Oral microbiota as a biomarker for predicting the risk of malignancy in indeterminate pulmonary nodules: a prospective multicenter study

作者：Qiong Ma, Chunxia Huang, Jiawei He, Xiao Zeng, Yingming Qu, Hongxia Xiang, Zhong Yang, Lei Mao, Ruyi Zheng, Junjie Xiao, Yuling Jiang, Shi-Yan Tan, Ping Xiao, Xiang Zhuang, Liting You, Xi Fu, Yifeng Ren, Chuan Zheng, Fengming You · 发表于：International Journal of Surgery · 年份：2024 · DOI：10.1097/js9.0000000000002152 · 被引用次数：13 · 研究领域：Oral microbiology and periodontitis research、Gut microbiota and health、Salivary Gland Disorders and Functions

BACKGROUND: Determining the benign or malignant status of indeterminate pulmonary nodules (IPN) with intermediate malignancy risk is a significant clinical challenge. Oral microbiota-lung cancer (LC) interactions have qualified oral microbiota as a promising non-invasive predictive biomarker in IPN. MATERIALS AND METHODS: Prospectively collected saliva, throat swabs, and tongue coating samples from 1040 IPN patients and 70 healthy controls across three hospitals. Following up, the IPNs were diagnosed as benign (BPN) or malignant pulmonary nodules (MPN). Through 16S rRNA sequencing, bioinformatics analysis, fluorescence in situ hybridization (FISH), and seven machine learning algorithms (support vector machine, logistic regression, naïve Bayes, multi-layer perceptron, random forest, gradient-boosting decision tree, and LightGBM), we revealed the oral microbiota characteristics at different stages of HC-BPN-MPN, identified the sample types with the highest predictive potential, constructed and evaluated the optimal MPN prediction model for predictive efficacy, and determined microbial biomarkers. Additionally, based on the SHAP algorithm interpretation of the ML model's output, we have developed a visualized IPN risk prediction system on the web. RESULTS: Saliva, tongue coating, and throat swab microbiotas exhibit site-specific characteristics, with saliva microbiota being the optimal sample type for disease prediction. The saliva-LightGBM model demonstrated the best predictive...