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代谢表型分析与转录组学元数据相结合可加强早期肝细胞癌的诊断。

Metabolic phenotyping combined with transcriptomics metadata fortifies the diagnosis of early-stage Hepatocellular carcinoma.

作者信息

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

机构信息

Research Institute of Pharmaceutical Sciences, Seoul National University, Seoul 08826, Republic of Korea; College of Pharmacy, Chonnam National University, Gwangju 61186, Republic of Korea.

College of Pharmacy, Seoul National University, Seoul 08826, Republic of Korea.

出版信息

J Adv Res. 2025 Aug;74:153-163. doi: 10.1016/j.jare.2024.09.007. Epub 2024 Sep 6.

Abstract

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.

摘要

引言

甲胎蛋白(AFP)的低敏感性使其不适用于作为早期肝细胞癌(eHCC)监测的独立标志物。因此,需要额外的具有更高敏感性的血液生物标志物。

目的

鉴于eHCC发展过程中独特的代谢变化,本研究提出了一组血清代谢物,它们可能作为eHCC患者的非侵入性诊断指标。

方法

通过四种不同的代谢组学平台分析从正常对照(NC)、肝硬化和eHCC患者获得的血清样本。对从公共来源检索到的极早期HCC转录组数据集进行的荟萃分析支持了与代谢变化的综合解读。

结果

共有94种代谢物与疾病进展状态显著相关。对来自荟萃分析的显著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途径。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%,而仅基于AFP训练的模型准确率为45.4%,通过纳入代谢物面板和AFP,该模型增强了区分三组的能力。使用受试者工作特征曲线检查训练后的模型时,AFP与代谢物面板组合模型在NC与eHCC(1.000对0.810)以及肝硬化与eHCC(0.926对0.556)的比较中显示出更大的曲线下面积值。在独立验证队列中结果一致。

结论

本研究强调了循环代谢物标志物在eHCC诊断中的作用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7f31/12302345/2e1f472651b1/ga1.jpg

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