Exploration and validation of potential diagnostic biomarkers of PANoptosis in periodontitis using bioinformatics approaches.

BACKGROUND

Periodontitis represents a chronic inflammatory disease resulting from the interaction between plaque-associated microorganisms and the host immune response. PANoptosis, a newly recognized programmed cell death, is linked to multiple inflammatory disorders. However, its role in periodontitis pathogenesis and diagnostic potential remains unclear. This study aimed to identify key PANoptosis-related biomarkers in periodontitis using bioinformatics analysis followed by experimental verification.

METHODS

Three machine-learning algorithms were applied to identify key differentially expressed PANoptosis-related genes (DE-PRGs), followed by ROC analyses in public datasets and sequencing data to assess their diagnostic performance for periodontitis. Functional enrichment, immune infiltration analysis, and drug prediction analyses were conducted on these DE-PRGs. The mRNA expression of these genes in human gingival tissue was confirmed by qPCR, and protein expression was validated in a rat periodontitis model through immunohistochemistry.

RESULTS

ALOX5, CDK5R1, MMP14, and JAK3 were identified as key candidate genes, primarily enriched in inflammation, oxidative stress and immune pathways. These genes showed good diagnostic value in both public periodontitis databases and sequencing data. qPCR and IHC findings showed higher expression levels of ALOX5, MMP14, and JAK3 in human and rat periodontitis tissues compared to healthy controls, while CDK5R1 expression was reduced (P < 0.05). Drug prediction identified tofacitinib, zileuton, and melatonin as potential periodontitis therapeutics.

CONCLUSION

This study provides new evidence linking PANoptosis to periodontitis. ALOX5, CDK5R1, MMP14 and JAK3 were identified as PANoptosis-related biomarkers and potential therapeutic targets. These findings support the precision diagnosis and treatment of periodontitis.