Yeo Injoon, Kim Gi-Ae, Kim Hyunsoo, Lee Ji Hyeon, Sohn Areum, Gwak Geum-Youn, Lee Jeong-Hoon, Lim Young-Suk, Kim Youngsoo
Interdisciplinary Program in Bioengineering College of Engineering Seoul National University Seoul Korea.
Department of Internal Medicine Kyung Hee University School of Medicine Seoul Korea.
Hepatol Commun. 2020 Mar 13;4(5):753-768. doi: 10.1002/hep4.1500. eCollection 2020 May.
There is an urgent need for new biomarkers that address the shortcomings of current screening methods which fail to detect a large proportion of cases with hepatocellular carcinoma (HCC) at early stage. To develop a robust, multiple-biomarker panel based on multiple reaction monitoring-mass spectrometry with high performance in detecting early-stage HCC within at-risk populations. In the discovery set, 150 samples were analyzed to identify candidate biomarkers. The resulting list of candidates was tested in the training set (713 samples) to establish a multimarker panel, which was evaluated in the validation set (305 samples). We identified 385 serum HCC biomarker candidates in the discovery set and developed a multimarker panel consisting of 28 peptides that best differentiated HCC from controls. The area under the receiver operating characteristic curve of multimarker panel was significantly higher than alpha-fetoprotein (AFP) in the training (0.976 vs. 0.804; < 0.001) and validation (0.898 vs. 0.778; < 0.001) sets. In the validation set, this multimarker panel, compared with AFP, showed significantly greater sensitivity (81.1% vs. 26.8%; < 0.001) and lower specificity (84.8% vs. 98.8%; < 0.001) in detecting HCC cases. Combining AFP with the multimarker panel did not significantly improve the area under the receiver operating characteristic curve compared with the panel alone in the training (0.981 vs. 0.976; = 0.37) and validation set (0.906 vs. 0.898; = 0.75). The multiple reaction monitoring-mass spectrometry multimarker panel consisting of 28 peptides discriminates HCC cases from at-risk controls with high performance and may have potential for clinical application in HCC surveillance.
当前的筛查方法存在缺陷，无法检测出很大一部分早期肝细胞癌（HCC）病例，因此迫切需要新的生物标志物。基于多反应监测质谱技术开发一个强大的多生物标志物组合，以在高危人群中高效检测早期HCC。在发现集中，分析了150个样本以鉴定候选生物标志物。在训练集（713个样本）中对所得的候选列表进行测试，以建立一个多标志物组合，并在验证集（305个样本）中对其进行评估。我们在发现集中鉴定出385种血清HCC生物标志物候选物，并开发了一个由28种肽组成的多标志物组合，该组合能最好地区分HCC与对照。在训练集（0.976对0.804；<0.001）和验证集（0.898对0.778；<0.001）中，多标志物组合的受试者工作特征曲线下面积显著高于甲胎蛋白（AFP）。在验证集中，与AFP相比，该多标志物组合在检测HCC病例时显示出显著更高的灵敏度（81.1%对26.8%；<0.001）和更低的特异性（84.8%对98.8%；<0.001）。与单独使用该组合相比，在训练集（0.981对0.976；=0.37）和验证集（0.906对0.898；=0.75）中，将AFP与多标志物组合相结合并未显著改善受试者工作特征曲线下面积。由28种肽组成的多反应监测质谱多标志物组合能够高效地区分HCC病例与高危对照，并且在HCC监测中可能具有临床应用潜力。
