ArticleOral diseases2025
Oral Leukoplakia Microbiome Predicts the Degree of Dysplasia and is Shaped by Smoking and Tooth Loss.
Article in Oral diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The trial behind it
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Who cites it
6 citing papers in PubMed.
- Bacterial Involvement in Oral Squamous Cell Carcinoma and Potentially Malignant Oral Disorders.Oral diseases · 2026Observational
- A spatial-microbial-metabolic-immune framework for high-risk oral potentially malignant disorders: an evidence-ranked review for malignant transformation risk stratification.Frontiers in cellular and infection microbiology · 2026Review
- Metabolic crosstalk between oral microbiota and the host in OSCC: emerging roles of microbial metabolites in tumor initiation and progression.Frontiers in cellular and infection microbiology · 2026Review
- Bacteriome-based oral dysbiosis index in patients with oral squamous cell carcinoma.Journal of oral microbiology · 2026Article
- Article
- The rise and evolving role ofCurrent research in microbial sciences · 2025Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
objectiveThis study aimed to determine if the oral potentially malignant disorder, oral leukoplakia (OLK), exhibited microbiome changes that predict the degree of dysplasia and the risk of malignant progression.
resultsWe examined the microbiome in 216 swabs of OLK from 177 patients. Compared to healthy controls (n = 120 swabs from 61 patients), who were less likely to smoke and had better oral health, OLK patients exhibited an increased abundance of Rothia mucilaginosa, Streptococcus parasanguinis and S. salivarius, resembling acetaldehyde generating communities described previously. Compared to the patients' healthy contralateral normal (CLN) mucosa (n = 202), which acts as a matched control for oral health parameters, OLK exhibited increased S. infantis, Leptotrichia spp., Bergeyella spp., Porphyromonas spp. and F. nucleatum. Machine learning with clinical and microbiome data could discriminate high-risk dysplasia (moderate to severe) from low-risk dysplasia (none or mild) (sensitivity 87.4%; specificity 76.5%). Follow-up swabs were recovered from 58 patients, eight of whom progressed to a higher grade of dysplasia or OSCC and these eight patients exhibited a higher abundance of Fusobacterium species at their initial presentation.
conclusionsOur study suggests that the OLK microbiome has potential to be an aid to the prediction of dysplasia grade and the risk of malignant transformation.
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