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International journal of surgery (London, England)
Feb 01, 2025;111 (2): 2055-2071.

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

Qiong Ma, Chun-Xia Huang, Jia-Wei He, Xiao Zeng, Yu-Li Qu, Hong-Xia Xiang, Yang Zhong, Mao Lei, Ru-Yi Zheng, Jun-Jie Xiao, Yu-Ling Jiang, Shi-Yan Tan, Ping Xiao, Xiang Zhuang, Li-Ting You, Xi Fu, Yi-Feng Ren, Chuan Zheng, Feng-Ming You
Author Information
  1. Qiong Ma: Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, China.
  2. Chun-Xia Huang: Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, China.
  3. Jia-Wei He: Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, China.
  4. Xiao Zeng: Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, China.
  5. Yu-Li Qu: College of Artificial Intelligence, Xi'an Jiaotong University, Xian, Shanxi Province, China.
  6. Hong-Xia Xiang: Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, China.
  7. Yang Zhong: Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, China.
  8. Mao Lei: Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, China.
  9. Ru-Yi Zheng: Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, China.
  10. Jun-Jie Xiao: Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, China.
  11. Yu-Ling Jiang: Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, China.
  12. Shi-Yan Tan: Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, China.
  13. Ping Xiao: Department of Thoracic Surgery, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan Province, China.
  14. Xiang Zhuang: Department of Thoracic Surgery, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of China, Chengdu, Sichuan Province, China.
  15. Li-Ting You: Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, China.
  16. Xi Fu: Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, China.
  17. Yi-Feng Ren: Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, China.
  18. Chuan Zheng: Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, China.
  19. Feng-Ming You: Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan Province, China.
PMID: 39728732 DOI: 10.1097/JS9.0000000000002152

Abstract

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, na��ve 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 performance (AUC = 0.887, 95%CI: 0.865-0.918), and identified Actinomyces, Rothia, Streptococcus, Prevotella, Porphyromonas , and Veillonella as biomarkers for predicting MPN. FISH was used to confirm the presence of a microbiota within tumors, and external data from a LC cohort, along with three non-IPN disease cohorts, were employed to validate the specificity of the microbial biomarkers. Notably, coabundance analysis of the ecological network revealed that microbial biomarkers exhibit richer interspecies connections within the MPN, which may contribute to the pathogenesis of MPN.
CONCLUSION: This study presents a new predictive strategy for the clinic to determine MPNs from BPNs, which aids in the surgical decision-making for IPN.

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MeSH Term

Humans
Prospective Studies
Microbiota
Male
Female
Middle Aged
Lung Neoplasms
Aged
Saliva
Adult
Biomarkers
Multiple Pulmonary Nodules
Mouth
Biomarkers, Tumor
Risk Assessment
Machine Learning

Chemicals

Biomarkers
Biomarkers, Tumor
Journal Article Multicenter Study

Word Cloud

Created with Highcharts 10.0.0IPNmicrobiotapredictiveMPNbiomarkerspulmonaryriskpredictionmicrobialbenignmalignantindeterminatenodulesmalignancyOralLCoralbiomarkersalivathroattonguecoatingthreeanalysisFISHmachinerevealedcharacteristicsidentifiedsampleoptimalmodelexhibitdiseasepredictingwithinstudyBACKGROUND:Determiningstatusintermediatesignificantclinicalchallengemicrobiota-lungcancerinteractionsqualifiedpromisingnon-invasiveMATERIALSANDMETHODS:Prospectivelycollectedswabssamples1040patients70healthycontrolsacrosshospitalsFollowingIPNsdiagnosedBPN16SrRNAsequencingbioinformaticsfluorescencesituhybridizationsevenlearningalgorithmssupportvectorlogisticregressionna��veBayesmulti-layerperceptronrandomforestgradient-boostingdecisiontreeLightGBMdifferentstagesHC-BPN-MPNtypeshighestpotentialconstructedevaluatedefficacydeterminedAdditionallybasedSHAPalgorithminterpretationMLmodel'soutputdevelopedvisualizedsystemwebRESULTS:Salivaswabmicrobiotassite-specifictypesaliva-LightGBMdemonstratedbestperformanceAUC = 088795%CI:0865-0918ActinomycesRothiaStreptococcusPrevotellaPorphyromonasVeillonellausedconfirmpresencetumorsexternaldatacohortalongnon-IPNcohortsemployedvalidatespecificityNotablycoabundanceecologicalnetworkricherinterspeciesconnectionsmaycontributepathogenesisCONCLUSION:presentsnewstrategyclinicdetermineMPNsBPNsaidssurgicaldecision-makingnodules:prospectivemulticenter

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