Yang Yanping, Shen Jiacheng, Wei Sulan, Ye Maosong, Zhao Xing, Zhou Jian, Tong Lin, Hu Jie, Song Yuanlin, Wu Shengdi, Xu Nuo
Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, 200032, China.
Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital, Fudan University, 180 Fenglin Road, Shanghai, 200032, China.
BMC Microbiol. 2025 Aug 23;25(1):541. doi: 10.1186/s12866-025-04325-5.
OBJECTIVES: The exploration of how dysbiosis relates to lung masses is still nascent, with few studies focusing on the microbial characteristics across various sites. Therefore, we categorized the microbiota into feces and bronchoalveolar fluid (BALF) groups to compare microbial characteristics between benign and malignant masses, analyze their clinical correlations, and develop predictive models for lung cancer. METHODS: A total of 238 fecal samples and 34 BALF samples were collected from patients with benign and malignant masses and then analyzed by 16 SrRNA. We explored the distinct composition of the gut and lung microbiota and their associations with clinical features. The diagnostic models were constructed using microbial features identified through two approaches: random forest algorithm with five-fold cross-validation and comparative analysis of significantly differential taxa. The performance evaluation was subsequently conducted using receiver operating characteristic (ROC) analysis. RESULTS: There was no significant difference in α-and β-diversity between feces and BALF groups. The relative abundance of Lachnospiraceae_NK4A136_group (P = 0.003232) and Erysipelotrichaceae_UCG-003 (P = 0.01316) in feces group and Proteobacteria (P = 0.03654) in BALF group were significantly increased in lung cancer patients. We also found Bacteroides (P = 0.01458) was abundant in NSCLC than those of SCLC in feces group, while the BALF group was dominated by norank_c_Cyanobacteria (P = 0.03384). Smoking history appeared to be related to the distribution of microbiota, with enrichment of Parabacteroides (P = 0.02054) in feces and Prevotella_1 (P = 0.03286) in BALF. Furthermore, the patients with Sellimonas (P = 0.04148) in feces and Alloprevotella (P = 0.04283) in BALF seemed to have better response to chemotherapy combined with immunotherapy. For differentiating benign and malignant masses, the combination of Megasphaera and norank_p__Saccharibacteria in BALF demonstrated superior predictive performance, with an AUC reaching 0.8 (95% CI 0.59-1). CONCLUSION: The microbiota composition significantly differed between benign and malignant masses in both fecal and BALF groups, with minimal evidence supporting microbial migration between these two sites. Our findings suggest that BALF microbiota may serve as a more reliable biomarker for lung masses classification, offering valuable insights for early diagnosis and clinical decision-making.
目的:关于生态失调与肺肿块之间关系的探索仍处于初期阶段,很少有研究关注不同部位的微生物特征。因此,我们将微生物群分为粪便和支气管肺泡灌洗液(BALF)组,以比较良性和恶性肿块之间的微生物特征,分析它们与临床的相关性,并开发肺癌预测模型。 方法:从患有良性和恶性肿块的患者中总共收集了238份粪便样本和34份BALF样本,然后通过16SrRNA进行分析。我们探索了肠道和肺部微生物群的不同组成及其与临床特征的关联。使用通过两种方法确定的微生物特征构建诊断模型:采用五重交叉验证的随机森林算法和对显著差异分类群的比较分析。随后使用受试者工作特征(ROC)分析进行性能评估。 结果:粪便组和BALF组之间的α多样性和β多样性没有显著差异。肺癌患者粪便组中毛螺菌科_NK4A136_组(P = 0.003232)和丹毒丝菌科_UCG - 003(P = 0.01316)以及BALF组中变形菌门(P = 0.03654)的相对丰度显著增加。我们还发现,在粪便组中,非小细胞肺癌(NSCLC)患者的拟杆菌属(P = 0.01458)比小细胞肺癌(SCLC)患者丰富,而BALF组中以未分类_c_蓝细菌为主(P = 0.03384)。吸烟史似乎与微生物群的分布有关粪便中副拟杆菌属(P = 0.02054)和BALF中普雷沃菌属_1(P = 0.03286)富集。此外,粪便中含有Sellimonas(P = 0.04148)和BALF中含有Alloprevotella(P = 0.04283)的患者似乎对化疗联合免疫治疗反应更好。对于区分良性和恶性肿块,BALF中巨球型菌属和未分类_p__糖菌纲的组合表现出卓越的预测性能,曲线下面积(AUC)达到0.8(95%置信区间0.59 - 1)。 结论:粪便组和BALF组中良性和恶性肿块之间的微生物群组成存在显著差异,仅有极少证据支持这两个部位之间的微生物迁移。我们的研究结果表明,BALF微生物群可能作为肺肿块分类更可靠的生物标志物,为早期诊断和临床决策提供有价值的见解。
Sichuan Da Xue Xue Bao Yi Xue Ban. 2025-3-20
Microbiol Spectr. 2023-8-17
NPJ Biofilms Microbiomes. 2025-7-28
Nat Rev Microbiol. 2025-3
Gut. 2024-10-7
Chin Med J Pulm Crit Care Med. 2023-2-25
Curr Allergy Asthma Rep. 2024-8
Int J Chron Obstruct Pulmon Dis. 2024