Abstract
BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.