Gut Microbiota as Mediator and Moderator Between Hepatitis B Virus and Hepatocellular Carcinoma: A Prospective Study

IF 2.9 2区医学 Q2 ONCOLOGY

Cancer Medicine Pub Date : 2024-12-19 DOI:10.1002/cam4.70454

Bingren Hu, Yi Yang, Jiangqiao Yao, Ganglian Lin, Qikuan He, Zhiyuan Bo, Zhewei Zhang, Anlvna Li, Yi Wang, Gang Chen, Yunfeng Shan

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引用次数: 0

Abstract

Background

The impact of gut microbiome on hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) is unclear. We aimed to evaluate the potential correlation between gut microbiome and HBV-related HCC and introduced novel machine learning (ML) signatures based on gut microbe to predict the risk of HCC.

Materials and Methods

A total of 640 patients with chronic liver diseases or HCC were prospectively recruited between 2019 and 2022. Fecal samples were collected and subjected to 16S rRNA gene sequencing. Univariate and multivariate logistic regression was applied to identify risk characteristics. Several ML methods were employed to construct gut microbe-based models and the predictive performance was evaluated.

Results

A total of 571 patients were involved in the study, including 374 patients with HCC and 197 patients with chronic liver diseases. After the propensity score matching method, 147 pairs of participants were enrolled in the analysis. Bacteroidia and Bacteroidales were demonstrated to exert mediating effects between HBV and HCC, and the moderating effects varied across Bacilli, Lactobacillales, Erysipelotrichaceae, Actinomyces, and Roseburia. HBV, alpha-fetoprotein, alanine transaminase, triglyceride, and Child-Pugh were identified as independent risk factors for HCC occurrence. Seven ML-based HBV-gut microbe models were established to predict HCC, with AUCs ranging from 0.821 to 0.898 in the training set and 0.813–0.885 in the validation set. Furthermore, the merged clinical-HBV-gut microbe models exhibited a comparable performance to HBV-gut microbe models.

Conclusions

Gut microbes are important factors between HBV and HCC through its potential mediating and moderating effects, which can be used as valuable biomarkers for the pathogenesis of HBV-related HCC.

Abstract Image

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背景：肠道微生物组对乙型肝炎病毒（HBV）相关肝细胞癌（HCC）的影响尚不清楚。我们旨在评估肠道微生物组与 HBV 相关 HCC 之间的潜在相关性，并引入基于肠道微生物的新型机器学习（ML）特征来预测 HCC 风险：在2019年至2022年期间，共前瞻性地招募了640名慢性肝病或HCC患者。收集粪便样本并进行 16S rRNA 基因测序。应用单变量和多变量逻辑回归确定风险特征。采用多种 ML 方法构建基于肠道微生物的模型，并评估其预测性能：共有 571 名患者参与了研究，其中包括 374 名 HCC 患者和 197 名慢性肝病患者。经过倾向得分匹配法，147对参与者被纳入分析。研究结果表明，类杆菌属和类杆菌科在 HBV 和 HCC 之间具有中介效应，而在芽孢杆菌属、乳酸杆菌属、赤霉菌科、放线菌属和玫瑰糠疹菌属中，中介效应各不相同。HBV、甲胎蛋白、丙氨酸转氨酶、甘油三酯和 Child-Pugh 被确定为 HCC 发生的独立风险因素。建立了七个基于 ML 的 HBV-肠道微生物模型来预测 HCC，训练集的 AUC 为 0.821 至 0.898，验证集的 AUC 为 0.813 至 0.885。此外，合并的临床-HBV-肠道微生物模型与HBV-肠道微生物模型表现出相当的性能：结论：肠道微生物是 HBV 与 HCC 之间的重要因素，具有潜在的中介和调节作用，可用作 HBV 相关 HCC 发病机制的重要生物标志物。

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来源期刊

Cancer Medicine ONCOLOGY-

CiteScore

5.50

自引率

2.50%

发文量

907

审稿时长

19 weeks

期刊介绍： Cancer Medicine is a peer-reviewed, open access, interdisciplinary journal providing rapid publication of research from global biomedical researchers across the cancer sciences. The journal will consider submissions from all oncologic specialties, including, but not limited to, the following areas: Clinical Cancer Research Translational research ∙ clinical trials ∙ chemotherapy ∙ radiation therapy ∙ surgical therapy ∙ clinical observations ∙ clinical guidelines ∙ genetic consultation ∙ ethical considerations Cancer Biology: Molecular biology ∙ cellular biology ∙ molecular genetics ∙ genomics ∙ immunology ∙ epigenetics ∙ metabolic studies ∙ proteomics ∙ cytopathology ∙ carcinogenesis ∙ drug discovery and delivery. Cancer Prevention: Behavioral science ∙ psychosocial studies ∙ screening ∙ nutrition ∙ epidemiology and prevention ∙ community outreach. Bioinformatics: Gene expressions profiles ∙ gene regulation networks ∙ genome bioinformatics ∙ pathwayanalysis ∙ prognostic biomarkers. Cancer Medicine publishes original research articles, systematic reviews, meta-analyses, and research methods papers, along with invited editorials and commentaries. Original research papers must report well-conducted research with conclusions supported by the data presented in the paper.