School of Life Sciences, Peking University, Beijing, China; State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, China.
State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing Institute of Lifeomics, Beijing, China.
J Proteomics. 2020 Aug 15;225:103780. doi: 10.1016/j.jprot.2020.103780. Epub 2020 Apr 13.
Hepatocellular carcinoma (HCC) ranks fourth in cancer mortality worldwide, and third in China. Hepatitis B virus (HBV) infection is a main risk factor for HCC in China, and the early diagnosis of HCC in high-risk population is very important. However, the commonly used diagnostic biomarker alpha-fetoprotein has limitations in clinical practice. In order to identify reliable and noninvasive HCC urinary biomarkers, a high-throughput proteomics streamline was applied in the analysis of urine samples from 74 HCC and 82 high-risk patients with chronic HBV infected liver diseases. Candidate diagnostic markers were screened by feature selection algorithm, and were combined with random forest or simple voting algorithms in the training dataset. Then the multiple feature models were validated in an independent test dataset. The selected features were further verified by Multiple Reaction Monitoring (MRM) in another independent dataset. By integrating 7 features screened in the discovery phase, random forest model achieved AUC of 0.92 and 0.87 in training and test datasets, respectively, while voting model performed better with AUC of 0.94 and 0.90, respectively. In the MRM dataset, the 7 features were targeted quantified, and voting model integrating the 7 features achieved AUC of 0.95. Our work highlights the potential of noninvasive urinary protein biomarkers in HCC diagnosis with high-risk population, which will be beneficial to HCC auxiliary diagnosis and HCC surveillance. SIGNIFICANCE: A high throughput urinary proteome analysis platform was committed into the discovery of noninvasive HCC biomarkers in high-risk patients with chronic HBV infected liver diseases. The combination of 7 urinary features achieved good performance in distinguishing HCC from high-risk population. The expression of the 7 features was validated by targeted MRM, and the integration of the features also worked well in the MRM dataset. This is the first time that urinary proteomic strategy was applied in discovering HCC biomarkers from high-risk population. This result will be helpful for HCC auxiliary diagnosis and surveillance in a noninvasive way.
肝细胞癌 (HCC) 在全球范围内的癌症死亡率中排名第四，在中国排名第三。乙型肝炎病毒 (HBV) 感染是中国 HCC 的主要危险因素，对高危人群的 HCC 进行早期诊断非常重要。然而，常用的诊断生物标志物甲胎蛋白在临床实践中存在局限性。为了鉴定可靠且非侵入性的 HCC 尿液生物标志物，我们应用高通量蛋白质组学分析流程分析了来自 74 例 HCC 和 82 例慢性 HBV 感染肝病高危患者的尿液样本。通过特征选择算法筛选候选诊断标志物，并在训练数据集内将其与随机森林或简单投票算法相结合。然后，在独立测试数据集内对多特征模型进行验证。选择的特征进一步通过另一个独立数据集的多重反应监测 (MRM) 进行验证。通过整合在发现阶段筛选出的 7 个特征，随机森林模型在训练和测试数据集内的 AUC 分别为 0.92 和 0.87，而投票模型的 AUC 分别为 0.94 和 0.90，性能更好。在 MRM 数据集内，靶向定量了 7 个特征，整合了 7 个特征的投票模型的 AUC 为 0.95。我们的工作强调了高危人群 HCC 诊断中非侵入性尿液蛋白生物标志物的潜力，这将有利于 HCC 的辅助诊断和 HCC 的监测。意义：我们致力于应用高通量尿液蛋白质组分析平台在慢性 HBV 感染肝病高危患者中发现非侵入性 HCC 生物标志物。7 个尿液特征的组合在区分 HCC 与高危人群方面表现出良好的性能。通过靶向 MRM 验证了 7 个特征的表达，并且这些特征的整合在 MRM 数据集内也表现良好。这是首次应用尿液蛋白质组学策略从高危人群中发现 HCC 生物标志物。该结果将有助于以非侵入性方式进行 HCC 的辅助诊断和监测。
