Department of Liver Surgery, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China.
National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha 410008, Hunan, China.
Aging (Albany NY). 2023 Aug 3;15(15):7593-7615. doi: 10.18632/aging.204928.
Tryptophan metabolism is associated with tumorigenesis and tumor immune response in various cancers. Liver is the main place where tryptophan catabolism is performed. However, the role of tryptophan metabolism in hepatocellular carcinoma (HCC) has not been well clarified. In the present study, we described the mutations of 42 tryptophan metabolism-related genes (TRPGs) in HCC cohorts. Then, HCC patients were well distributed into two subtypes based on the expression profiles of the 42 TRPGs. The clinicopathological characteristics and tumor microenvironmental landscape of the two subtypes were profiled. We also established a TRPGs scoring system and identified four hallmark TRPGs, including and . Univariate and multivariate Cox regression analysis revealed that the TRPG signature was an independent prognostic indicator for HCC patients. Besides, the predictive accuracy of the TRPG signature was assessed by the receiver operating characteristic curve (ROC) analysis. These results showed that the TRPG risk model had an excellent capability in predicting survival in both TCGA and GEO HCC cohorts. Moreover, we discovered that the TRPG signature was significantly related to the different immune infiltration and therapeutic drug sensitivity. The functional experiments and immunohistochemistry staining analysis also validated the results above. Our comprehensive analysis enhanced our understanding of TRPGs in HCC. A novel predictive model based on TRPGs was built, which may be considered as a beneficial tool for predicting the clinical outcomes of HCC patients.
色氨酸代谢与各种癌症的肿瘤发生和肿瘤免疫反应有关。肝脏是色氨酸分解代谢的主要场所。然而,色氨酸代谢在肝细胞癌(HCC)中的作用尚未得到充分阐明。在本研究中,我们描述了 HCC 队列中 42 个色氨酸代谢相关基因(TRPGs)的突变。然后,根据 42 个 TRPGs 的表达谱将 HCC 患者分为两个亚型。分析了这两种亚型的临床病理特征和肿瘤微环境景观。我们还建立了一个 TRPGs 评分系统,并确定了四个标志性的 TRPGs,包括 和 。单因素和多因素 Cox 回归分析表明,TRPG 特征是 HCC 患者的独立预后指标。此外,通过接受者操作特征曲线(ROC)分析评估了 TRPG 特征的预测准确性。这些结果表明,TRPG 风险模型在 TCGA 和 GEO HCC 队列中均具有出色的生存预测能力。此外,我们发现 TRPG 特征与不同的免疫浸润和治疗药物敏感性显著相关。功能实验和免疫组织化学染色分析也验证了上述结果。我们的综合分析增强了我们对 HCC 中 TRPGs 的理解。建立了一个基于 TRPGs 的新预测模型,它可以被认为是预测 HCC 患者临床结局的有益工具。