J Gen Virol。 2017年10月12日。doi：10.1099 / jgv.0.000942。 [提前印刷

StephenW

J Gen Virol. 2017 Oct 12. doi: 10.1099/jgv.0.000942. [Epub ahead of print]
Next-generation sequencing revealed divergence in deletions of the preS region in the HBV genome between different HBV-related liver diseases.Jia J1,2, Liang X3,4, Chen S2, Wang H5,2, Li H2, Fang M2, Bai X3, Wang Z6, Wang M2, Zhu S3,4, Sun F7,3, Gao C2.
Author information
12​Department of Laboratory Medicine, The 105th Hospital of PLA, Hefei 230031, PR China.21​Department of Laboratory Medicine, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, 200438, PR China.34​Centre for Computational Systems Biology, School of Mathematical Sciences, Fudan University, Shanghai, PR China.43​Shanghai Key Lab of Intelligent Information Processing and School of Computer Science, Fudan University, Shanghai, PR China.55​Department of Clinical Laboratory, The First Affiliated Hospital of Chinese PLA's General Hospital, Beijing 100048, PR China.66​Shanghai Institute of Technology, Shanghai 201418, PR China.77​Molecular and Computational Program Department of Biological Sciences, University of Southern California, LA 90089, USA.

AbstractIn order to investigate if deletion patterns of the preS region can predict liver disease advancement, the preS region of the hepatitis B virus (HBV) genome in 45 chronic hepatitis B (CHB) and 94 HBV-related hepatocellular carcinoma (HCC) patients was sequenced by next-generation sequencing (NGS) and the percentages of nucleotide deletion in the preS region were analysed. Hierarchical clustering and heatmaps based on deletion percentages of preS revealed different deletion patterns between CHB and HCC patients. Intergenotype comparison also indicated divergence in preS deletions between HBV genotype B and C. No significant difference was found in preS deletion patterns between sera and matched adjacent non-tumour tissues. Based on hierarchical clustering, HCC patients were classed into two groups with different preS deletion patterns and different clinical features. Finally, the support vector machine (SVM) model was trained on preS nucleotide deletion percentages and used to predict HCC versus CHB patients. The prediction performance was assessed with fivefold cross-validation and independent cohort validation. The median area under the curve (AUC) was 0.729 after repeating SVM 500 times with fivefold cross-validations. After parameter optimization, the SVM model was used to predict an independent cohort with 51 CHB patients and 72 HCC patients and the AUC was 0.727. In conclusion, the use of the NGS method revealed a prominent divergence in preS deletion patterns between disease groups and virus genotypes, but not between different tissue types. Quantitative NGS data combined with a machine learning method could be a powerful approach for prediction of the status of different diseases.


PMID:29022863DOI:10.1099/jgv.0.000942

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StephenW

J Gen Virol。 2017年10月12日。doi：10.1099 / jgv.0.000942。 [提前印刷]
下一代测序显示不同HBV相关肝脏疾病之间HBV基因组中preS区域缺失的差异。
贾杰1,2，梁X3,4，陈S2，王和5,2，李H2，方M2，白X3，王泽6，王M2，朱S3,4，孙F7,3，高C2。
作者信息

1
    2解放军第105医院实验医学系，合肥230031。
2
    1第二军医大学东方肝胆外科医院实验医学系，上海，200438，中国。
3
    4复旦大学数学科学系计算系统生物学中心，上海，中国。
4
    3上海复旦大学智能信息处理与计算机科学学院上海市重点实验室。
五
    5中国人民解放军总医院第一附属医院临床实验室，北京100048。
6
    6上海理工大学，上海201418，中国。
7
    7分子和计算程序生物科学系，南加州大学，LA 90089，USA。

抽象

为了研究preS区域的缺失模式是否能够预测肝脏疾病进展，将45例慢性乙型肝炎（CHB）和94例HBV相关性肝细胞癌（HCC）患者的乙型肝炎病毒（HBV）基因组的前期区域进行了测序通过下一代测序（NGS）和前S区核苷酸缺失的百分比进行了分析。基于preS的删除百分比的分层聚类和热图揭示了CHB和HCC患者之间的不同缺失模式。基因型比较也表明HBV基因型B和C之间preS缺失的差异。在血清和匹配的相邻非肿瘤组织之间的preS缺失模式中没有发现显着差异。基于分层聚类，将HCC患者分为两组，具有不同的preS缺失模式和不同临床特征。最后，支持向量机（SVM）模型训练前S核苷酸缺失百分比，用于预测HCC与CHB患者。预测性能通过五次交叉验证和独立队列验证进行评估。曲线上的中值面积（AUC）在重复SVM 500次后进行了五次交叉验证，为0.729。参数优化后，SVM模型用于预测51例CHB患者和72例HCC患者的独立队列，AUC为0.727。总之，使用NGS方法揭示了疾病组与病毒基因型之间preS缺失模式的显着差异，但不表现在不同组织类型之间。量化NGS数据结合机器学习方法可能是预测不同疾病状况的有力手段。

结论：
    29022863
DOI：
    10.1099 / jgv.0.000942