Current research status of risk assessment models for oral squamous cell carcinoma based on microbiome databases
6 affiliations
- 1Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology and National Center for Stomatology and National Clinical Research Center for Oral Diseases and National Engineering Research Center of Oral Biomaterials and Digital Medical Devices
- 2Department of Oral and Maxillofacial Surgery, Guiyang Stomatological Hospital
- 3Fifth Clinical Division, Peking University School and Hospital of Stomatology and National Center for Stomatology and National Clinical Research Center for Oral Diseases and National Engineering Research Center of Oral Biomaterials and Digital Medical Devices
- 4Lingchuan Stomatology Inc.
- 5Department of Respiratory and Critical Care Medicine, The Second Medical Center and National Clinical Research Center for Geriatric Disease, Chinese PLA General Hospital
- 6Central Laboratory, Peking University School and Hospital of Stomatology and National Center for Stomatology and National Clinical Research Center for Oral Diseases and National Engineering Research Center of Oral Biomaterials and Digital Medical Devices
Abstract
Oral squamous cell carcinoma (OSCC) is one of the most common oral malignancies. Due to insidious early symptoms, high invasiveness, and frequent cervical lymph node metastasis, patient survival remains poor. Recent studies demonstrate significant associations between oral dysbiosis and OSCC. To address limited early screening and suboptimal survival rates, this review systematically summarizes OSCC microbiome research and disease risk assessment applications. Studies reveal marked oral dysbiosis in patients with OSCC, characterized by enrichment of Fusobacterium and Porphyromonas , reduced Neisseria abundance, and altered community stability. These promote tumorigenesis through chronic pro-inflammatory signaling, the production of carcinogenic metabolites, and direct oncogenic pathway interference. Consequently, the oral microbiome offers non-invasive, sensitive biomarkers for early detection. We identify and distinguish four categories of predictive models in the current literature: (1) detection/discrimination of prevalent OSCC, (2) prediction of malignant transformation from oral potentially malignant disorders, (3) prediction of recurrence or metastasis after treatment, and (4) prognostic models for survival prediction. Existing databases include comprehensive and OSCC-specific collections with multi-omics integration. Risk assessment models encompass traditional statistics, machine learning, deep learning, and multimodal fusion, and have shown promising performance in retrospective cohorts. However, standardized databases are lacking, and existing models face confounding interference, limited generalizability, and insufficient interpretability. Furthermore, current predictive models are predominantly based on 16S rRNA sequencing (bacteriome-centric), whereas comprehensive risk assessment requires integrating interkingdom entities, including fungi, viruses, and archaea, through metagenomic and virome profiling. Critically, the distinction between promising retrospective discrimination and validated prospective clinical utility remains a major translational barrier. Future directions include developing standardized longitudinal databases, integrating multi-omics, using explainable artificial intelligence, and conducting prospective validation studies to achieve personalized precision treatment.
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BibTeX
@article{li2026current,
title = {{Current research status of risk assessment models for oral squamous cell carcinoma based on microbiome databases}},
author = {Guan-Lin Li and Zhu-Chao Zhang and Xiao-Ming Lyu and Mo-Xi Li and Pei-Yi Lin and Hui-Shan Yu and Yi-Yang Zhu and Guo-Hao Zhang and Cheng-Gang Duan and Yoo Cheung and Wei Li and Zhi-Jian Zhang and Tian-Song Xu and Feng Chen and Ming-Wei Huang},
journal = {Frontiers in Microbiology},
year = {2026},
month = oct,
doi = {10.3389/fmicb.2026.1894946},
url = {https://www.frontiersin.org/articles/10.3389/fmicb.2026.1894946/full},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Current research status of risk assessment models for oral squamous cell carcinoma based on microbiome databases
AU - Guan-Lin Li
AU - Zhu-Chao Zhang
AU - Xiao-Ming Lyu
AU - Mo-Xi Li
AU - Pei-Yi Lin
AU - Hui-Shan Yu
AU - Yi-Yang Zhu
AU - Guo-Hao Zhang
AU - Cheng-Gang Duan
AU - Yoo Cheung
AU - Wei Li
AU - Zhi-Jian Zhang
AU - Tian-Song Xu
AU - Feng Chen
AU - Ming-Wei Huang
PY - 2026
DA - 2026/10/26
JO - Frontiers in Microbiology
DO - 10.3389/fmicb.2026.1894946
UR - https://www.frontiersin.org/articles/10.3389/fmicb.2026.1894946/full
AB - Oral squamous cell carcinoma (OSCC) is one of the most common oral malignancies. Due to insidious early symptoms, high invasiveness, and frequent cervical lymph node metastasis, patient survival remains poor. Recent studies demonstrate significant associations between oral dysbiosis and OSCC. To address limited early screening and suboptimal survival rates, this review systematically summarizes OSCC microbiome research and disease risk assessment applications. Studies reveal marked oral dysbiosis in patients with OSCC, characterized by enrichment of Fusobacterium and Porphyromonas , reduced Neisseria abundance, and altered community stability. These promote tumorigenesis through chronic pro-inflammatory signaling, the production of carcinogenic metabolites, and direct oncogenic pathway interference. Consequently, the oral microbiome offers non-invasive, sensitive biomarkers for early detection. We identify and distinguish four categories of predictive models in the current literature: (1) detection/discrimination of prevalent OSCC, (2) prediction of malignant transformation from oral potentially malignant disorders, (3) prediction of recurrence or metastasis after treatment, and (4) prognostic models for survival prediction. Existing databases include comprehensive and OSCC-specific collections with multi-omics integration. Risk assessment models encompass traditional statistics, machine learning, deep learning, and multimodal fusion, and have shown promising performance in retrospective cohorts. However, standardized databases are lacking, and existing models face confounding interference, limited generalizability, and insufficient interpretability. Furthermore, current predictive models are predominantly based on 16S rRNA sequencing (bacteriome-centric), whereas comprehensive risk assessment requires integrating interkingdom entities, including fungi, viruses, and archaea, through metagenomic and virome profiling. Critically, the distinction between promising retrospective discrimination and validated prospective clinical utility remains a major translational barrier. Future directions include developing standardized longitudinal databases, integrating multi-omics, using explainable artificial intelligence, and conducting prospective validation studies to achieve personalized precision treatment.
SN - 1664-302X
ER -