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This study evaluates the relationship between HBV and liver cancer by detecting HBV sequences from RNA sequencing (RNA-seq) data and applying deep learning to estimate the likelihood of HBV-related oncogenic transformation. RNA-seq samples were analyzed using Pathseq and convolutional neural network (CNN) models. The CNN identified HBV sequence prevalence and reached an AUC of 0.998 for predicting cancerous transformation. Longer sequences improved recognition accuracy, and no viral synergism enhancing HBV pathogenicity was observed, supporting improved management and prognosis.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/advanced-prediction-of-hepatic-oncogenic-transformation-in-hbv-patients-via-rna-seq-data-analysis-and-deep-learning-techniques/331332/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/advanced-prediction-of-hepatic-oncogenic-transformation-in-hbv-patients-via-rna-seq-data-analysis-and-deep-learning-techniques/331332.png","ImageObject",300,407,{"name":92,"@type":93},"Miles","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-29","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":39},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the main goal of this study in HBV-related liver cancer research?","Question",{"text":112,"@type":113},"To assess the correlation between HBV infection and liver cancer development by detecting HBV sequences from RNA-seq data and using deep learning to estimate oncogenic transformation likelihood.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which analytical approach and model were used to analyze RNA-seq data?",{"text":117,"@type":113},"RNA-seq data were processed using Pathseq, and a convolutional neural network (CNN) was applied to analyze HBV sequence prevalence in liver cancer samples.",{"name":119,"@type":110,"acceptedAnswer":120},"How accurate was the CNN model in predicting HBV-related oncogenic transformations?",{"text":121,"@type":113},"The CNN achieved an Area Under the Curve (AUC) of 0.998 for predicting cancerous transformations.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},331332,1790698196,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":39,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145},13056703019404,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","Article  \nAdvanced Prediction of Hepatic Oncogenic Transformation in HBV Patients via RNA-Seq Data Analysis and Deep Learning Techniques  \nZhengtai Li , Lei Huang and Changyuan Yu *  \nCitation: Li, Z.; Huang, L.; Yu, C. Advanced Prediction of Hepatic Oncogenic Transformation in HBV Patients via RNA-Seq Data Analysis and Deep Learning Techniques. Int. J. Mol. Sci. 2024, 25, 9827. [https://](https://)[ ](https://)[doi.org/10.3390/ijms25189827](doi.org/10.3390/ijms25189827)  \nAcademic Editor: Naoyuki Kataoka  \nReceived: 29 July 2024  \nRevised: 9 September 2024  \nAccepted: 10 September 2024  \nPublished: 11 September 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \nCollege of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China; [lizhengtaii@126.com](lizhengtaii@126.com) (Z.L.)  \n* [Correspondence: yucy@mail.buct.edu.cn](Correspondence: yucy@mail.buct.edu.cn); Tel.: +86-189-1129-5516  \nAbstract: 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 sequencesand 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.  \nKeywords: hepatitis B virus; liver cancer; deep learning; RNA-seq  \n1. Introduction  \nLiver cancer remains a global health challenge, with the number of cases worldwide expected to exceed one million by 2025 . Infections with Hepatitis B virus (HBV) and Hepatitis C virus (HCV) are considered major risk factors for Hepatocellular Carcinoma (HCC) [1] . Research shows a strong link between Hepatitis B virus (HBV) infection and liver cancer. However, not all HBV carriers develop liver cancer. This discrepancy is particularly noticeable in areas where HBV is widespread [2,3] . Over half of global liver cancer cases are linked to viral infections, including HBV [4] . Annually, HBV and HCC contribute to more than 1 .3 million deaths [5] .  \nNumerous studies have explored the replication mechanisms of Hepatitis B virus (HBV), a double-stranded DNA virus. HBV replicates through a unique reverse transcription process involving an RNA intermediate known as pre-genomic RNA (pgRNA) [6,7] . This process, like that of retroviruses, enables the tracking of HBV activity by detecting RNA intermediates. HBV’s genome is notably compact, and its replication mechanism bears similarities to retroviruses [8] . Upon entering hepatocytes, HBV’s relaxed circular double-stranded DNA (rcDNA) is converted into stable covalently closed circular DNA (cccDN","cbCaihBvbi13LplV","https://ap.wps.com/l/cbCaihBvbi13LplV","pdf",2955337,14,"English","# Introduction\n## HBV, HCV and global liver cancer risk\n## HBV replication mechanisms and cccDNA biology\n## Deep learning and RNA-seq for cancer biomarker discovery","[{\"question\":\"What is the main goal of this study in HBV-related liver cancer research?\",\"answer\":\"To assess the correlation between HBV infection and liver cancer development by detecting HBV sequences from RNA-seq data and using deep learning to estimate oncogenic transformation likelihood.\"},{\"question\":\"Which analytical approach and model were used to analyze RNA-seq data?\",\"answer\":\"RNA-seq data were processed using Pathseq, and a convolutional neural network (CNN) was applied to analyze HBV sequence prevalence in liver cancer samples.\"},{\"question\":\"How accurate was the CNN model in predicting HBV-related oncogenic transformations?\",\"answer\":\"The CNN achieved an Area Under the Curve (AUC) of 0.998 for predicting cancerous transformations.\"}]","Advanced Prediction of Hepatic Oncogenic Transformation in HBV Patients via RNA-Seq Data Analysis and Deep Learning Techniques | PDF",1789992688,35]