Aims: To explore tyrosine metabolism-related characteristics in liver hepatocellular carcinoma (LIHC) and to establish a risk signature for the prognostic prediction of LIHC. Novel prognostic signatures contribute to the mining of novel biomarkers, which are essential for the construction of a precision medicine system for LIHC and the improvement of survival. Background: Tyrosine metabolism plays a critical role in the initiation and development of LIHC. Based on the tyrosine metabolism-related characteristics in LIHC, this study developed a risk signature to improve the prognostic prediction of patients with LIHC. Objective: To investigate the correlation between tyrosine metabolism and progression of LIHC and to develop a tyrosine metabolism-related prognostic model. Methods: Gene expression and clinicopathological information of LIHC were obtained from The Cancer Genome Atlas (TCGA) database. Distinct subtypes of LIHC were classified by performing consensus cluster analysis on the tyrosine metabolism-related genes. Univariate and Lasso Cox regression were used to develop a RiskScore prognosis model. Kaplan-Meier (KM) survival analysis with log-rank test and area under the curve (AUC) of receiver operating characteristic (ROC) were employed in the prognostic evaluation and prediction validation. Immune infiltration, tyrosine metabolism score, and pathway enrichment were evaluated using single-sample gene set enrichment analysis (ssGSEA). Finally, a nomogram model was developed with the RiskScore and other clinicopathological features. Results: Based on the tyrosine metabolism genes in the TCGA cohort, we identified 3 tyrosine metabolism-related subtypes showing significant prognostic differences. Four candidate genes selected from the common differentially expressed genes (DEGs) between the 3 subtypes were used to develop a RiskScore model, which could effectively divide LIHC patients into high- and lowrisk groups. In both the training and validation sets, high-risk patients tended to have worse overall survival, less active immunotherapy response, higher immune infiltration and clinical grade, and higher oxidative, fatty, and xenobiotic metabolism pathways. Multivariate analysis confirmed that the RiskScore was an independent indicator for the prognosis of LIHC. The results from pan-- cancer analysis also supported that the RiskScore had a strong prognostic performance in other cancers. The nomogram demonstrated that the RiskScore contributed the most to the prediction of LIHC prognosis. Conclusion: Our study developed a tyrosine metabolism-related risk model that performed well in survival prediction, showing the potential to serve as an independent prognostic predictor for LIHC treatment.

中文翻译：

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肝细胞癌治疗中酪氨酸代谢相关预后特征的分析和验证

目的：探讨肝细胞癌 (LIHC) 中酪氨酸代谢相关特征，并建立 LIHC 预后预测的风险特征。新的预后特征有助于挖掘新的生物标志物，这对于构建 LIHC 精准医疗系统和提高生存率至关重要。背景：酪氨酸代谢在 LIHC 的发生和发展中起着至关重要的作用。本研究根据 LIHC 的酪氨酸代谢相关特征，开发了一个风险特征，以改善 LIHC 患者的预后预测。目的：探讨酪氨酸代谢与 LIHC 进展的相关性，建立酪氨酸代谢相关的预后模型。方法：从癌症基因组图谱（TCGA）数据库中获得 LIHC 的基因表达和临床病理信息。通过对酪氨酸代谢相关基因进行共识聚类分析，对 LIHC 的不同亚型进行分类。使用单变量和 Lasso Cox 回归来开发 RiskScore 预后​​模型。预后评估和预测验证采用卡普兰-迈耶 (KM) 生存分析、对数秩检验和受试者工作特征 (ROC) 曲线下面积 (AUC)。使用单样本基因集富集分析（ssGSEA）评估免疫浸润、酪氨酸代谢评分和通路富集。最后，利用风险评分和其他临床病理特征开发了列线图模型。结果：根据 TCGA 队列中的酪氨酸代谢基因，我们确定了 3 种与酪氨酸代谢相关的亚型，显示出显着的预后差异。从3种亚型之间常见的差异表达基因（DEG）中选出的4个候选基因被用来开发RiskScore模型，该模型可以有效地将LIHC患者分为高风险组和低风险组。在训练和验证集中，高风险患者的总体生存率往往较差，免疫治疗反应较差，免疫浸润和临床分级较高，以及氧化、脂肪和异生物质代谢途径较高。多变量分析证实RiskScore是LIHC预后的独立指标。泛癌症分析的结果也支持 RiskScore 在其他癌症中具有很强的预后表现。列线图表明，RiskScore 对 LIHC 预后的预测贡献最大。结论：我们的研究开发了一种酪氨酸代谢相关风险模型，在生存预测中表现良好，显示出作为 LIHC 治疗的独立预后预测因子的潜力。