Diagnostic potential of salivary microbiota in persistent pulmonary nodules: identifying biomarkers and functional pathways using 16S rRNA sequencing and machine learning
Abstract Background The aim of this study was to explore the microbial variations and biomarkers in the oral environment of patients with persistent pulmonary nodules (pPNs) and to reveal the potential biological functions of the salivary microbiota in pPNs. Materials and methods This study included...
Saved in:
| Main Authors: | , , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2024-11-01
|
| Series: | Journal of Translational Medicine |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12967-024-05802-7 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850064925511122944 |
|---|---|
| author | Xiao Zeng Qiong Ma Chun-Xia Huang Jun-Jie Xiao Xi Fu Yi-Feng Ren Yu-Li Qu Hong-Xia Xiang Mao Lei Ru-Yi Zheng Yang Zhong Ping Xiao Xiang Zhuang Feng-Ming You Jia-Wei He |
| author_facet | Xiao Zeng Qiong Ma Chun-Xia Huang Jun-Jie Xiao Xi Fu Yi-Feng Ren Yu-Li Qu Hong-Xia Xiang Mao Lei Ru-Yi Zheng Yang Zhong Ping Xiao Xiang Zhuang Feng-Ming You Jia-Wei He |
| author_sort | Xiao Zeng |
| collection | DOAJ |
| description | Abstract Background The aim of this study was to explore the microbial variations and biomarkers in the oral environment of patients with persistent pulmonary nodules (pPNs) and to reveal the potential biological functions of the salivary microbiota in pPNs. Materials and methods This study included a total of 483 participants (141 healthy controls and 342 patients with pPNs) from June 2022 and January 2024. Saliva samples were subjected to sequencing of the V3–V4 region of the 16S rRNA gene to assess microbial diversity and differential abundance. Seven advanced machine learning algorithms (logistic regression, support vector machine, multi-layer perceptron, naïve Bayes, random forest, gradient boosting decision tree, and LightGBM) were utilized to evaluate performance and identify key microorganisms, with fivefold cross-validation employed to ensure robustness. The Shapley Additive exPlanations (SHAP) algorithm was employed to explain the contribution of these core microbiotas to the predictive model. Additionally, the PICRUSt2 algorithm was used to predict the microbial functions. Results The salivary microbial composition in pPNs group showed significantly lower α- and β-diversity compared to healthy controls. A high-accuracy LightGBM model was developed, identifying six core genera—Fusobacterium, Solobacterium, Actinomyces, Porphyromonas, Atopobium, and Peptostreptococcus—as pPNs biomarkers. Additionally, a visualization pPNs risk prediction system was developed. The immune responses and metabolic activities differences in salivary microbiota between the patients with pPNs and healthy controls were revealed. Conclusions This study highlights the potential clinical applications of the salivary microbiota for enable earlier detection and targeted interventions, offering significant promise for advancing clinical management and improving patient outcomes in pPNs. Graphical abstract |
| format | Article |
| id | doaj-art-b8482b52c3d64f7ba24642754424a78d |
| institution | DOAJ |
| issn | 1479-5876 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Translational Medicine |
| spelling | doaj-art-b8482b52c3d64f7ba24642754424a78d2025-08-20T02:49:09ZengBMCJournal of Translational Medicine1479-58762024-11-0122111610.1186/s12967-024-05802-7Diagnostic potential of salivary microbiota in persistent pulmonary nodules: identifying biomarkers and functional pathways using 16S rRNA sequencing and machine learningXiao Zeng0Qiong Ma1Chun-Xia Huang2Jun-Jie Xiao3Xi Fu4Yi-Feng Ren5Yu-Li Qu6Hong-Xia Xiang7Mao Lei8Ru-Yi Zheng9Yang Zhong10Ping Xiao11Xiang Zhuang12Feng-Ming You13Jia-Wei He14Hospital of Chengdu University of Traditional Chinese MedicineHospital of Chengdu University of Traditional Chinese MedicineHospital of Chengdu University of Traditional Chinese MedicineHospital of Chengdu University of Traditional Chinese MedicineHospital of Chengdu University of Traditional Chinese MedicineHospital of Chengdu University of Traditional Chinese MedicineCollege of Artificial Intelligence, Xi’an Jiaotong UniversityHospital of Chengdu University of Traditional Chinese MedicineHospital of Chengdu University of Traditional Chinese MedicineHospital of Chengdu University of Traditional Chinese MedicineHospital of Chengdu University of Traditional Chinese MedicineDepartment of Thoracic Surgery, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of ChinaDepartment of Thoracic Surgery, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, School of Medicine, University of Electronic Science and Technology of ChinaHospital of Chengdu University of Traditional Chinese MedicineHospital of Chengdu University of Traditional Chinese MedicineAbstract Background The aim of this study was to explore the microbial variations and biomarkers in the oral environment of patients with persistent pulmonary nodules (pPNs) and to reveal the potential biological functions of the salivary microbiota in pPNs. Materials and methods This study included a total of 483 participants (141 healthy controls and 342 patients with pPNs) from June 2022 and January 2024. Saliva samples were subjected to sequencing of the V3–V4 region of the 16S rRNA gene to assess microbial diversity and differential abundance. Seven advanced machine learning algorithms (logistic regression, support vector machine, multi-layer perceptron, naïve Bayes, random forest, gradient boosting