Dynamic edge-based biomarker non-invasively predicts hepatocellular carcinoma with hepatitis B virus infection for individual patients based on blood testing
Dynamic edge-based biomarker non-invasively predicts hepatocellular carcinoma with hepatitis B virus infection for individual patients based on blood testing
复制标题
基于动态边缘的生物标志物基于血液检测，无创地预测个体患者患有乙型肝炎病毒感染的肝细胞癌。
DOI：
10.1093/jmcb/mjz025
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发表时间：
2019-08-01
影响因子：
5.5
通讯作者：
Su, Shibing
中科院分区：
文献类型：
--
作者：
Lu, Yiyu;Fang, Zhaoyuan;Su, Shibing
在亚洲和非洲，由B型肝炎病毒（HBV）引起的肝细胞癌（HCC）是癌症相关死亡的主要原因。为个体患者开发有效和非侵入性的HCC生物标志物仍然是早期诊断和方便监测的迫切任务。我们分析了来自健康供体和不同状态（即HBV携带者、慢性乙型肝炎B、肝硬化和HCC）的慢性HBV感染患者的外周血单个核细胞的转录组学谱，根据我们的动态网络生物标志物算法确定了一组19个候选基因。这些基因可以表征HCC进展过程中的不同阶段，并将肝硬化确定为癌变前的关键过渡阶段。候选基因的相互作用效应（即共表达）被用来建立一个准确的预测模型：所谓的基于边缘的生物标志物。考虑到生物标志物在临床应用中的方便性和鲁棒性，我们进行了功能分析，并在我们收集的队列的其他独立样本中验证了候选基因，最终选择COL 5A 1，HLA-DQB 1，MMP 2和CDK 4构建边缘面板作为预测模型。我们证明，边缘面板在肝癌的诊断和预后方面具有很高的准确性和特异性，特别是对于甲胎蛋白阴性肝癌患者。我们的研究不仅为每个个体患者提供了一种新的基于边缘的生物标志物，用于HBV相关HCC的非侵入性和有效诊断，而且还从网络和动力学角度引入了一种整合个体分子相互作用项用于临床诊断和预后的新方法。
Hepatitis B virus (HBV)-induced hepatocellular carcinoma (HCC) is a major cause of cancer-related deaths in Asia and Africa. Developing effective and non-invasive biomarkers of HCC for individual patients remains an urgent task for early diagnosis and convenient monitoring. Analyzing the transcriptomic profiles of peripheral blood mononuclear cells from both healthy donors and patients with chronic HBV infection in different states (i.e. HBV carrier, chronic hepatitis B, cirrhosis, and HCC), we identified a set of 19 candidate genes according to our algorithm of dynamic network biomarkers. These genes can both characterize different stages during HCC progression and identify cirrhosis as the critical transition stage before carcinogenesis. The interaction effects (i.e. co-expressions) of candidate genes were used to build an accurate prediction model: the so-called edge-based biomarker. Considering the convenience and robustness of biomarkers in clinical applications, we performed functional analysis, validated candidate genes in other independent samples of our collected cohort, and finally selected COL5A1, HLA-DQB1, MMP2, and CDK4 to build edge panel as prediction models. We demonstrated that the edge panel had great performance in both diagnosis and prognosis in terms of precision and specificity for HCC, especially for patients with alpha-fetoprotein-negative HCC. Our study not only provides a novel edge-based biomarker for non-invasive and effective diagnosis of HBV-associated HCC to each individual patient but also introduces a new way to integrate the interaction terms of individual molecules for clinical diagnosis and prognosis from the network and dynamics perspectives.