动态基于边缘的生物标记物基于血液测试无创地预测针对个

StephenW

J Mol Cell Biol. 2019 Mar 29. pii: mjz025. doi: 10.1093/jmcb/mjz025. [Epub ahead of print]
Dynamic edge-based biomarker noninvasively predicts hepatocellular carcinoma with hepatitis B virus infection for individual patients based on blood testing.
Lu Y1, Fang Z2, Li M2,3, Qian C, Zeng T2, Lu L2, Chen Q1, Zhang H1, Zhou Q1, Sun Y4, Xue X4, Hu Y5, Chen L2,6,7,8, Su S1.
Author information

1
    Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai, China, 201203.
2
    Key Laboratory of Systems Biology, Center for Excellence in Molecular Cell Science, Institute of Biochemistry and Cell Biology, Shanghai Institute of Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China.
3
    Minhang Branch, Zhongshan Hospital, Fudan University/Institute of Fudan-Minhang Academic Health System, Minhang Hospital, Fudan University, Shanghai 201199, China.
4
    Qidong Liver Cancer Institute, Qidong People's Hospital, Qidong 226200, China.
5
    Institute of liver disease, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China.
6
    Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming 650223, China.
7
    School of Life Science and Technology, Shanghai Tech University, Shanghai 201210, China.
8
    Research Center for Brain Science and Brain-Inspired Intelligence, Shanghai 201210, China.

Abstract

Hepatitis B virus (HBV)-induced hepatocellular carcinoma (HCC) is a major cause of cancer-related deaths in Asia and Africa. Developing effective and noninvasive 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 (PBMCs) from both healthy donors and patients with chronic HBV infection in different states (i.e., HBV carrier (HBVC), chronic hepatitis B (CHB), cirrhosis and HCC), we identified a set of 19 candidate genes according to our algorithm of dynamic network biomarkers (DNBs). 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 (AFP)-negative HCC. Our study not only provides a novel edge-based biomarker for noninvasive and effective diagnosis of HBV-associated HCC to each individual patients 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. Trial registration: Identifier: NCT03189992. Registered on June 4, 2017. Retrospectively registered (www.clinicaltrials.gov).

© The Author(s) 2019. Published by Oxford University Press on behalf of Journal of Molecular Cell Biology, IBCB, SIBS, CAS.
KEYWORDS:

Diagnosis and prognosis; Dynamic network biomarker; Edge-based biomarker; Hepatitis B virus; Hepatocellular carcinoma

PMID:
    30925583
DOI:
    10.1093/jmcb/mjz025

StephenW

J Mol Cell Biol。 2019年3月29日.pii：mjz025。 doi：10.1093 / jmcb / mjz025。 [印刷前的电子版]
动态基于边缘的生物标记物基于血液测试无创地预测针对个体患者的具有乙型肝炎病毒感染的肝细胞癌。
Lu Y1，Fang Z2，Li M2,3，Qian C，Zeng T2，Lu L2，Chen Q1，Zhang H1，Zhou Q1，Sun Y4，Xue X4，Hu Y5，Chen L2,6,7,8，Su S1。
作者信息

1
    上海中医药大学跨学科中西医结合研究所，上海，201203。
2
    中国科学院上海生命科学研究院生物化学与细胞生物学研究所，分子细胞科学优秀中心系统生物学重点实验室，上海200031
3
    复旦大学附属中山医院闵行分院/复旦大学闵行医院复旦 - 闵行学术卫生系统研究所，上海201199
4
    启东市人民医院启东肝癌研究所，祁东226200
五
    上海中医药大学曙光医院肝病研究所，上海201203
6
    中国科学院动物进化与遗传研究中心，昆明650223
7
    上海理工大学生命科学与技术学院，上海201210
8
    脑科学与脑力智能研究中心，上海201210，中国。

抽象

乙型肝炎病毒（HBV）诱导的肝细胞癌（HCC）是亚洲和非洲癌症相关死亡的主要原因。为个体患者开发有效和无创的HCC生物标志物仍然是早期诊断和方便监测的紧迫任务。分析健康供者和不同状态慢性HBV感染患者（即HBV携带者（HBVC），慢性乙型肝炎（CHB），肝硬化和HCC）外周血单个核细胞（PBMCs）的转录组谱，我们确定了一组根据我们的动态网络生物标记物（DNB）算法，有19个候选基因。这些基因可以表征HCC进展期间的不同阶段，并将肝硬化确定为癌发生前的关键过渡阶段。候选基因的相互作用效应（即共表达）用于建立准确的预测模型：所谓的基于边缘的生物标志物。考虑到生物标志物在临床应用中的便利性和稳健性，我们进行了功能分析，在我们收集的队列的其他独立样本中验证了候选基因，最后选择COL5A1，HLA-DQB1，MMP2和CDK4构建边缘组作为预测模型。我们证明边缘组在HCC的准确性和特异性方面在诊断和预后方面都有很好的表现，特别是对于甲胎蛋白（AFP）阴性HCC患者。我们的研究不仅为每个患者提供了一种新的基于边缘的生物标志物，用于无创和有效诊断HBV相关HCC，而且还从网络和动力学的角度介绍了将各个分子的相互作用项整合用于临床诊断和预后的新方法。 。试验注册：标识符：NCT03189992。 2017年6月4日注册。回顾性注册（www.clinicaltrials.gov）。

©The Author（s）2019。由牛津大学出版社代表分子细胞生物学杂志，IBCB，SIBS，CAS出版。
关键词：

诊断和预后;动态网络生物标志物;基于边缘的生物标志物;乙型肝炎病毒;肝细胞癌

结论：
    30925583
DOI：
    10.1093 / jmcb / mjz025

StephenW

https://watermark.silverchair.co ... ejcHWqV5CKWa9jUQLco