decision tree, and LightGBM) were utilized to evaluate performance and identify key microorganisms, with fivefold cross-validation employed to ensure robustness. The Shapley Additive exPlanations (SHAP) algorithm was employed to explain the contribution of these core microbiotas to the predictive model. Additionally, the PICRUSt2 algorithm was used to predict the microbial functions. Results The salivary microbial composition in pPNs group showed significantly lower α- and β-diversity compared to healthy controls. A high-accuracy LightGBM model was developed, identifying six core genera—Fusobacterium, Solobacterium, Actinomyces, Porphyromonas, Atopobium, and Peptostreptococcus—as pPNs biomarkers. Additionally, a visualization pPNs risk prediction system was developed. The immune responses and metabolic activities differences in salivary microbiota between the patients with pPNs and healthy controls were revealed. Conclusions This study highlights the potential clinical applications of the salivary microbiota for enable earlier detection and targeted interventions, offering significant promise for advancing clinical management and improving patient outcomes in pPNs. Graphical abstracthttps://doi.org/10.1186/s12967-024-05802-7BiomakersPersistent pulmonary nodulesMachine learningLung cancerMicrobiota16S rRNA sequencing |
| spellingShingle | Xiao Zeng Qiong Ma Chun-Xia Huang Jun-Jie Xiao Xi Fu Yi-Feng Ren Yu-Li Qu Hong-Xia Xiang Mao Lei Ru-Yi Zheng Yang Zhong Ping Xiao Xiang Zhuang Feng-Ming You Jia-Wei He Diagnostic potential of salivary microbiota in persistent pulmonary nodules: identifying biomarkers and functional pathways using 16S rRNA sequencing and machine learning Journal of Translational Medicine Biomakers Persistent pulmonary nodules Machine learning Lung cancer Microbiota 16S rRNA sequencing |
| title | Diagnostic potential of salivary microbiota in persistent pulmonary nodules: identifying biomarkers and functional pathways using 16S rRNA sequencing and machine learning |
| title_full | Diagnostic potential of salivary microbiota in persistent pulmonary nodules: identifying biomarkers and functional pathways using 16S rRNA sequencing and machine learning |
| title_fullStr | Diagnostic potential of salivary microbiota in persistent pulmonary nodules: identifying biomarkers and functional pathways using 16S rRNA sequencing and machine learning |
| title_full_unstemmed | Diagnostic potential of salivary microbiota in persistent pulmonary nodules: identifying biomarkers and functional pathways using 16S rRNA sequencing and machine learning |
| title_short | Diagnostic potential of salivary microbiota in persistent pulmonary nodules: identifying biomarkers and functional pathways using 16S rRNA sequencing and machine learning |
| title_sort | diagnostic potential of salivary microbiota in persistent pulmonary nodules identifying biomarkers and functional pathways using 16s rrna sequencing and machine learning |
| topic | Biomakers Persistent pulmonary nodules Machine learning Lung cancer Microbiota 16S rRNA sequencing |
| url | https://doi.org/10.1186/s12967-024-05802-7 |
| work_keys_str_mv | AT xiaozeng diagnosticpotentialofsalivarymicrobiotainpersistentpulmonarynodulesidentifyingbiomarkersandfunctionalpathwaysusing16srrnasequencingandmachinelearning AT qiongma diagnosticpotentialofsalivarymicrobiotainpersistentpulmonarynodulesidentifyingbiomarkersandfunctionalpathwaysusing16srrnasequencingandmachinelearning AT chunxiahuang diagnosticpotentialofsalivarymicrobiotainpersistentpulmonarynodulesidentifyingbiomarkersandfunctionalpathwaysusing16srrnasequencingandmachinelearning AT junjiexiao diagnosticpotentialofsalivarymicrobiotainpersistentpulmonarynodulesidentifyingbiomarkersandfunctionalpathwaysusing16srrnasequencingandmachinelearning AT xifu diagnosticpotentialofsalivarymicrobiotainpersistentpulmonarynodulesidentifyingbiomarkersandfunctionalpathwaysusing16srrnasequencingandmachinelearning AT yifengren diagnosticpotentialofsalivarymicrobiotainpersistentpulmonarynodulesidentifyingbiomarkersandfunctionalpathwaysusing16srrnasequencingandmachinelearning AT yuliqu diagnosticpotentialofsalivarymicrobiotainpersistentpulmonarynodulesidentifyingbiomarkersandfunctionalpathwaysusing16srrnasequencingandmachinelearning AT hongxiaxiang diagnosticpotentialofsalivarymicrobiotainpersistentpulmonarynodulesidentifyingbiomarkersandfunctionalpathwaysusing16srrnasequencingandmachinelearning AT maolei diagnosticpotentialofsalivarymicrobiotainpersistentpulmonarynodulesidentifyingbiomarkersandfunctionalpathwaysusing16srrnasequencingandmachinelearning AT ruyizheng diagnosticpotentialofsalivarymicrobiotainpersistentpulmonarynodulesidentifyingbiomarkersandfunctionalpathwaysusing16srrnasequencingandmachinelearning AT yangzhong diagnosticpotentialofsalivarymicrobiotainpersistentpulmonarynodulesidentifyingbiomarkersandfunctionalpathwaysusing16srrnasequencingandmachinelearning AT pingxiao diagnosticpotentialofsalivarymicrobiotainpersistentpulmonarynodulesidentifyingbiomarkersandfunctionalpathwaysusing16srrnasequencingandmachinelearning AT xiangzhuang diagnosticpotentialofsalivarymicrobiotainpersistentpulmonarynodulesidentifyingbiomarkersandfunctionalpathwaysusing16srrnasequencingandmachinelearning AT fengmingyou diagnosticpotentialofsalivarymicrobiotainpersistentpulmonarynodulesidentifyingbiomarkersandfunctionalpathwaysusing16srrnasequencingandmachinelearning AT jiaweihe diagnosticpotentialofsalivarymicrobiotainpersistentpulmonarynodulesidentifyingbiomarkersandfunctionalpathwaysusing16srrnasequencingandmachinelearning |