Huang Chunxia, Ma Qiong, Zeng Xiao, He Jiawei, You Fengming, Fu Xi, Ren Yifeng
Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, 610072, Sichuan Province, China.
TCM Regulating Metabolic Diseases Key Laboratory of Sichuan Province, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, 610072, Sichuan Province, China.
Sci Rep. 2025 Mar 29;15(1):10848. doi: 10.1038/s41598-025-95692-6.
Microorganisms are one of the most promising biomarkers for cancer, and the relationship between microorganisms and lung cancer occurrence and development provides significant potential for pulmonary nodule (PN) diagnosis from a microbiological perspective. This study aimed to analyze the salivary microbiota features of patients with PN and assess the potential of the salivary microbiota as a non-invasive PN biomarker. We collected saliva smples from 153 patients with PN and 40 controls. Using 16 S rRNA gene sequencing, differences in α- and β-diversity and community composition between the group with PN and controls were analyzed. Subsequently, specific microbial variables were selected using six models were trained on the selected salivary microbial features. The models were evaluated using metrics, such as the area under the receiver operating characteristic curve (AUC), to identify the best-performing model. Furthermore, the Bayesian optimization algorithm was used to optimize this best-performing model. Finally, the SHapley Additive exPlanations (SHAP) interpretability framework was used to interpret the output of the optimal model and identify potential PN biomarkers. Significant differences in α- and β-diversity were observed between the group with PN and controls. Although the predominant genera were consistent between the groups, significant disparities were observed in their relative abundances. By leveraging the random forest algorithm, ten characteristic microbial variables were identified and incorporated into six models, which effectively facilitated PN diagnosis. The XGBoost model demonstrated the best performance. Further optimization of the XGBoost model resulted in a Bayesian Optimization-based XGBoost (BOXGB) model. Based on the BOXGB model, an online saliva microbiota-based PN prediction platform was developed. Lastly, SHAP analysis suggested Defluviitaleaceae_UCG-011, Aggregatibacter, Oribacterium, Bacillus, and Prevotalla are promising non-invasive PN biomarkers. This study proved salivary microbiota as a non-invasive PN biomarker, expanding the clinical diagnostic approaches for PN.
微生物是癌症最有前景的生物标志物之一，微生物与肺癌发生发展之间的关系从微生物学角度为肺结节（PN）诊断提供了巨大潜力。本研究旨在分析PN患者的唾液微生物群特征，并评估唾液微生物群作为非侵入性PN生物标志物的潜力。我们收集了153例PN患者和40例对照的唾液样本。使用16S rRNA基因测序，分析了PN组与对照组之间α-和β-多样性以及群落组成的差异。随后，使用六种模型选择特定的微生物变量，并在所选择的唾液微生物特征上进行训练。使用受试者工作特征曲线下面积（AUC）等指标对模型进行评估，以确定表现最佳的模型。此外，使用贝叶斯优化算法对这个表现最佳的模型进行优化。最后，使用SHapley加性解释（SHAP）可解释性框架来解释最优模型的输出并确定潜在的PN生物标志物。在PN组与对照组之间观察到α-和β-多样性的显著差异。尽管两组之间的优势菌属一致，但在它们的相对丰度上观察到显著差异。通过利用随机森林算法，确定了10个特征微生物变量并将其纳入六个模型，这有效地促进了PN诊断。XGBoost模型表现最佳。对XGBoost模型的进一步优化产生了基于贝叶斯优化的XGBoost（BOXGB）模型。基于BOXGB模型，开发了一个基于唾液微生物群的在线PN预测平台。最后，SHAP分析表明Defluviitaleaceae_UCG - 011、聚集杆菌属、口腔杆菌属、芽孢杆菌属和普雷沃菌属是有前景的非侵入性PN生物标志物。本研究证明唾液微生物群可作为非侵入性PN生物标志物，拓展了PN的临床诊断方法。
