College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China.
Int J Mol Sci. 2024 Sep 11;25(18):9827. doi: 10.3390/ijms25189827.
Liver cancer, recognized as a significant global health issue, is increasingly correlated with Hepatitis B virus (HBV) infection, as evidenced by numerous scientific studies. This study aims to examine the correlation between HBV infection and the development of liver cancer, focusing on using RNA sequencing (RNA-seq) to detect HBV sequences and applying deep learning techniques to estimate the likelihood of oncogenic transformation in individuals with HBV. Our study utilized RNA-seq data and employed Pathseq software and sophisticated deep learning models, including a convolutional neural network (CNN), to analyze the prevalence of HBV sequences in the samples of patients with liver cancer. Our research successfully identified the prevalence of HBV sequences and demonstrated that the CNN model achieved an exceptional Area Under the Curve (AUC) of 0.998 in predicting cancerous transformations. We observed no viral synergism that enhanced the pathogenicity of HBV. A detailed analysis of sequences misclassified by the CNN model revealed that longer sequences were more conducive to accurate recognition. The findings from this study provide critical insights into the management and prognosis of patients infected with HBV, highlighting the potential of advanced analytical techniques in understanding the complex interactions between viral infections and cancer development.
肝癌作为一个全球性的健康问题，与乙型肝炎病毒（HBV）感染密切相关，这已被大量科学研究所证实。本研究旨在探讨 HBV 感染与肝癌发展之间的相关性，重点是使用 RNA 测序（RNA-seq）检测 HBV 序列，并应用深度学习技术估计 HBV 感染者发生致癌转化的可能性。我们的研究利用了 RNA-seq 数据，并采用了 Pathseq 软件和复杂的深度学习模型，包括卷积神经网络（CNN），来分析肝癌患者样本中 HBV 序列的流行情况。我们的研究成功地确定了 HBV 序列的流行情况，并表明 CNN 模型在预测癌变方面取得了卓越的曲线下面积（AUC）为 0.998。我们没有观察到增强 HBV 致病性的病毒协同作用。对 CNN 模型错误分类的序列进行详细分析表明，较长的序列更有利于准确识别。这项研究的结果为 HBV 感染患者的管理和预后提供了重要的见解，突出了先进分析技术在理解病毒感染与癌症发展之间复杂相互作用的潜力。
