Identification the Low Oxidative Stress Subtype of Periodontitis.

Objective: The aim of this research was to identify the low oxidative stress-related genes expression (L-OS) subtype in patients with periodontitis.

Methods: Microarray data (MA) were retrieved from the Gene Expression Omnibus database. The different oxidative stress (OS) subtypes of periodontitis were identified by K-means clustering analysis and gene set variation analysis (GSVA). Differentially expressed genes (DEGs) (|Log fold change (FC)| >1, q < 0.05) amongst the OS subtypes and healthy controls (HCs) were identified by Limma R package. The genomic feature of L-OS subtype and corresponding medicines were evaluated and visualised with Drug-Gene Interaction Database and cytoscape-v3.7.2 software (Pearson correlation coefficient > 0.4). Finally, the LASSO-Logistic regression model was adopted to evaluate and predict patients' OS phenotype in routine clinical practice.

Results: The 241 periodontitis samples and 69 HCs were included. Thirty-three DEGs between the L-OS and high oxidative stress-related genes expression (H-OS) subtypes and 96 DEGs, including 8 transcription factors, between L-OS subtype and HCs were identified, respectively. Then, the network of TFs-Genes-Drugs was constructed to determine genomic feature of L-OS subtype. Finally, a 4-gene signature formula and the cutoff value were identified by ML with LASSO model to predict patients' classification.

Conclusions: For the first time, we identified L-OS subtype of periodontitis and evaluated its genomic feature with MA.