代谢表型与转录组学元数据相结合,强化了早期肝细胞癌的诊断。

Sun Jo Kim, Cheol Woon Jung, Nguyen Hoang Anh, Young Cheol Yoon, Nguyen Phuoc Long, Soon-Sun Hong, Eun Ju Cho, Sung Won Kwon
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引用次数: 0

摘要

简介:甲胎蛋白(AFP)的灵敏度较低,不适合单独作为早期肝细胞癌(eHCC)监测的标志物。因此,需要其他灵敏度更高的血液生物标志物:目的:鉴于新陈代谢的变化是 eHCC 发展的显著特征,本研究提出了一组血清代谢物,可作为 eHCC 患者的非侵入性诊断指标:采用四种不同的代谢组学平台分析了正常对照组(NC)、肝硬化和 eHCC 患者的血清样本。对从公共资源检索到的极早期 HCC 转录组数据集进行的荟萃分析支持对代谢变化进行综合解释:结果:共有 94 个代谢物与疾病进展状态显著相关。荟萃分析中的重要代谢物和差异表达基因的综合分析强调了包括胆汁酸生物合成、苯丙氨酸和酪氨酸代谢以及丁酸代谢在内的代谢通路。与这些途径相关的 11 种代谢物被汇编成一个代谢物面板,用作诊断特征。该模型的准确率为 81.8%,而仅根据甲胎蛋白训练的模型的准确率为 45.4%。使用接收器操作特征曲线对训练好的模型进行检查后发现,在 NC 和 eHCC(1.000 对 0.810)以及肝硬化和 eHCC(0.926 对 0.556)之间的比较中,AFP 和代谢物组合模型的曲线下面积值更大。这一结果在独立验证队列中也是一致的:本研究强调了循环代谢物标记物在 eHCC 诊断中的作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Metabolic phenotyping combined with transcriptomics metadata fortifies the diagnosis of early-stage Hepatocellular carcinoma.

Introduction: The low sensitivity of alpha-fetoprotein (AFP) renders it unsuitable as a stand-alone marker for early hepatocellular carcinoma (eHCC) surveillance. Therefore, additional blood-based biomarkers with enhanced sensitivities are required.

Objectives: In light of the metabolic changes that are distinctive to eHCC development, the current study presents a panel of serum metabolites that may serve as noninvasive diagnostic indicators for patients with eHCC.

Methods: Serum samples obtained from normal control (NC), cirrhosis, and eHCC patients were analyzed by four different metabolomic platforms. A meta-analysis of very early-stage HCC transcriptomic datasets retrieved from public sources supports the integrated interpretation with metabolic changes.

Results: A total of 94 metabolites were significantly correlated with a progressive disease status. Integrated analysis of the significant metabolites and differentially expressed genes from meta-analysis emphasized metabolic pathways including bile acid biosynthesis, phenylalanine and tyrosine metabolism, and butanoate metabolism. The 11 metabolites associated with these pathways were compiled into a metabolite panel for use as diagnostic signatures. With an accuracy of 81.8%, compared with 45.4% for a model trained solely on AFP, the model enhanced its ability to differentiate between the three groups by incorporating a metabolite panel and AFP. Upon examining the trained models using receiver operating characteristic curves, the AFP and metabolite panel combined model exhibited greater area under the curve values in comparisons between NC and eHCC (1.000 versus 0.810) and cirrhosis and eHCC (0.926 versus 0.556). The result was consistent in an independent validation cohort.

Conclusion: This study emphasizes the role of circulating metabolite markers in the diagnosis of eHCC.

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