基因组模型在帮助临床决策方面证明了其有效性。谷氨酰胺代谢在ccRCC的免疫TME形成中发挥着至关重要的作用。它在区分风险和预测ccRCC患者生存方面非常有效。图像特征可以用作预测ccRCC免疫疗法的新生物标志物。©2023年作者，独家许可Springer-Verlag GmbH Germany，Springer Nature的一部分。

Metabolic reprogramming is essential for establishing the tumor microenvironment (TME). Glutamine has been implicated in cancer metabolism, but its role in clear cell renal carcinoma (ccRCC) remains unknown. Transcriptome data of patients with ccRCC and single-cell RNA sequencing (scRNA-seq) data were obtained from The Cancer Genome Atlas (TCGA, 539 ccRCC samples and 59 normal samples) database and GSE152938 (5 ccRCC samples). Differentially expressed genes related to glutamine metabolism (GRGs) were obtained from the MSigDB database. Consensus cluster analysis distinguished metabolism-related ccRCC subtypes. LASSO-Cox regression analysis was used to construct a metabolism-related prognostic model. The ssGSEA and ESTIMATE algorithms evaluated the level of immune cell infiltration in the TME, and the immunotherapy sensitivity score was obtained from TIDE. Cell-cell communication analysis was used to observe the distribution and effects of the target genes in the cell subsets. An image genomics model was constructed using imaging feature extraction and a machine learning algorithm. Results: Fourteen GRGs were identified. Overall survival and progression-free survival rates were lower in metabolic cluster 2, compared with those in cluster 1. The matrix/ESTIMATE/immune score in C1 decreased, but tumor purity in C2 increased. Immune cells were more active in the high-risk group, in which CD8 + T cells, follicular helper T cells, Th1 cells, and Th2 cells were significantly higher than those in the low-risk group. The expression levels of immune checkpoints were also significantly different between the two groups. RIMKL mainly appeared in epithelial cells in the single-cell analysis. ARHGAP11B was sparsely distributed. The imaging genomics model proved effective in aiding with clinical decisions. Glutamine metabolism plays a crucial role in the formation of immune TMEs in ccRCC. It is effective in differentiating the risk and predicting survival in patients with ccRCC. Imaging features can be used as new biomarkers for predicting ccRCC immunotherapy.© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.