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Updated: Apr 14, 2026
Robust Ligature-Induced Model of Murine Periodontitis for the Evaluation of Oral Neutrophils
Published on: January 21, 2020
A machine learning model for periodontitis based on integrative gene expression analysis: validation in an
Shin-Kyu Lee1,2, Jung-Min Oh3,4,5, Sae-A Lee3
1Department of Periodontics and Dental Research Institute, Pusan National University Dental Hospital, Yangsan, Korea.
Purpose:
To develop a gene expression-based prediction model for periodontitis by identifying a compact set of predictive genes and to validate the model using an independent cohort of patient samples analyzed by reverse transcription quantitative polymerase chain reaction (RT-qPCR).
Methods:
Using a total of 9 Gene Expression Omnibus (GEO) series, we first performed feature selection through differential expression analysis and SHapley Additive exPlanations (SHAP) values in 2 GEO series (GSE10334 and GSE16134). The remaining datasets were then integrated to construct an extended multi-cohort dataset (680 samples: 193 healthy and 487 with periodontitis) for model development using the XGBoost classifier. An exhaustive search with nested cross-validation (CV) was conducted to identify the optimal gene subset. Model performance was estimated using repeated 10-fold CV and summarized by the area under the receiver operating characteristic curve (AUC) with corresponding standard deviations. Gene-level interpretation was performed using SHAP rankings and univariate analyses. Validation was conducted in a newly collected RT-qPCR patient cohort (n=20; 10 healthy individuals and 10 patients with periodontitis) derived from gingival tissue samples, using z-score-transformed ΔCt values without model retraining.
Results:
The optimal 4-gene model (tissue inhibitor of metalloproteinases-4 [TIMP4], RNA binding motif protein 25 [RBM25], TLC domain containing 3A [TLCD3A], and TSR1 ribosome maturation factor [TSR1]) achieved an AUC of 0.936±0.031 in repeated 10-fold CV. Among individual genes, TIMP4 demonstrated the strongest discriminatory performance (AUC=0.867), followed by TLCD3A (AUC=0.849), TSR1 (AUC=0.782), and RBM25 (AUC=0.728). In the independent RT-qPCR cohort, the 4-gene model yielded an AUC of 0.790 (95% confidence interval, 0.548-0.979).
Conclusions:
The compact 4-gene XGBoost model provides reproducible and interpretable prediction of periodontitis across GEO datasets and demonstrates moderate performance in a newly collected RT-qPCR patient cohort, suggesting preliminary cross-platform feasibility. However, the wide confidence intervals underscore the need for larger prospective cohorts to confirm its clinical utility.
