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机器学习方法与生物信息学分析发现了乙型肝炎病毒相关肝细胞重塑和肝细胞癌的关键基因组特征。

Machine Learning Approach and Bioinformatics Analysis Discovered Key Genomic Signatures for Hepatitis B Virus-Associated Hepatocyte Remodeling and Hepatocellular Carcinoma.

作者信息

Adugna Adane, Amare Gashaw Azanaw, Jemal Mohammed

机构信息

Medical Laboratory Sciences, College of Health Sciences, Debre Markos University, Ethiopia.

Department of Biomedical Sciences, School of Medicine, Debre Markos University, Ethiopia.

出版信息

Cancer Inform. 2025 Apr 16;24:11769351251333847. doi: 10.1177/11769351251333847. eCollection 2025.

DOI:10.1177/11769351251333847
PMID:40291818
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12033511/
Abstract

Hepatitis B virus (HBV) causes liver cancer, which is the third most common cause of cancer-related death worldwide. Chronic inflammation via HBV in the host hepatocytes causes hepatocyte remodeling (hepatocyte transformation and immortalization) and hepatocellular carcinoma (HCC). Recognizing cancer stages accurately to optimize early screening and diagnosis is a primary concern in the outlook of HBV-induced hepatocyte remodeling and liver cancer. Genomic signatures play important roles in addressing this issue. Recently, machine learning (ML) models and bioinformatics analysis have become very important in discovering novel genomic signatures for the early diagnosis, treatment, and prognosis of HBV-induced hepatic cell remodeling and HCC. We discuss the recent literature on the ML approach and bioinformatics analysis revealed novel genomic signatures for diagnosing and forecasting HBV-associated hepatocyte remodeling and HCC. Various genomic signatures, including various microRNAs and their associated genes, long noncoding RNAs (lncRNAs), and small nucleolar RNAs (snoRNAs), have been discovered to be involved in the upregulation and downregulation of HBV-HCC. Moreover, these genetic biomarkers also affect different biological processes, such as proliferation, migration, circulation, assault, dissemination, antiapoptosis, mitogenesis, transformation, and angiogenesis in HBV-infected hepatocytes.

摘要

乙型肝炎病毒(HBV)可引发肝癌,肝癌是全球癌症相关死亡的第三大常见原因。宿主肝细胞中由HBV引起的慢性炎症会导致肝细胞重塑(肝细胞转化和永生化)以及肝细胞癌(HCC)。准确识别癌症阶段以优化早期筛查和诊断是HBV诱导的肝细胞重塑和肝癌研究中的首要关注点。基因组特征在解决这一问题中发挥着重要作用。近年来,机器学习(ML)模型和生物信息学分析在发现用于HBV诱导的肝细胞重塑和HCC早期诊断、治疗及预后的新型基因组特征方面变得非常重要。我们讨论了近期有关ML方法和生物信息学分析的文献,这些研究揭示了用于诊断和预测HBV相关肝细胞重塑和HCC的新型基因组特征。已发现多种基因组特征,包括各种微小RNA及其相关基因、长链非编码RNA(lncRNA)和小核仁RNA(snoRNA),参与了HBV-HCC的上调和下调过程。此外,这些遗传生物标志物还影响不同的生物学过程,如HBV感染肝细胞中的增殖、迁移、循环、侵袭、播散、抗凋亡、有丝分裂、转化和血管生成。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/84fc/12033511/f890d96e4781/10.1177_11769351251333847-fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/84fc/12033511/45e136b1b207/10.1177_11769351251333847-fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/84fc/12033511/48835a2dde2f/10.1177_11769351251333847-fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/84fc/12033511/f890d96e4781/10.1177_11769351251333847-fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/84fc/12033511/45e136b1b207/10.1177_11769351251333847-fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/84fc/12033511/48835a2dde2f/10.1177_11769351251333847-fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/84fc/12033511/f890d96e4781/10.1177_11769351251333847-fig3.jpg

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本文引用的文献

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Multiple machine learning algorithms, validation of external clinical cohort and assessments of model gain effects will better serve cancer research on bioinformatic models.多种机器学习算法、外部临床队列验证以及模型增益效应评估将更好地服务于生物信息学模型的癌症研究。
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Machine learning approach identifies inflammatory gene signature for predicting survival outcomes in hepatocellular carcinoma.机器学习方法识别出用于预测肝细胞癌生存结果的炎症基因特征。
Sci Rep. 2024 Dec 5;14(1):30328. doi: 10.1038/s41598-024-81395-x.
3
Unveiling novel prognostic biomarkers and therapeutic targets for HBV-associated hepatocellular carcinoma through integrated bioinformatic analysis.
通过综合生物信息学分析揭示乙型肝炎病毒相关性肝细胞癌的新型预后生物标志物和治疗靶点。
Medicine (Baltimore). 2024 Oct 25;103(43):e40134. doi: 10.1097/MD.0000000000040134.
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Serum laminin γ2 monomer as a predictive biomarker for hepatocellular carcinoma in patients with chronic hepatitis B virus infection: a retrospective cohort study.血清层粘连蛋白 γ2 单体作为慢性乙型肝炎病毒感染患者肝细胞癌的预测性生物标志物:一项回顾性队列研究。
Sci Rep. 2024 Oct 25;14(1):25395. doi: 10.1038/s41598-024-77068-4.
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ML-GAP: machine learning-enhanced genomic analysis pipeline using autoencoders and data augmentation.ML-GAP:使用自动编码器和数据增强的机器学习增强基因组分析管道。
Front Genet. 2024 Sep 25;15:1442759. doi: 10.3389/fgene.2024.1442759. eCollection 2024.
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as a potential prognostic biomarker in hepatocellular carcinoma linked to immune infiltration and ferroptosis.作为与免疫浸润和铁死亡相关的肝细胞癌潜在预后生物标志物。
Chin J Cancer Res. 2024 Aug 30;36(4):378-397. doi: 10.21147/j.issn.1000-9604.2024.04.03.
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A machine learning model to predict liver-related outcomes after the functional cure of chronic hepatitis B.一种用于预测慢性乙型肝炎功能性治愈后肝脏相关结局的机器学习模型。
J Hepatol. 2025 Feb;82(2):235-244. doi: 10.1016/j.jhep.2024.08.016. Epub 2024 Aug 31.
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