Identification and Validation of a Nine-Gene Amino Acid Metabolism-Related Risk Signature in HCC.
Identification and Validation of a Nine-Gene Amino Acid Metabolism-Related Risk Signature in HCC.
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HCC 九基因氨基酸代谢相关风险特征的鉴定和验证
DOI：
10.3389/fcell.2021.731790
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发表时间：
2021
影响因子：
5.5
通讯作者：
Xu K
中科院分区：
文献类型：
--
作者：
Zhao Y;Zhang J;Wang S;Jiang Q;Xu K
背景：肝细胞癌（HCC）是世界上第二大致死性癌症，代谢重编程是其显著特征。在代谢产物谱中，氨基酸代谢的变化在很大程度上支持了肿瘤的增殖和转移，但氨基酸代谢相关基因在肝癌中的作用尚缺乏系统的研究。因此，迫切需要一种有效的氨基酸代谢相关的预测信号来评估HCC患者的预后，以进行个体化治疗。材料与方法：将来自TCGA-LIHC和GSE14520（GPL3921）数据集的HCC的RNA-seq数据分别定义为训练集和验证集。从分子标记数据库中提取氨基酸代谢基因。进行单变量考克斯和LASSO回归分析以建立预测性风险特征。采用K-M曲线、ROC曲线、单变量和多变量考克斯回归分析评价该风险特征的预测价值。通过GSEA和CIBERSORTx软件分析功能富集。结果如下：构建了一个包括B3GAT3、B4GALT2、CYB5R3、GNPDA1、GOT2、HEXB、HMGCS2、PLOD2和SEPHS1在内的9个氨基酸代谢相关的风险标记，用于预测HCC患者的总生存期（OS）。根据风险评分将患者分为高风险组和低风险组，低风险患者的风险评分较低，生存时间较长。单因素和多因素考克斯回归分析证实，该特征是HCC的独立危险因素。ROC曲线显示，该风险特征可以有效预测肝癌患者的1、2、3和5年生存时间。此外，基于训练集和验证集建立了预后列线图。这些基因与免疫调节密切相关。结论：我们的研究确定了一个九基因氨基酸代谢相关的风险特征，并建立了预测肝癌OS的诺模图。这些发现将有助于我们个性化肝癌患者的治疗。
Background: Hepatocellular carcinoma (HCC) is the world’s second most deadly cancer, and metabolic reprogramming is its distinguishing feature. Among metabolite profiling, variation in amino acid metabolism supports tumor proliferation and metastasis to the most extent, yet a systematic study on the role of amino acid metabolism-related genes in HCC is still lacking. An effective amino acid metabolism-related prediction signature is urgently needed to assess the prognosis of HCC patients for individualized treatment. Materials and Methods: RNA-seq data of HCC from the TCGA-LIHC and GSE14520 (GPL3921) datasets were defined as the training set and validation set, respectively. Amino acid metabolic genes were extracted from the Molecular Signature Database. Univariate Cox and LASSO regression analyses were performed to build a predictive risk signature. K-M curves, ROC curves, and univariate and multivariate Cox regression were conducted to evaluate the predictive value of this risk signature. Functional enrichment was analyzed by GSEA and CIBERSORTx software. Results: A nine-gene amino acid metabolism-related risk signature including B3GAT3, B4GALT2, CYB5R3, GNPDA1, GOT2, HEXB, HMGCS2, PLOD2, and SEPHS1 was constructed to predict the overall survival (OS) of HCC patients. Patients were separated into high-risk and low-risk groups based on risk scores and low-risk patients had lower risk scores and longer survival time. Univariate and multivariate Cox regression verified that this signature was an independent risk factor for HCC. ROC curves showed that this risk signature can effectively predict the 1-, 2-, 3- and 5-year survival times of patients with HCC. Additionally, prognostic nomograms were established based on the training set and validation set. These genes were closely correlated with the immune regulation. Conclusion: Our study identified a nine-gene amino acid metabolism-related risk signature and built predictive nomograms for OS in HCC. These findings will help us to personalize the treatment of liver cancer patients.
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