Determine microbial markers for benign and malignant diagnosis of pulmonary nodules in blood stasis constitution based on saliva 16S rRNA gene sequencing and machine learning algorithms

<p indent="0mm">Current diagnostic methods for pulmonary nodules are not ideal in clinical practice, especially for distinguishing benign from malignant nodules in high-risk populations. Therefore, there is an urgent need for precise and non-invasive diagnostic strategies. The oral microbiota, as an emerging biomarker, has received widespread attention, and advanced machine-learning algorithms provide new ideas for the development of non-invasive microbial markers for early identification of pulmonary nodules. In this study, saliva samples and information on traditional Chinese medicine constitution were collected from patients with pulmonary nodules. Based on histopathological detection of nodule tissue and the results of a blood stasis constitution scoring table, patients with benign (<italic>n</italic>=136) and malignant (<italic>n</italic>=73) pulmonary nodules of blood stasis constitution were selected as the study subjects. Using 16S rRNA sequencing technology and bioinformatics analysis, we revealed the oral microbial diversity, microbiota health index, microbiota dysbiosis index, species composition, differential bacterial genera, and the correlation between microorganisms and clinical characteristics of the subjects in patients with benign and malignant pulmonary nodules of blood stasis constitution. Based on the amplicon sequence variant of the salivary microbiota, seven machine learning techniques [logistic regression, support vector machine, multi-layer perceptron, naïve bayes, random forest, gradient boosting decision trees, light gradient boosting machine (lightGBM)] were used to construct a diagnostic model for pulmonary nodules of blood stasis constitution, and the predictive performance metrics were evaluated to determine the optimal performance model. The feature importance ranking based on the optimal model and the SHAP method was used to explain the contribution of features and screen for potential diagnostic biomarkers of pulmonary nodules in blood stasis constitution. Finally, based on PICRUSt2 functional prediction analysis of the microbiota, the potential mechanism of the oral microbiota in the malignant progression of pulmonary nodules was explored. The study suggested that salivary microbiota diversity of benign and malignant pulmonary nodules in blood stasis constitution was significantly different, with significantly reduced α and β diversity values in malignant pulmonary nodules (<italic>P</italic>​&lt;0.05). Simultaneously, compared to benign pulmonary nodules in blood stasis constitution, the microbiota health index was significantly reduced, and the microbiota dysbiosis index was significantly increased in patients with malignant pulmonary nodules (<italic>P</italic>​&lt;0.05). In terms of species composition, we found rich microbiota composition and high levels of differential microbiota in the saliva of patients with benign and malignant pulmonary nodules of blood stasis constitution, respectively. Additionally, Distance-based Redundancy Analysis showed a correlation between salivary microbiota composition and clinical characteristics in benign and malignant pulmonary nodules of blood stasis constitution. The seven machine learning models we developed were able to distinguish the malignancy probability of pulmonary nodules in blood stasis constitution. When validated on the model test dataset, the average performance AUC was 0.693–0.893, with LightGBM achieving high accuracy [area under the curve (AUC)=0.893, 95% CI: 0.866–0.925]. Based on the importance ranking of optimal model features and the SHAP method to explain feature contributions, potential biomarkers for predicting the risk of malignancy in pulmonary nodules of blood stasis constitution were identified as <italic>Granulicatella</italic>, <italic>Fusobacterium</italic>, <italic>Delftia</italic>, <italic>Streptococcus</italic>, <italic>Alloprevotella</italic>, and <italic>Veillonella</italic>. Finally, based on PICRUSt2 functional prediction analysis of the microbiota, we found interesting metabolic reprogramming that may exist in the oral microbiota during the malignant development of pulmonary nodules. Overall, machine learning models trained based on salivary microbiota features have good predictive performance. The oral microbiota can serve as a potential diagnostic biomarker for the differential diagnosis of pulmonary nodules in blood stasis constitution. This discovery opens new avenues for the development of non-invasive, highly accurate identification tools for high-risk populations with pulmonary nodules and deserves further research and clinical validation.