Decoding chromosomal instability in neutrophil signature in the pathogenesis of periodontitis: evidence from artificial intelligence-driven multi-omics and in vitro validation.

Periodontitis (PDT) is a chronic inflammatory disease driven by a dysregulated host immune response, with neutrophils playing a pivotal role. Chromosomal instability (CI) has emerged as a potent trigger of inflammation, yet its involvement in non-malignant diseases like PDT remains unexplored. Bulk RNA-seq data from gingival tissues between PDT patients and healthy control(HC) were analyzed via Limma framework to identify differentially expressed genes (DEGs) and CI-associated DEGs. WGCNA pinpointed a module correlated with neutrophil infiltration. Next, CI and neutrophil(CIN)-associated signature were acquired by integrating Limma and WGCNA results. NMF was used for molecular subtyping based on CIN-associated signature. A machine learning framework constructed a diagnostic model, with SHAP analysis identifying the hub gene based on PDT training and validation bulk profiles. Single-cell RNA-seq data validated cellular-level findings, and cutting-edge analytical framework inferred TFF3 functional impact at neutrophil of PDT patients in a temporal and spatial manners. The AI platform DrugReflector screened for therapeutic compounds for PDT patients, and in vitro assays examined the expression of hub gene. We identified 5 CI and neutrophil(CIN)-associated signature for PDT patients, which can guide the forecasting of PDT pathogenesis and PDT patient stratification. Besides, TFF3 can be considered as CIN-associated up-regulated pathogenic factor involved in PDT pathogenesis, which was mainly expressed in neutrophil. BRD-K92455082 as a candidate therapeutic agent targeting TFF3 for the treatment of PDT. This study first identifies a novel CIN-associated co-expression patterns in PDT. TFF3 can be considered as a candidate hub gene associated with a pro-inflammatory neutrophil state, providing new insights into the molecular taxonomy of PDT.