低级别胶质瘤（LGG）是中枢神经系统（CNS）最常见的恶性肿瘤之一。越来越多的证据表明，色氨酸代谢在肿瘤中具有重要意义。因此，本研究旨在全面阐明色氨酸代谢相关基因（TRG）与LGG之间的关系。首先分析了LGG和正常组织中TRG的表达水平。然后，利用最小绝对收缩和选择算子（LASSO）回归分析确定了具有预后价值和LGG差异表达的关键TRG。随后，根据关键TRG的表达水平构建了风险模型，并进行了一致性聚类分析。然后，分析了不同风险组和分子亚型之间的预后价值、临床病理因素和肿瘤免疫微环境（TIME）特征。最后，在LGG患者中分别分析了每个关键TRG的表达、预后和TIME。共纳入来自癌症基因组图谱（TCGA）数据集的510名LGG患者和来自基因型组织表达（GTEx）数据集的1152个正常组织，以评估TRG的表达水平。LASSO回归分析后，我们确定了六个关键TRG并构建了TRG风险模型。生存分析显示，风险模型是LGG患者的独立预测因子。包含风险评分和独立临床病理因素的诊断模型可以准确预测LGG患者的预后。此外，基于六个TRG的表达结果进行的一致性聚类分析结果将LGG患者分为两个不同的簇群，这两个簇群在预后、临床病理因素和TIME方面存在显著差异。最后，我们对LGG患者中六个关键TRG的表达、预后和免疫浸润进行了验证。本研究证明了色氨酸代谢在LGG的病理进展中起到重要作用。我们构建的风险模型和分子亚型不仅可以用作预测LGG患者预后的指标，还与LGG患者的临床病理因素和TIME密切相关。总之，本研究为实现LGG患者的精准治疗提供了理论支持。版权所有©2023年Li，Ling，Xiang，Ding和Yue。

Lower-grade glioma (LGG) is one of the most common malignant tumors in the central nervous system (CNS). Accumulating evidence have demonstrated that tryptophan metabolism is significant in tumor. Therefore, this study aims to comprehensively clarify the relationship between tryptophan metabolism-related genes (TRGs) and LGGs.The expression level of TRGs in LGG and normal tissues was first analyzed. Next, the key TRGs with prognostic value and differential expression in LGGs were identified using the least absolute shrinkage and selection operator (LASSO) regression analysis. Subsequently, a risk model was constructed and Consensus clustering analysis was conducted based on the expression level of key TRGs. Then, the prognostic value, clinicopathological factors, and tumor immune microenvironment (TIME) characteristics between different risk groups and molecular subtypes were analyzed. Finally, the expression, prognosis, and TIME of each key TRGs were analyzed separately in LGG patients.A total of 510 patients with LGG from The Cancer Genome Atlas (TCGA) dataset and 1,152 normal tissues from the Genotype-Tissue Expression (GTEx) dataset were included to evaluate the expression level of TRGs. After LASSO regression analysis, we identified six key TRGs and constructed a TRGs risk model. The survival analysis revealed that the risk model was the independent predictor in LGG patients. And the nomogram containing risk scores and independent clinicopathological factors could accurately predict the prognosis of LGG patients. In addition, the results of the Consensus cluster analysis based on the expression of the six TRGs showed that it could classify the LGG patients into two distinct clusters, with significant differences in prognosis, clinicopathological factors and TIME between these two clusters. Finally, we validated the expression, prognosis and immune infiltration of six key TRGs in patients with LGG.This study demonstrated that tryptophan metabolism plays an important role in the progression of LGG. In addition, the risk model and the molecular subtypes we constructed not only could be used as an indicator to predict the prognosis of LGG patients but also were closely related to the clinicopathological factors and TIME of LGG patients. Overall, our study provides theoretical support for the ultimate realization of precision treatment for patients with LGG.Copyright © 2023 Li, Ling, Xiang, Ding and Yue.