Wang Lei, Zhao Hui, Wu Fan, Chen Jiale, Xu Hanjie, Gong Wanwan, Wen Sijia, Yang Mengmeng, Xia Jiazeng, Chen Yu, Chen Daozhen
Institute for Reproductive Health and Genetic Diseases, The Affiliated Wuxi Maternity and Child Health Care Hospital of Nanjing Medical University, Wuxi 214002, China.
Department of Hepatopancreatobiliary Surgery, The Affiliated Wuxi No.2 People's Hospital of Nanjing Medical University, Wuxi 214002, China.
Comput Struct Biotechnol J. 2025 Mar 18;27:1173-1186. doi: 10.1016/j.csbj.2025.03.030. eCollection 2025.
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.
胆管癌（CCA）起源于肿瘤周围的胆汁微环境，但从胆汁到胆管内癌（IntraCCA）组织的微生物易位仍知之甚少。先前关于从胆道良性疾病（BBD）到CCA的胆汁微生物群改变的研究结果并不一致，这凸显了进行跨研究分析的必要性。我们对五个队列（N = 266）进行了全面分析，包括我们新建立的16S rRNA基因谱分析（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IntraCCA组织之间细菌的一致性表明微生物群从胆汁迁移到肿瘤内组织。一个计算随机森林机器学习模型有效地将IntraCCA组织与CCA胆汁区分开来，确定红球菌和罗尔斯通氏菌具有诊断意义。该模型在区分CCA胆汁和BBD胆汁方面也表现出色，在外部验证中AUC值达到0.931。使用无监督层次聚类，我们根据队列中的微生物特征建立了胆汁类型。17个属的组合有效地将患者分为胆汁类型A和胆汁类型B。胆汁类型B能有力地将CCA与BBD区分开来，亚胆汁类型B1与晚期TNM分期和较差的预后相关。在这17个属中，由鞘氨醇单胞菌、葡萄球菌、马赛菌、芽孢杆菌、卟啉杆菌、劳森菌和气球菌组成的细菌簇1在胆汁类型B1中富集，预测CCA的AUC为0.96。葡萄球菌成为CCA诊断和分期的一个有前景的单属预测指标。总之，本研究描绘了一条从肠道经CCA胆汁到Intra-CCA组织的潜在微生物群转变途径，提出胆汁类型和葡萄球菌作为CCA预后的生物标志物。
