Early diagnosis of hepatic cancer is a major public health challenge. While changes in serum N-glycans have been observed as patients progress from liver fibrosis/cirrhosis to hepatocellular carcinoma (HCC), the predictive performance of N-glycans is yet to be determined for HCC early diagnosis as well as differential diagnosis from liver fibrosis/cirrhosis. In a total sample of 247 patients with hepatitis B virus-related liver disease, we characterized and compared the serum N-glycans in very early/early and intermediate/advanced stages of HCC and those with liver fibrosis/cirrhosis. Additionally, we performed a retrospective timeline analysis of the serum N-glycans 6 and 12 months before a diagnosis of the very early/early stage of HCC (EHCC). A predictive model was built, named hereafter as Glycomics-EHCC, incorporating the glycan peaks (GPs) 1, 2, and 4. The model showed a larger area under the receiver operating characteristic curve compared with a traditional model with the α-fetoprotein (0.936 vs. 0.731, respectively). The Glycomics-EHCC model had a sensitivity of 84.6% and specificity of 85.0% at a cutoff value of −0.39 to distinguish EHCC from liver fibrosis/cirrhosis. Moreover, the Glycomics-EHCC model was able to forecast a future EHCC diagnosis with a sensitivity and specificity over 90% and 85%, respectively, using the serum N-glycan biosignatures 6 or 12 months earlier when the patients were suffering from liver fibrosis/cirrhosis before being diagnosed with EHCC. This serum glycomic biosignature model warrants further clinical studies in independent population samples and offers promise to forecast EHCC and its differential diagnosis from liver fibrosis/cirrhosis.

中文翻译：

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使用N-糖蛋白生物特征的早期乙型肝炎病毒相关肝癌临床诊断的预测模型。

肝癌的早期诊断是主要的公共卫生挑战。随着患者从肝纤维化/肝硬化发展为肝细胞癌（HCC）的过程中已观察到血清N聚糖的变化，但对于HCC的早期诊断以及肝纤维化/肝硬化。在总共247名与乙型肝炎病毒相关的肝病患者中的样本中，我们对HCC的早期/早期，中期/晚期以及肝纤维化/肝硬化患者的血清N-聚糖进行了表征和比较。此外，我们在诊断HCC的早期/早期（EHCC）之前的6和12个月对血清N-聚糖进行了回顾性时间表分析。建立了一个预测模型，以下简称为Glycomics-EHCC，其中包含了聚糖峰（GPs）1，参见图2和4。与传统的带有甲胎蛋白的模型相比，该模型在接收器工作特性曲线下显示出更大的面积（分别为0.936和0.731）。Glycomics-EHCC模型在临界值为-0.39时具有84.6％的敏感性和85.0％的特异性，可将EHCC与肝纤维化/肝硬化区分开。此外，Glycomics-EHCC模型能够使用6或12个月前患者患有肝纤维化时的血清N-聚糖生物特征，预测未来EHCC诊断的敏感性和特异性分别超过90％和85％。被诊断为EHCC之前肝硬化。该血清糖化生物特征模型值得在独立人群样本中进行进一步的临床研究，并有望预测EHCC及其与肝纤维化/肝硬化的鉴别诊断。与具有甲胎蛋白的传统模型相比，该模型在接收器工作特性曲线下显示出更大的面积（分别为0.936和0.731）。Glycomics-EHCC模型在临界值为-0.39时具有84.6％的敏感性和85.0％的特异性，可将EHCC与肝纤维化/肝硬化区分开。此外，Glycomics-EHCC模型能够使用患者在6或12个月前患肝纤维化时的血清N-聚糖生物特征来预测将来的EHCC诊断，其敏感性和特异性分别超过90％和85％。被诊断为EHCC之前肝硬化。该血清糖化生物特征模型值得在独立人群样本中进行进一步的临床研究，并有望预测EHCC及其与肝纤维化/肝硬化的鉴别诊断。与具有甲胎蛋白的传统模型相比，该模型在接收器工作特性曲线下显示出更大的面积（分别为0.936和0.731）。Glycomics-EHCC模型在临界值为-0.39时具有84.6％的敏感性和85.0％的特异性，可将EHCC与肝纤维化/肝硬化区分开。此外，Glycomics-EHCC模型能够使用患者在6或12个月前患肝纤维化时的血清N-聚糖生物特征来预测将来的EHCC诊断，其敏感性和特异性分别超过90％和85％。被诊断为EHCC之前肝硬化。该血清糖化生物特征模型值得在独立人群样本中进行进一步的临床研究，并有望预测EHCC及其与肝纤维化/肝硬化的鉴别诊断。