Shared pathogenic genes and therapeutic targets in periodontitis and systemic juvenile idiopathic arthritis: An integrative bioinformatics and machine learning study
- 1The First Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang Uygur Autonomous Region, China
- 2The Second Affiliated Hospital of Xinjiang Medical University, Urumqi, Xinjiang Uygur Autonomous Region, China
Abstract
Periodontitis (PD) and systemic juvenile idiopathic arthritis (sJIA) are chronic inflammatory diseases with potential clinical links, yet their shared molecular mechanisms remain unclear—patients with JIA face elevated periodontal risk, the temporomandibular joint affected in JIA shares inflammatory pathways with PD, Th17 cells drive both conditions, and glucocorticoid therapy may exacerbate periodontal vulnerability. This study aimed to explore the shared mechanisms and potential therapeutic targets of PD and sJIA using genetic expression data from the GEO database and performed comprehensive bioinformatics analyses, including differential expression gene analysis, weighted gene co‑expression network analysis (WGCNA), functional enrichment analysis, protein‑protein interaction network construction, and machine learning across 175 predictive models with the Ridge + AdaBoost ensemble identified as the best‑performing combination, followed by SHapley Additive exPlanations (SHAP) for model interpretability, CIBERSORT for immune infiltration assessment, and molecular docking with 100‑ns molecular dynamics simulations for therapeutic target validation. We identified 37 shared candidate genes between PD and sJIA, which were significantly enriched in IL‑17, NF‑κB, rheumatoid arthritis, and lipid atherosclerosis pathways, and machine learning screening further selected six core diagnostic genes (FAM46C, CXCL1, SELP, LGALSL, ELOVL4, VCAN), with SELP demonstrating the most robust cross‑model, cross‑dataset diagnostic value (AUC > 0.8); SHAP analysis confirmed SELP as a stable risk‑driving predictor across all models, while CXCL1 consistently showed protective effects. Immune infiltration revealed shared neutrophil elevation and CD8 ⁺ T‑cell reduction, with conserved CXCL1 and SELP correlations with neutrophils and resting mast cells, and FAM46C with plasma cells.Drug‑target network analysis identified IL1B, MMP1, and ITGAM as core targets, and molecular docking yielded strong binding affinities for deoxycholic acid–MMP1 (−7.6 kcal/mol), kaempferol–IL1B (−7.2 kcal/mol), kaempferol–ITGAM (−6.9 kcal/mol), with MD simulations confirming stable and specific binding. Collectively, this study explores shared genetic and immunological characteristics between PD and sJIA, suggests that SELP may serve as a critical cross‑disease diagnostic biomarker, and offers novel insights into their pathogenesis and potential therapeutic targets that warrant further experimental validation. Author summary: Periodontitis, a common oral disease, and systemic juvenile idiopathic arthritis, a rare autoimmune condition in children, have been clinically linked, yet their shared molecular basis remains unclear. By analyzing gene expression data from patients with each disease using network-based and machine learning approaches, we identified 37 genes commonly altered in both conditions. Among these, we pinpointed SELP as the strongest candidate for a shared diagnostic marker. We also found that both diseases involve elevated levels of neutrophils and reduction in CD8 ⁺ T cell, and identified several natural compounds that may bind to key proteins driving these diseases. Our findings provide a foundation for understanding how these two seemingly distinct conditions may be connected at the molecular level and highlight SELP and other genes as potential targets for future diagnostics or therapies. However, these are computational predictions that require experimental and clinical validation before they can inform patient care.
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BibTeX
@article{deng2026shared,
title = {{Shared pathogenic genes and therapeutic targets in periodontitis and systemic juvenile idiopathic arthritis: An integrative bioinformatics and machine learning study}},
author = {Qingao Deng and Junjie Wang and Xing Wang and Lu Qi},
journal = {PLOS Computational Biology},
year = {2026},
month = sep,
doi = {10.1371/journal.pcbi.1014669},
url = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014669},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Shared pathogenic genes and therapeutic targets in periodontitis and systemic juvenile idiopathic arthritis: An integrative bioinformatics and machine learning study
AU - Qingao Deng
AU - Junjie Wang
AU - Xing Wang
AU - Lu Qi
PY - 2026
DA - 2026/09/23
JO - PLOS Computational Biology
DO - 10.1371/journal.pcbi.1014669
UR - https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1014669
AB - Periodontitis (PD) and systemic juvenile idiopathic arthritis (sJIA) are chronic inflammatory diseases with potential clinical links, yet their shared molecular mechanisms remain unclear—patients with JIA face elevated periodontal risk, the temporomandibular joint affected in JIA shares inflammatory pathways with PD, Th17 cells drive both conditions, and glucocorticoid therapy may exacerbate periodontal vulnerability. This study aimed to explore the shared mechanisms and potential therapeutic targets of PD and sJIA using genetic expression data from the GEO database and performed comprehensive bioinformatics analyses, including differential expression gene analysis, weighted gene co‑expression network analysis (WGCNA), functional enrichment analysis, protein‑protein interaction network construction, and machine learning across 175 predictive models with the Ridge + AdaBoost ensemble identified as the best‑performing combination, followed by SHapley Additive exPlanations (SHAP) for model interpretability, CIBERSORT for immune infiltration assessment, and molecular docking with 100‑ns molecular dynamics simulations for therapeutic target validation. We identified 37 shared candidate genes between PD and sJIA, which were significantly enriched in IL‑17, NF‑κB, rheumatoid arthritis, and lipid atherosclerosis pathways, and machine learning screening further selected six core diagnostic genes (FAM46C, CXCL1, SELP, LGALSL, ELOVL4, VCAN), with SELP demonstrating the most robust cross‑model, cross‑dataset diagnostic value (AUC > 0.8); SHAP analysis confirmed SELP as a stable risk‑driving predictor across all models, while CXCL1 consistently showed protective effects. Immune infiltration revealed shared neutrophil elevation and CD8 ⁺ T‑cell reduction, with conserved CXCL1 and SELP correlations with neutrophils and resting mast cells, and FAM46C with plasma cells.Drug‑target network analysis identified IL1B, MMP1, and ITGAM as core targets, and molecular docking yielded strong binding affinities for deoxycholic acid–MMP1 (−7.6 kcal/mol), kaempferol–IL1B (−7.2 kcal/mol), kaempferol–ITGAM (−6.9 kcal/mol), with MD simulations confirming stable and specific binding. Collectively, this study explores shared genetic and immunological characteristics between PD and sJIA, suggests that SELP may serve as a critical cross‑disease diagnostic biomarker, and offers novel insights into their pathogenesis and potential therapeutic targets that warrant further experimental validation. Author summary: Periodontitis, a common oral disease, and systemic juvenile idiopathic arthritis, a rare autoimmune condition in children, have been clinically linked, yet their shared molecular basis remains unclear. By analyzing gene expression data from patients with each disease using network-based and machine learning approaches, we identified 37 genes commonly altered in both conditions. Among these, we pinpointed SELP as the strongest candidate for a shared diagnostic marker. We also found that both diseases involve elevated levels of neutrophils and reduction in CD8 ⁺ T cell, and identified several natural compounds that may bind to key proteins driving these diseases. Our findings provide a foundation for understanding how these two seemingly distinct conditions may be connected at the molecular level and highlight SELP and other genes as potential targets for future diagnostics or therapies. However, these are computational predictions that require experimental and clinical validation before they can inform patient care.
SN - 1553-7358
ER -