Bile-Liver phenotype: Exploring the microbiota landscape in bile and intratumor of cholangiocarcinoma.

IF 4.4 2区 生物学 Q2 BIOCHEMISTRY & MOLECULAR BIOLOGY
Computational and structural biotechnology journal Pub Date : 2025-03-18 eCollection Date: 2025-01-01 DOI:10.1016/j.csbj.2025.03.030
Lei Wang, Hui Zhao, Fan Wu, Jiale Chen, Hanjie Xu, Wanwan Gong, Sijia Wen, Mengmeng Yang, Jiazeng Xia, Yu Chen, Daozhen Chen
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引用次数: 0

Abstract

Cholangiocarcinoma (CCA) arises within the peritumoral bile microenvironment, yet microbial translocation from bile to intracholangiocarcinoma (IntraCCA) tissues remains poorly understood. Previous studies on bile microbiota alterations from biliary benign disease (BBD) to CCA have yielded inconsistent results, highlighting the need for cross-study analysis. We presented a comprehensive analysis of five cohorts (N = 266), including our newly established 16S rRNA gene profiling (n = 42), to elucidate these microbiota transitions. The concordance of bacteria between CCA bile and intraCCA tissue, represented by Enterococcus and Staphylococcus, suggested microbiota migration from bile to intratumoral tissues. A computational random forest machine learning model effectively distinguished intraCCA tissue from CCA bile, identifying Rhodococcus and Ralstonia as diagnostically significant. The model also excelled in differentiating CCA bile from BBD bile, achieving an AUC value of 0.931 in external validation. Using unsupervised hierarchical clustering, we established Biletypes based on microbial signatures in our cohort. A combination of 17 genera effectively stratified patients into Biletype A and Biletype B. Biletype B robustly discerned CCA from BBD, with Sub-Biletype B1 correlating with advanced TNM stage and poorer prognosis. Among the 17 genera, bacterial Cluster 1, composed of Sphingomonas, Staphylococcus, Massilia, Paenibacillus, Porphyrobacter, Lawsonella, and Aerococcus, was enriched in Biletype B1 and predicted CCA with an AUC of 0.96. Staphylococcus emerged as a promising single-genus predictor for CCA diagnosis and staging. In conclusion, this study delineates a potential microbiota transition pathway from the gut through CCA bile to intra-CCA tissue, proposing Biletypes and Staphylococcus as biomarkers for CCA prognosis.

胆肝表型:探索胆管癌肿瘤内胆汁和肿瘤内的微生物群景观。
胆管癌(CCA)发生在肿瘤周围的胆汁微环境中,然而从胆汁到胆管内癌(IntraCCA)组织的微生物易位仍然知之甚少。先前关于胆道良性疾病(BBD)到CCA的胆汁微生物群改变的研究得出了不一致的结果,强调了交叉研究分析的必要性。我们对五个队列(N = 266)进行了综合分析,包括我们新建立的16S rRNA基因谱(N = 42),以阐明这些微生物群的转变。以肠球菌和葡萄球菌为代表的CCA胆汁和acca内组织之间的细菌一致性表明微生物群从胆汁迁移到肿瘤内组织。计算随机森林机器学习模型有效地区分了acca内组织和CCA胆汁,识别出红球菌和Ralstonia具有诊断意义。该模型在鉴别CCA胆汁和BBD胆汁方面也表现出色,外部验证的AUC值为0.931。使用无监督分层聚类,我们在我们的队列中建立了基于微生物特征的Biletypes。17个属的组合有效地将患者分为A型和B型。B型可以区分CCA和BBD,而B1亚型与TNM晚期和预后较差相关。在17个属中,由鞘单胞菌、葡萄球菌、Massilia、Paenibacillus、Porphyrobacter、Lawsonella和Aerococcus组成的细菌簇1富集Biletype B1,预测CCA的AUC为0.96。葡萄球菌作为一种有希望的单属预测因子出现在CCA的诊断和分期中。总之,本研究描述了从肠道通过CCA胆汁到CCA内组织的潜在微生物群转换途径,提出了Biletypes和葡萄球菌作为CCA预后的生物标志物。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Computational and structural biotechnology journal
Computational and structural biotechnology journal Biochemistry, Genetics and Molecular Biology-Biophysics
CiteScore
9.30
自引率
3.30%
发文量
540
审稿时长
6 weeks
期刊介绍: Computational and Structural Biotechnology Journal (CSBJ) is an online gold open access journal publishing research articles and reviews after full peer review. All articles are published, without barriers to access, immediately upon acceptance. The journal places a strong emphasis on functional and mechanistic understanding of how molecular components in a biological process work together through the application of computational methods. Structural data may provide such insights, but they are not a pre-requisite for publication in the journal. Specific areas of interest include, but are not limited to: Structure and function of proteins, nucleic acids and other macromolecules Structure and function of multi-component complexes Protein folding, processing and degradation Enzymology Computational and structural studies of plant systems Microbial Informatics Genomics Proteomics Metabolomics Algorithms and Hypothesis in Bioinformatics Mathematical and Theoretical Biology Computational Chemistry and Drug Discovery Microscopy and Molecular Imaging Nanotechnology Systems and Synthetic Biology
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