预测肝细胞癌预后与免疫治疗反应的色氨酸代谢模式的发展
Development of tryptophan metabolism patterns to predict prognosis and immunotherapeutic responses in hepatocellular carcinoma.
发表日期:2023 Aug 03
作者:
Guo Long, Dong Wang, Jianing Tang, Weifeng Tang
来源:
Immunity & Ageing
摘要:
色氨酸代谢与多种癌症的肿瘤发生和肿瘤免疫应答相关。肝脏是色氨酸降解的主要场所。然而,色氨酸代谢在肝细胞癌(HCC)中的作用尚未被很好地阐明。在本研究中,我们描述了HCC队列中42个与色氨酸代谢相关的基因(TRPGs)的突变情况。然后,根据这42个TRPGs的表达谱将HCC患者分为两个亚型。对这两个亚型的临床病理特征和肿瘤微环境景观进行了评估。我们还建立了一个TRPGs评分系统,并且确定了四个标志性的TRPGs,包括ACSL3,ADH1B,ALDH2和HADHA。单因素和多因素Cox回归分析表明,TRPGs签名是HCC患者的独立预后指标。此外,通过受试者工作特征曲线(ROC)分析评估了TRPGs签名的预测准确性。这些结果显示,TRPG风险模型在TCGA和GEO HCC队列中对预测生存具有出色的能力。此外,我们发现TRPGs签名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。我们的综合分析加深了我们对HCC中TRPGs的理解。基于TRPGs的新型预测模型被建立,可以作为预测HCC患者临床结果的有益工具。
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 ACSL3, ADH1B, ALDH2, and HADHA. 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.