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鉴定出一种四基因代谢特征,可预测肝细胞癌的总生存期。

Identification of a four-gene metabolic signature predicting overall survival for hepatocellular carcinoma.

机构信息

Department of Hepatobiliary Surgery, Meizhou People's Hospital, Meizhou, China.

Guangdong Provincial Key Laboratory of Precision Medicine and Clinical Translational Research of Hakka Population, Meizhou, China.

出版信息

J Cell Physiol. 2020 Feb;235(2):1624-1636. doi: 10.1002/jcp.29081. Epub 2019 Jul 16.

Abstract

While hundreds of consistently altered metabolic genes had been identified in hepatocellular carcinoma (HCC), the prognostic role of them remains to be further elucidated. Messenger RNA expression profiles and clinicopathological data were downloaded from The Cancer Genome Atlas-Liver Hepatocellular Carcinoma and GSE14520 data set from the Gene Expression Omnibus database. Univariate Cox regression analysis and lasso Cox regression model established a novel four-gene metabolic signature (including acetyl-CoA acetyltransferase 1, glutamic-oxaloacetic transaminase 2, phosphatidylserine synthase 2, and uridine-cytidine kinase 2) for HCC prognosis prediction. Patients in the high-risk group shown significantly poorer survival than patients in the low-risk group. The signature was significantly correlated with other negative prognostic factors such as higher α-fetoprotein. The signature was found to be an independent prognostic factor for HCC survival. Nomogram including the signature shown some clinical net benefit for overall survival prediction. Furthermore, gene set enrichment analyses revealed several significantly enriched pathways, which might help explain the underlying mechanisms. Our study identified a novel robust four-gene metabolic signature for HCC prognosis prediction. The signature might reflect the dysregulated metabolic microenvironment and provided potential biomarkers for metabolic therapy and treatment response prediction in HCC.

摘要

虽然已经在肝细胞癌 (HCC) 中鉴定出数百个持续改变的代谢基因,但它们的预后作用仍有待进一步阐明。从癌症基因组图谱-肝肝细胞癌和基因表达综合数据库中的 GSE14520 数据集下载信使 RNA 表达谱和临床病理数据。单变量 Cox 回归分析和套索 Cox 回归模型建立了一种新的四基因代谢特征(包括乙酰辅酶 A 乙酰转移酶 1、谷草转氨酶 2、磷酸丝氨酸合酶 2 和尿苷-胞苷激酶 2),用于 HCC 预后预测。高风险组的患者生存明显差于低风险组的患者。该特征与其他负预后因素(如较高的甲胎蛋白)显著相关。该特征被发现是 HCC 生存的独立预后因素。包括该特征的列线图显示出对总生存期预测的一些临床净获益。此外,基因集富集分析揭示了几个显著富集的途径,这可能有助于解释潜在的机制。我们的研究确定了一种用于 HCC 预后预测的新型稳健的四基因代谢特征。该特征可能反映了失调的代谢微环境,并为 HCC 的代谢治疗和治疗反应预测提供了潜在的生物标志物。

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