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.

中文翻译：

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基于血液边缘的动态生物标记物可通过血液检测以无创方式预测个别患者的乙型肝炎病毒感染的肝细胞癌。

乙肝病毒（HBV）诱发的肝细胞癌（HCC）是亚洲和非洲与癌症相关的死亡的主要原因。为个体患者开发有效的，非侵入性的肝癌生物标志物仍然是早期诊断和方便监测的紧迫任务。分析来自健康供体和处于不同状态（即HBV携带者，慢性乙型肝炎，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谱，根据我们的算法，我们确定了一组19个候选基因动态网络生物标记。这些基因既可以表征HCC进程中的不同阶段，又可以将肝硬化确定为致癌之前的关键过渡阶段。交互作用（即 候选基因的共表达）用于建立准确的预测模型：所谓的基于边缘的生物标记。考虑到生物标志物在临床应用中的便利性和鲁棒性，我们进行了功能分析，在收集的队列的其他独立样本中验证了候选基因，最后选择了COL5A1，HLA-DQB1，MMP2和CDK4来构建边缘面板作为预测模型。我们证明边缘板在肝癌的准确性和特异性方面，在诊断和预后方面均具有出色的表现，尤其是对于甲胎蛋白阴性的肝癌患者。